behavioral psychology research paper

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• v.37(1); 2014 May

The Evidence-Based Practice of Applied Behavior Analysis

Timothy a. slocum.

Utah State University, Logan, UT USA

Ronnie Detrich

Wing Institute, Oakland, CA USA

Susan M. Wilczynski

Ball State University, Muncie, IN USA

Trina D. Spencer

Northern Arizona University, Flagstaff, AZ USA

Oregon State University, Corvallis, OR USA

Katie Wolfe

University of South Carolina, Columbia, SC USA

Evidence-based practice (EBP) is a model of professional decision-making in which practitioners integrate the best available evidence with client values/context and clinical expertise in order to provide services for their clients. This framework provides behavior analysts with a structure for pervasive use of the best available evidence in the complex settings in which they work. This structure recognizes the need for clear and explicit understanding of the strength of evidence supporting intervention options, the important contextual factors including client values that contribute to decision making, and the key role of clinical expertise in the conceptualization, intervention, and evaluation of cases. Opening the discussion of EBP in this journal, Smith ( The Behavior Analyst, 36 , 7–33, 2013 ) raised several key issues related to EBP and applied behavior analysis (ABA). The purpose of this paper is to respond to Smith’s arguments and extend the discussion of the relevant issues. Although we support many of Smith’s ( The Behavior Analyst, 36 , 7–33, 2013 ) points, we contend that Smith’s definition of EBP is significantly narrower than definitions that are used in professions with long histories of EBP and that this narrowness conflicts with the principles that drive applied behavior analytic practice. We offer a definition and framework for EBP that aligns with the foundations of ABA and is consistent with well-established definitions of EBP in medicine, psychology, and other professions. In addition to supporting the systematic use of research evidence in behavior analytic decision making, this definition can promote clear communication about treatment decisions across disciplines and with important outside institutions such as insurance companies and granting agencies.

Almost 45 years ago, Baer et al. ( 1968 ) described a new discipline—applied behavior analysis (ABA). This discipline was distinguished from the experimental analysis of behavior by its focus on social impact (i.e., solving socially important problems in socially important settings). ABA has produced remarkably powerful interventions in fields such as education, developmental disabilities and autism, clinical psychology, behavioral medicine, organizational behavior management, and a host of other fields and populations. Behavior analysts have long recognized that developing interventions capable of improving client behavior solves only one part of the problem. The problem of broad social impact must be solved by having interventions implemented effectively in socially important settings and at scales of social importance (Baer et al. 1987 ; Horner et al. 2005b ; McIntosh et al. 2010 ). This latter set of challenges has proved to be more difficult. In many cases, demonstrations of effectiveness are not sufficient to produce broad adoption and careful implementation of these procedures. Key decision makers may be more influenced by variables other than the increases and decreases in the behaviors of our clients. In addition, even when client behavior is a very powerful factor in decision making, it does not guarantee that empirical data will be the basis for treatment selection; anecdotes, appeals to philosophy, or marketing have been given priority over evidence of outcomes (Carnine 1992 ; Polsgrove 2003 ).

Across settings in which behavior analysts work, there has been a persistent gap between what is known from research and what is actually implemented in practice. Behavior analysts have been concerned with the failed adoption of research-based practices for years (Baer et al. 1987 ). Even in the fields in which behavior analysts have produced powerful interventions, the vast majority of current practice fails to take advantage of them.

Behavior analysts have not been alone in recognizing serious problems with the quality of interventions used employed in practice settings. In the 1960s, many within the medical field recognized a serious research-to-practice gap. Studies suggested that a relatively small percentage (estimates range from 10 to 25 %) of medical treatment decisions were based on high-quality evidence (Goodman 2003 ). This raised the troubling question of what basis was used for the remaining decisions if it was not high-quality evidence. These concerns led to the development of evidence-based practice (EBP) of medicine (Goodman 2003 ; Sackett et al. 1996 ).

The research-to-practice gap appears to be universal across professions. For example, Kazdin ( 2000 ) has reported that less than 10 % of the child and adolescent mental health treatments reported in the professional literature have been systematically evaluated and found to be effective and those that have not been evaluated are more likely to be adopted in practice settings. In recognition of their own research-to-practice gaps, numerous professions have adopted an EBP framework. Nursing and other areas of health care, social work, clinical and educational psychology, speech and language pathology, and many others have adopted this framework and adapted it to the specific needs of their discipline to help guide decision-making. Not only have EBP frameworks been helping to structure professional practice, but they have also been used to guide federal policy. With the passage of No Child Left Behind ( 2002 ) and the reauthorization of the Individuals with Disabilities Education Improvement Act ( 2005 ), the federal department of education has aligned itself with the EBP movement. A recent memorandum from the federal Office of Management and Budget instructed agencies to consider evidence of effectiveness when awarding funds, increase the use of evidence in competitions, and to encourage widespread program evaluation (Zients 2012 ). The memo, which used the term evidence-based practice extensively, stated: “Where evidence is strong, we should act on it. Where evidence is suggestive, we should consider it. Where evidence is weak, we should build the knowledge to support better decisions in the future” (Zients 2012 , p. 1).

EBP is more broadly an effort to improve decision-making in applied settings by explicitly articulating the central role of evidence in these decisions and thereby improving outcomes. It addresses one of the long-standing challenges for ABA; the need to effectively support and disseminate interventions in the larger social systems in which our work is embedded. In particular, EBP addresses the fact that many decision-makers are not sufficiently influenced by the best evidence that is relevant to important decisions. EBP is an explicit statement of one of ABA’s core tenets—a commitment to evidence-based decision-making. Given that the EBP framework is well established in many disciplines closely related to ABA and in the larger institutional contexts in which we operate (e.g., federal policy and funding agencies), aligning ABA with EBP offers an opportunity for behavior analysts to work more effectively within broader social systems.

Discussion of issues related to EBP in ABA has taken place across several years. Researchers have extensively discussed methods for identifying well-supported treatments (e.g., Horner et al. 2005a ; Kratochwill et al. 2010 ), and systematically reviewed the evidence to identify these treatments (e.g., Maggin et al. 2011 ; National Autism Center 2009 ). However, until recently, discussion of an explicit definition of EBP in ABA has been limited to conference papers (e.g., Detrich 2009 ). Smith ( 2013 ) opened a discussion of the definition and critical features of EBP of ABA in the pages of The Behavior Analyst . In his thought-provoking article, Smith raised many important points that deserve serious discussion as the field moves toward a clear vision of EBP of ABA. Most importantly, Smith ( 2013 ) argued that behavior analysts must carefully consider how EBP is to be defined and understood by researchers and practitioners of behavior analysis.

Definitions Matter

We find much to agree with in Smith’s paper, and we will describe these points of agreement below. However, we have a core disagreement with Smith concerning the vision of what EBP is and how it might enhance and expand the effective practice of ABA. As behavior analysts know, definitions matter. A well-conceived definition can promote conceptual understanding and set the context for effective action. Conversely, a poor definition or confusion about definitions hinders clear understanding, communication, and action.

In providing a basis for his definition of EBP, Smith refers to definitions in professions that have well-developed conceptions of EBP. He quotes the American Psychological Association (APA) ( 2005 ) definition (which we quote here more extensively than he did):

Evidence-based practice in psychology (EBPP) is the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. This definition of EBPP closely parallels the definition of evidence-based practice adopted by the Institute of Medicine ( 2001 , p. 147) as adapted from Sackett et al. ( 2000 ): “Evidence-based practice is the integration of best research evidence with clinical expertise and patient values.” The purpose of EBPP is to promote effective psychological practice and enhance public health by applying empirically supported principles of psychological assessment, case formulation, therapeutic relationship, and intervention.

The key to understanding this definition is to note how APA and the Institute of Medicine use the word practice . Clearly, practice does not refer to an intervention; instead, it references one’s professional behavior. This is the sense in which one might speak of the professional practice of behavior analysis. American Psychological Association Presidential Task Force of Evidence-Based Practice ( 2006 ) further elaborates this point:

It is important to clarify the relation between EBPP and empirically supported treatments (ESTs)…. ESTs are specific psychological treatments that have been shown to be efficacious in controlled clinical trials, whereas EBPP encompasses a broader range of clinical activities (e.g., psychological assessment, case formulation, therapy relationships). As such, EBPP articulates a decision-making process for integrating multiple streams of research evidence—including but not limited to RCTs—into the intervention process. (p. 273)

In contrast, Smith defined EBP not as a decision-making process but as a set of interventions that have been shown to be efficacious through rigorous research. He stated:

An evidence-based practice is a service that helps solve a consumer’s problem. Thus it is likely to be an integrated package of procedures, operationalized in a manual, and validated in studies of socially meaningful outcomes, usually with group designs. (p. 27).

Smith’s EBP is what APA has clearly labeled an empirically supported treatment . This is a common misconception found in conversation and in published articles (e.g., Cook and Cook 2013 ) but at odds with formal definitions provided by many professional organizations; definitions which result from extensive consideration and debate by representative leaders of each professional field (e.g., APA 2005 ; American Occupational Therapy Association 2008 ; American Speech-Language Hearing Association 2005 ; Institute of Medicine 2001 ).

Before entering into the discussion of a useful definition of EBP of ABA, we should clarify the functions that we believe a useful definition of EBP should perform. First, a useful definition should align with the philosophical tenets of ABA, support the most effective current practice of ABA, and contribute to further improvement of ABA practice. A definition that is in conflict with the foundations of ABA or detracts from effective practice clearly would be counterproductive. Second, a useful definition of EBP of ABA should enhance social support for ABA practice by describing its empirical basis and decision-making processes in a way that is understandable to professions that already have well-established definitions of EBP. A definition that corresponds with the fundamental components of EBP in other fields would promote ABA practice by improving communication with external audiences. This improved communication is critical in the interdisciplinary contexts in which behavior analysts often practice and for legitimacy among those familiar with EBP who often control local contingencies (e.g., policy makers and funding agencies).

Based on these functions, we propose the following definition: Evidence-based practice of applied behavior analysis is a decision-making process that integrates (a) the best available evidence with (b) clinical expertise and (c) client values and context. This definition positions EBP as a pervasive feature of all professional decision-making by a behavior analyst with respect to client services; it is not limited to a narrowly restricted set of situations or decisions. The definition asserts that the best available evidence should be a primary influence on all decision-making related to services for clients (e.g., intervention selection, progress monitoring, etc.). It also recognizes that evidence cannot be the sole basis for a decision; effective decision-making in a discipline as complex as ABA requires clinical expertise in identifying, defining, and analyzing problems, determining what evidence is relevant, and deciding how it should be applied. In the absence of this decision-making framework, practitioners of ABA would be conceptualized as behavioral technicians rather than analysts. Further, the definition of EBP of ABA includes client values and context. Decision-making is necessarily based on a set of values that determine the goals that are to be pursued and the means that are appropriate to achieve them. Context is included in recognition of the fact that the effectiveness of an intervention is highly dependent upon the context in which it is implemented. The definition asserts that effective decision-making must be informed by important contextual factors. We elaborate on each component of the definition below, but first we contrast our definition with that offered by Smith ( 2013 ).

Although Smith ( 2013 ) made brief reference to the other critical components of EBP, he framed EBP as a list of multicomponent interventions that can claim a sufficient level of research support. We agree with his argument that such lists are valuable resources for practitioners and therefore developing them should be a goal of researchers. However, such lists are not, by themselves , a powerful means of improving the effectiveness of behavior analytic practice. The vast majority of decisions faced in the practice of behavior analysis cannot be made by implementing the kind of manualized, multicomponent treatment packages described by Smith.

There are a number of reasons a list of interventions is not an adequate basis for EBP of ABA. First, there are few interventions that qualify as “practices” under Smith’s definition. For example, when discussing the importance of manuals for operationalizing treatments, Smith stated that the requirement that a “practice” be based on a manual, “sharply reduces the number of ABA approaches that can be regarded as evidence based. Of the 11 interventions for ASD identified in the NAC ( 2009 ) report, only the three that have been standardized in manuals might be considered to be practices, and even these may be incomplete” (p. 18). Thus, although the example referenced the autism treatment literature, it seems apparent that even a loose interpretation of this particular criterion would leave all practitioners with a highly restricted number of intervention options.

Second, even if more “practices” were developed and validated, many consumers cannot be well served with existing multicomponent packages. In order to meet their clients’ needs, behavior analysts must be able to selectively implement focused interventions alone or in combination. This flexibility is necessary to meet the diverse needs of their clients and to minimize the response demands on direct care providers or staff, who are less likely to implement a complicated intervention with fidelity (Riley-Tillman and Chafouleas 2003 ).

Third, the strategy of assembling a list of treatments and describing these as “practices” severely limits the ways in which research findings are used by practitioners. With the list approach to defining EBP, research only impacts practice by placing an intervention on a list when a specific criteria has been met. Thus, any research on an intervention that is not sufficiently broad or manualized to qualify as a “practice” has no influence on EBP. Similarly, a research study that shows clear results but is not part of a sufficient body of support for an intervention would also have no influence. A study that provides suggestive results but is not methodologically strong enough to be definitive would have no influence, even if it were the only study that is relevant to a given problem.

The primary problem with a list approach is that it does not provide a strong framework that directs practitioners to include the best available evidence in all of their professional decision-making. Too often, practitioners who consult such lists find that no interventions relevant to their specific case have been validated as “evidence-based” and therefore EBP is irrelevant. In contrast, definitions of EBP as a decision-making process can provide a robust framework for including research evidence along with clinical expertise and client values and context in the practice of behavior analysis. In the next sections, we explore the components of this definition in more detail.

Best Available Evidence

The term “best available evidence” occupies a critical and central place in the definition and concept of EBP; this aligns with the fundamental reliance on scientific research that is one of the core tenets of ABA. The Behavior Analyst Certification Board ( 2010 ) Guidelines for Responsible Conduct for Behavior Analysts repeatedly affirm ways in which behavior analysts should base their professional conduct on the best available evidence. For example:

Behavior analysts rely on scientifically and professionally derived knowledge when making scientific or professional judgments in human service provision, or when engaging in scholarly or professional endeavors.

• The behavior analyst always has the responsibility to recommend scientifically supported most effective treatment procedures. Effective treatment procedures have been validated as having both long-term and short-term benefits to clients and society.
• Clients have a right to effective treatment (i.e., based on the research literature and adapted to the individual client).

A Continuum of Evidence Quality

The term best implies that evidence can be of varying quality, and that better quality evidence is preferred over lower quality evidence. Quality of evidence for informing a specific practical question involves two dimensions: (a) relevance of the evidence and (b) certainty of the evidence.

The dimension of relevance recognizes that some evidence is more germane to a particular decision than is other evidence. This idea is similar to the concept of external validity. External validity refers to the degree to which research results apply to a range of applied situations whereas relevance refers to the degree to which research results apply to a specific applied situation. In general, evidence is more relevant when it matches the particular situation in terms of (a) important characteristics of the clients, (b) specific treatments or interventions under consideration, (c) outcomes or target behaviors including their functions, and (d) contextual variables such as the physical and social environment, staff skills, and the capacity of the organization. Unless all conditions match perfectly, behavior analysts are necessarily required to use their expertise to determine the applicability of the scientific evidence to each unique clinical situation. Evidence based on functionally similar situations is preferred over evidence based on situations that share fewer important characteristics with the specific practice situation. However, functional similarity between a study or set of studies and a particular applied problem is not always obvious.

The dimension of certainty of evidence recognizes that some evidence provides stronger support for claims that a particular intervention produced a specific result. Any instance of evidence can be evaluated for its methodological rigor or internal validity (i.e., the degree to which it provides strong support for the claim of effectiveness and rules out alternative explanations). Anecdotes are clearly weaker than more systematic observations, and well-controlled experiments provide the strongest evidence. Methodological rigor extends to the quality of the dependent measure, treatment fidelity, and other variables of interest (e.g., maintenance of skill acquisition), all of which influence the certainty of evidence. But the internal validity of any particular study is not the only variable influencing the certainty of evidence; the quantity of evidence supporting a claim is also critical to its certainty. Both systematic and direct replication are vital for strengthening claims of effectiveness (Johnston and Pennypacker 1993 ; Sidman 1960 ). Certainty of evidence is based on both the rigor of each bit of evidence and the degree to which the findings have been consistently replicated. Although these issues are simple in principle, operationalizing and measuring rigor of research is extremely complex. Numerous quality appraisal systems for both group and single-subject research have been proposed and used in systematic reviews (see below for more detail).

Under ideal circumstances, consistently high-quality evidence that closely matches the specifics of the practice situation is available; unfortunately, this is not always the case, and evidence-based practitioners of ABA must proceed despite an imperfect evidence base. The mandate to use the best available evidence specifies that the practitioner make decisions based on the best evidence that is available. Although this statement may seem rather obvious, the point is worth underscoring because the implications are highly relevant to behavior analysts. In an area with considerable high-quality relevant research, the standards for evidence should be quite high. But in an area with more limited research, the practitioner should take advantage of the best evidence that is available. This may require tentative reliance on research that is somewhat weaker or is only indirectly relevant to the specific situation at hand. For example, ideally, evidence-based practitioners of ABA would rely on well-controlled experimental results that have been replicated with the precise population with whom they are working. However, if this kind of evidence is not available, they might have to make decisions based on a single study that involves a similar but not identical population.

This idea of using the best of the available evidence is very different from one of using only extremely high-quality evidence (i.e., empirically supported treatments). If we limit EBP to considering only the highest quality evidence, we leave the practitioner with no guidance in the numerous situations in which high-quality and directly relevant evidence (i.e., precise matching of setting, function, behavior, motivating operations and precise procedures) simply does not exist. This approach would lead to a form of EBP that is irrelevant to the majority of decisions that a behavior analyst must make on a daily basis. Instead, our proposed definition of EBP asserts that the practitioner should be informed by the best evidence that is available.

Expanding Research on Utility of Treatments

Smith ( 2013 ) argued that the research methods used by behavior analysts to evaluate these treatments should be expanded to more comprehensively describe the utility of interventions. He suggested that too much ABA research is conducted in settings that do not approximate typical service settings, optimizing experimental control at the expense of external validity. Along this same line of reasoning, he noted that it is important to test the generality of effects across clients and identify variables that predict differential effectiveness. He suggested systematically reporting results from all research participants (e.g., the intent-to-treat model), and purposive selection of participants would provide a more complete account of the situations in which treatments are successful and those in which they are unsuccessful. Smith argued that researchers should include more distal and socially important outcomes because with a narrow target “behavior may change, but remain a problem for the individual or may be only a small component of a much larger cluster of problems such as addiction or delinquency.” He pointed out that in order to best support effective practice, research must demonstrate that an intervention produces or contributes to producing the socially important outcomes that would cause a consumer to say that the problem is solved.

Further, Smith argues that many of the questions most relevant to EBP—questions about the likely outcomes of a treatment when applied in a particular type of situation—are well suited to group research designs. He argued that RCTs are likely to be necessary within a program of research because:

most problems pose important actuarial questions (e.g., determining whether an intervention package is more effective than community treatment as usual; deciding whether to invest in one intervention package or another, both, or neither; and determining whether the long-term benefits justify the resources devoted to the intervention)…. A particularly important actuarial issue centers on the identification of the conditions under which the intervention is most likely to be effective. (p. 23)

We agree that selection of research methods should be driven by the kinds of questions being asked and that group research designs are the methods of choice for some types of questions that are central to EBP. Therefore, we support Smith’s call for increased use of group research designs within ABA. If practice decisions are to be informed by the best available evidence, we must take advantage of both group and single-subject designs. However, we disagree with Smith’s statement that EBP should be limited to treatments that are validated “usually with group designs” (Smith, p. 27). Practitioners should be supported by reviews of research that draw from all of the available evidence and provide the best recommendations possible given the state of knowledge on the particular question. In most areas of behavior analytic practice, single-subject research makes up a large portion of the best available evidence. The Institute for Education Science (IES) has recognized the contribution single case designs can make toward identifying effective practices and has recently established standards for evaluating the quality of single case design studies (Institute of Educational Sciences, n.d. ; Kratochwill et al. 2013 ).

Classes of Evidence

Identifying the best available evidence to inform specific practice decisions is extremely complex, and no single currently available source of evidence can adequately inform all aspects of practice. Therefore, we outline a number of strategies for identifying and summarizing evidence in ways that can support the EBP of ABA. We do not intend to cover all sources of evidence comprehensively, but merely outline some of the options available to behavior analysts.

Empirically Supported Treatment Reviews

Empirically supported treatments (EST) are identified through a particular form of systematic literature review. Systematic reviews bring a rigorous methodology to the process of reviewing research. The development and use of these methods are, in part, a response to the recognition that the process of reviewing the literature is subject to threats to validity. The systematic review process is characterized by explicitly stated and replicable methods for (a) searching for studies, (b) screening studies for relevance to the review question, (c) appraising the methodological quality of studies, (d) describing outcomes from each study, and (e) determining the degree to which the treatment (or treatments) is supported by the research. When the evidence in support of a treatment is plentiful and of high quality, the treatment generally earns the status of an EST. Many systematic reviews, however, find that no intervention for a particular problem has sufficient evidence to qualify as an EST.

Well-known organizations in medicine (e.g., Cochrane Collaboration), education (e.g., What Works Clearinghouse), and mental health (e.g., National Registry of Evidence-based Programs and Practices) conduct EST reviews. Until recently, systematic reviews have focused nearly exclusively on group research; however, systematic reviews of single-subject research are quickly becoming more common and more sophisticated (e.g., Carr 2009 ; NAC 2009 ; Maggin et al. 2012 ).

Systematic reviews for EST status is one important way to summarize the best available evidence because it can give a relatively objective evaluation of the strength of the research literature supporting a particular intervention. But systematic reviews are not infallible; as with all other research and evaluation methods, they require skillful application and are subject to threats to validity. The results of reviews can change dramatically based on seemingly minor changes in operational definitions and procedures for locating articles, screening for relevance, describing treatments, appraising methodological quality, describing outcomes, summarizing outcomes for the body of research as a whole, and rating the degree to which an intervention is sufficiently supported (Slocum et al. 2012a ; Wilczynski 2012 ). Systematic reviews and claims based upon them must be examined critically with full recognition of their limitations just as one examines primary research reports.

Behavior analysts encounter many situations in which no ESTs have been established for the particular combination of client characteristics, target behaviors, functions, contexts, and other parameters for decision-making. This dearth may exist because no systematic review has addressed the particular problem or because a systematic review has been conducted but failed to find any well-supported treatments for the particular problem. For example, in a recent review of all of the recommendations in the empirically supported practice guides published by the IES, 45 % of the recommendations had minimal support (Slocum et al. 2012b ). As Smith noted ( 2013 ), only 3 of the 11 interventions that the NAC identified as meeting quality standards might be considered practices in the sense that they are manualized. In these common situations, a behavior analyst cannot respond by simply selecting an intervention from a list of ESTs. A comprehensive EBP of ABA requires additional strategies for reviewing research evidence and drawing practice recommendations from existing evidence—strategies that can glean the best available evidence from an imperfect research base and formulate practice recommendations that are most likely to lead to favorable outcomes under conditions of uncertainty.

Other Methods for Reviewing Research Literature

The three strategies outlined below may complement systematic reviews in guiding behavior analysts toward effective decision-making.

Narrative Reviews of the Literature

There has been a long tradition across disciplines of relying on narrative reviews to summarize what is known with respect to treatments for a class of problems (e.g., aggression) or what is known about a particular treatment (e.g., token economy). The author of the review, presumably an expert, selects the theme and synthesizes the research literature that he or she considers most relevant. Narrative reviews allow the author to consider a wide range of research including studies that are indirectly relevant (e.g., those studying a given problem with a different population or demonstrating general principles) and studies that may not qualify for systematic reviews because of methodological limitations but which illustrate important points nonetheless. Narrative reviews can consider a broader array of evidence and have greater interpretive flexibility than most systematic reviews.

As with all sources of evidence, there are difficulties with narrative reviews. The selection of the literature is left up to the author’s discretion; there are no methodological guidelines and little transparency about how the author decided which literature to include and which to exclude. There is always the risk of confirmation bias that the author emphasized literature that is consistent with her preconceived opinions. Even with a peer-review process, it is always possible that the author neglected or misinterpreted research relevant to the discussion. These concerns not withstanding, narrative reviews may provide the best available evidence when no systematic reviews exist or when substantial generalizations from the systematic review to the practice context are needed. Many textbooks (e.g., Cooper et al. 2007 ) and handbooks (e.g., Fisher et al. 2011 ; Madden et al. 2013 ) provide excellent examples of narrative reviews that can provide important guidance for evidence-based practitioners of ABA.

Best Practice Guides

Best practice guides are another source of evidence that can inform decisions in the absence of available and relevant systematic reviews. Best practice guides provide recommendations that reflect the collective wisdom of an expert panel. It is presumed that the recommendations reflect what is known from the research literature, but the validity of recommendations is largely derived from the panel’s expertise rather than from the rigor of their methodology. Recommendations from best practice panels are usually much broader than the recommendations from systematic reviews. The recommendations from these guides can provide important information about how to implement a treatment, how to adapt the treatment for specific circumstances, and what is necessary for broad scale or system-wide implementation.

The limitations to best practice guides are similar to those for narrative reviews; specifically, potential bias and lack of transparency are significant concerns. Panel members are typically not selected using a specific set of operationalized criteria. Bias is possible if the panel is drawn too narrowly. If the panel is drawn too broadly; however, the panel may have difficulty reaching a consensus (Wilczynski 2012 ).

Empirically Supported Practice Guides

Empirically supported practice guides, a more recently developed strategy, integrate the strengths of systematic reviews and best practice panels. In this type of review, an expert panel is charged with developing recommendations on a topic. As part of the process, a systematic review of the literature is conducted. Following the systematic review, the panel generates a set of recommendations and objectively determines the strength of evidence for the recommendation and assigns an evidence rating. When there is little empirical evidence directly related to a specific issue, the panel’s recommendations may have weak research support but nonetheless may be based on the best evidence that is available. The obvious advantage of empirically supported practice guides is that there is greater transparency about the review process and certainty of recommendations. Practice recommendations are usually broader than those derived from systematic reviews and address issues related to implementation and acceptable variations to enhance the treatment’s contextual fit (Shanahan et al. 2010 ; Slocum et al. 2012b ). Although empirically supported practice guides offer the objectivity of a systematic review and the flexibility of best practice guidelines, they also face potential sources of error from both methods. Systematic and explicit criteria are used to review the research and rate the level of evidence for each recommendation; however, it is the panel that formulates recommendations. Thus, results of these reviews are influenced by the selection of panel members. When research evidence is incomplete or equivocal, panelists must exercise judgment in interpreting the evidence and drawing conclusions (Shanahan et al. 2010 ).

Other Units of Analysis

Smith ( 2013 ) weighed in on the critical issue of the unit of analysis when describing and evaluating treatments (Slocum and Wilczynski 2008 ). The unit of analysis refers to whether EBP should focus on (a) principles, such as reinforcement; (b) tactics, such as backward chaining; (c) multicomponent packages, such as Functional Communication Training; or (d) even more comprehensive systems, such as Early Intensive Behavioral Intervention. After reviewing the ongoing debate between those favoring a smaller unit of analysis that focuses on specific procedures and those favoring a larger unit of analysis that evaluates the effects of multicomponent packages, Smith made a case that the multicomponent treatment package is the key unit in EBP. Smith noted that practitioners rarely solve a client’s problem with a single procedure; instead, solutions typically involve combinations of procedures. He argued that the unit should be “a service aimed at solving people’s problems” and procedures that are merely components of such services are not sufficiently complete to be the proper unit of analysis for EBP. He further stated that these treatment packages should include strategies for implementation in typical service settings and an intervention manual.

We concur that the multicomponent treatment package is a particularly significant and strategic unit of treatment because it specifies a suite of procedures and exactly how they are to be used together to solve a problem. Validated treatment packages are far more than the sum of their parts. A well-developed treatment package can be revised and optimized over many iterations in a way that would be difficult or impossible for a practitioner to accomplish independently. In addition, research outcomes from implementation of treatment packages reflect the interaction of the components, and these interactions may not be evident in the research literature on the individual components. Further, research on the outcomes from multicomponent packages can evaluate broader and more socially important outcomes than is generally possible when evaluating more narrowly defined treatments. For example, in the case of teaching a child with autism to communicate, research on a focused procedure such as time delay may indicate that its use leads to more independent communicative responses; however, research on a comprehensive Early Intensive Behavioral Intervention can evaluate the impact of the program on children’s global development or intellectual functioning.

Having recognized our agreement with Smith ( 2013 ) on the special importance of multicomponent treatment packages for EBP, we hasten to add that this type of intervention is not enough to support a broad and robust EBP of ABA. EBP must also provide guidance to the practitioner in the frequently encountered situations in which well-established treatment packages are not available. In these situations, problems may be best addressed by building an intervention from a set of elemental components. These components, referred to as practice elements (Chorpita et al. 2005 , 2007 ) or kernels (Embry 2004 ; Embry and Biglan 2008 ), may be validated either directly or indirectly. The practitioner assembles a particular combination of components to solve a specific problem. Because this newly constructed package has not been evaluated as a whole, there is additional uncertainty about the effectiveness of the package, and the quality of evidence may be considered lower than a well-supported treatment package (Slocum et al. 2012b ; Smith 2013 ; however, see Chorpita ( 2003 ) for a differing view). Nonetheless, treatment components that are supported by strong evidence provide the practitioner with tools to solve practical problems when EST packages are not relevant.

In some cases, behavior analysts are presented with problems that cannot be addressed even by assembling established components. In these cases, the ABA practitioner must apply principles of behavior to construct an intervention and must depend on these principles to guide sensible modifications of interventions in response to client needs and to support sensible implementation of interventions. Principles of behavior are broadly generalized statements describing behavioral relations. Their empirical base is extremely large and diverse including both human and nonhuman participants across numerous contexts, behaviors, and consequences. Although principles of behavior are based on an extremely broad research literature, they are also stated at a broad level. As a result, the behavior analyst must use a great deal of judgment in applying principles to particular problems and a particular attempt to apply a principle to solve a problem may not be successful. Thus, although behavioral principles are supported by evidence, newly constructed interventions based on these principles have not yet been evaluated. These interventions must be considered less certain or validated than treatment packages or elements that have been demonstrated to be effective for specific problems, populations, and context (Slocum et al. 2012b ).

Evidence-based practitioners of ABA recognize that the process of selecting and implementing treatments always includes some level of uncertainty (Detrich et al. 2013 ). One of the fundamental tenets of ABA shared with many other professions is that the best evidence regarding the effectiveness of an intervention does not come from systematic literature reviews, best practice guides, or principles of behavior, but from close continual contact with the relevant outcomes (Bushell and Baer 1994 ). The BACB guidelines ( 2010 ) state that, “behavior analysts recognize limits to the certainty with which judgments or predictions can be made about individuals” (item 3.0 [c]). As a result, “the behavior analyst collects data…needed to assess progress within the program” (item 4.07) and “modifies the program on the basis of data” (item 4.08). Thus, an important feature of the EBP of ABA is that professional decision-making does not end with the selection of an initial intervention. The process continues with ongoing progress monitoring and adjustments to the treatment plan as needed to achieve the targeted outcomes. Progress monitoring and data-based decision-making are the ultimate hedge against the inherent uncertainties of imperfect knowledge derived from research. As the quality of the best available evidence decreases, the importance of frequent direct measurement of client progress increases.

Practice decisions are always accompanied by some degree of uncertainty; however, better decisions are likely when multiple of sources of evidence are integrated. For example, a multicomponent treatment package may be an EST for clients who differ slightly from those the practitioner currently serves. Confidence in the use of this treatment may be increased if there is evidence showing the central components are effective with clients belonging to the population of interest. The principles of behavior might further inform sensible variations appropriate for the specific context of practice. When considered together, numerous sources of evidence increase the confidence the behavior analyst can have in the intervention. And when the plan is implemented, progress monitoring may reveal the need for additional adjustments. Each of these different classes of evidence provides answers to different questions for the practitioner, resulting in a more fine-grained analysis of the clinical problem and solutions to it (Detrich et al. 2013 ).

Client Values and Context

In order to be compatible with the underlying tenets of ABA, parallel with other professions, and to promote effective practice, a definition of EBP of ABA must include client values and context among the primary contributors to professional decision-making. Baer et al. ( 1968 ) suggested that the word applied refers to an immediate and important change in behavior that has practical value and that this value is determined “by the interest which society shows in the problems” (p. 92)—that is, by social values. Wolf ( 1978 ) went on to specify that behavior analytic practice can only be termed successful if it addresses goals that are meaningful to our clients, uses procedures that are judged appropriate by our clients, and produces effects that are valued by our clients. These foundational tenets of ABA correspond with the centrality of client values in classic definitions of EBP (e.g., Institute of Medicine 2001 ). Like medical professionals and those in the many other fields that have adopted similar conceptualizations of EBP, behavior analysts have long recognized that client values are critical contributors to responsible decision-making.

Behavior analysts have defined the client as the individual who is the focus of the behavior change, other individuals who are critical to the behavior change process (Baer et al. 1968 ; Heward et al. 2005 ), as well as outside individuals or groups who may have a stake in the target behavior or improved outcomes (Baer et al. 1987 ; Wolf 1978 ). Wolf ( 1978 ) argued that only our clients can judge the social validity of our work and suggested that behavior analysts address three levels of social validity: (a) the social significance of the goals, (b) the social desirability of the procedures, and (c) the social importance of the outcomes. With respect to selection of interventions, Wolf noted, “not only is it important to determine the acceptability of treatment procedures to participants for ethical reasons, it may also be that the acceptability of the program is related to effectiveness, as well as to the likelihood that the program will be adopted and supported by others” (p. 210). He further maintained that clients are the ultimate arbiters of whether or not the effects of a program are sufficiently helpful to be termed successful.

The concept of social validity directs our attention to some of the important aspects of the context of intervention. Intervention always occurs in some context and features of that context can directly influence the fidelity with which the intervention is implemented and its effectiveness. Albin et al. ( 1996 ) expanded further on the contextual variables that might be critical for designing and implementing effective interventions. They described the concept of contextual fit or the congruence of a behavioral support plan and the context and indicate that this fit will determine its implementation, effectiveness, and maintenance.

Contextual fit includes the issues of social validity, but also explicitly encompasses issues associated with the individuals who implement treatments and manage other aspects of the environments within which treatments are implemented. Behavioral intervention plans prescribe the behavior of implementers. These implementers may include professionals, such as therapists and teachers, as well as nonprofessionals, such as family and community members. It is important to consider characteristics of these implementers when developing plans because the success of a plan may hinge on how it corresponds with the values, skills, goals, and stressors of the implementers. Effective plans must be within the skill repertoire of the implementers, or training to fidelity must occur to introduce the plan components into that repertoire. Values, goals, and stressors refer to motivating operations that determine the reinforcing or punishing value of implementing the plan. Plans that provide little reinforcement and substantial punishment in the process of implementation or outcomes are unlikely to be implemented with fidelity or maintained over time. The effectiveness of behavioral interventions is also influenced by their compatibility with other aspects of their context. Plans that are compatible with ongoing routines are more likely to be implemented than those that conflict (Riley-Tillman and Chafouleas 2003 ). Interventions require various kinds of resources to be implemented and sustained. For example, financial resources may be necessary to purchase curricula, equipment, or other goods. Interventions may require human resources such as direct service staff, training, supervision, administration, and consultation. Fixsen et al. ( 2005 ) have completed an extensive review of contextual variables that can potentially influence the quality of intervention implementation. Behavior analytic practice is unlikely to be effective if it does not consider the context in which interventions will be implemented.

Extensive behavior analytic research has documented the importance of social validity and other contextual factors in producing behavioral changes with practical value. This research tradition is as old as our field (e.g., Jones and Azrin 1969 ) and continues through the present day. For example, Strain et al. ( 2012 ) provided multiple examples of the impact of social validity considerations on relevant outcomes. They reported that integrating client values, preferences, and characteristics in the selection and implementation of an intervention can successfully inform decisions regarding (a) how to design service delivery systems, (b) how to support implementers with complex strategies, (c) when to fade support, (e) how to identify important and unanticipated effects, and (f) how to focus on future research efforts.

Benazzi et al. ( 2006 ) examined the effect of stakeholder participation in intervention planning on the acceptability and usability of behavior intervention plans (BIP) based on descriptive functional behavior assessments (FBA). Plans developed by behavior experts were rated as high in technical adequacy, but low in acceptability. Conversely, plans developed by key stakeholders were highly acceptable, but lacked technical adequacy. However, when the process included both behavior experts and key stakeholders, BIPs were considered both acceptable and technically adequate. Thus, the BIPs developed by behavior analysts may be marginalized and implementation may be less likely to occur in the absence of key stakeholder input. Thus, a practical commitment to effective interventions that are implemented and maintained with integrity over time requires that behavior analysts consider motivational variables such as the alignment of interventions with the values, reinforcers, and punishers of relevant stakeholders.

Clinical Expertise

All of the key components for expert behavior analytic practice (i.e., identification of important behavioral problems, recognition of underlying behavioral processes, weighing of evidence supporting various treatment options, selecting and implementing treatments in complex social contexts, engaging in ongoing data-based decision making, and being responsive to client values and context) require clinical expertise. Clinical expertise refers to the competence attained by practitioners through education, training, and experience that results in effective practice (American Psychological Association Presidential Task Force of Evidence-Based Practice 2006 ). Clinical expertise is the means by which the best available evidence is applied to individual cases in all their complexity. Based on the work of Goodheart ( 2006 ), we suggest that clinical expertise in EBP of ABA includes (a) knowledge of the research literature and its applicability to particular clients, (b) incorporation of the conceptual system of ABA, (c) breadth and depth of clinical and interpersonal skills, (d) integration of client values and context, (e) recognition of the need for outside consultation, (f) data-based decision making, and (g) ongoing professional development. In the sections that follow, we describe each component of clinical expertise in ABA.

Knowledge and Application of the Research Literature

ABA practitioners must be skilled in applying the best available evidence to unique cases in specific contexts. The role of the best available evidence in EBP of ABA was discussed above. Practitioners need to be knowledgeable about the scientific literature and able to appropriately apply the literature to behaviors, clients, and contexts that are rarely a perfect match to the behaviors, clients, and contexts in any particular study. This confluence of knowledge and skillful application requires that the behavior analyst respond to the functionally important features of cases. A great deal of training is necessary to build the expertise required to discriminate critical functional features from those that are incidental. These discriminations must be made with respect to the presenting problem (i.e., the behavioral patterns that have been identified as problematic, their antecedent stimuli, motivating operations, and consequences); client variables such as histories, skills, and preferences; and contextual variables that may impact the effectiveness of various treatment options as applied to the particular case. These skills are reflected in BACB Guidelines 1.01 and 2.10 cited above.

Incorporation of the Conceptual System

The critical features of a case must be identified and mapped onto the conceptual system of ABA. It is not enough to recognize that a particular feature of the environment is important; it must also be understood in terms of its likely behavioral function. This initial conceptualization is necessary in order to generate reasonable hypotheses that may be tested in more thorough analyses. Developing the skill of describing cases in terms of likely behavioral functions typically requires a great deal of formal and informal training as well as ongoing learning from experience. These repertoires are usually acquired through extensive training, supervised practice, and the ongoing feedback of client outcomes. This is recognized in BACB Guidelines; for example, 4.0 states that “the behavior analyst designs programs that are based on behavior analytic principles” (BACB 2010 ).

Breadth and Depth of Clinical and Interpersonal Skills

Evidence-based practitioners of behavior analysis must be able to implement various assessment and intervention procedures with fidelity, and often to train and supervise others to implement such procedures with fidelity. Further, clinical expertise in ABA requires that the practitioner have effective interpersonal skills. For example, he must be able to explain the behavioral philosophy and approach, in nonbehavioral terms, to various audiences who may have different theoretical orientations. BCBA Guidelines 1.05 specifies that behavior analysts “use language that is fully understandable to the recipient of those services” (BACB 2010 ).

Integration of Client Values and Context

In all aspects of their work, practitioners of evidence-based ABA must integrate the values and preferences of the client and other stakeholders as well as the features of the specific context that may impact the effectiveness of an intervention. These factors can be considered additional variables that the behavior analyst must attend to when planning and providing behavior-analytic services. For example, when assessment data suggest behavior serves a particular function, a range of intervention alternatives may be considered (see Geiger, Carr, and LeBlanc for an example of a model for selecting treatments for escape-maintained problem behavior). A caregiver’s statements might suggest that one type of intervention may not be viable due to limited resources while another treatment may be acceptable based on financial considerations, available resources, or other practical factors; the behavior analyst must have the training and expertise to evaluate and incorporate these factors into initial treatment selection and to re-evaluate these concerns as a part of progress monitoring for both treatment integrity and client improvement. BACB Guideline 4.0 states that the behavior analyst “involves the client … in the planning of … programs, [and] obtains the consent of the client” and 4.1 states that “if environmental conditions hamper implementation of the behavior analytic program, the behavior analyst seeks to eliminate the environmental constraints, or identifies in writing the obstacles to doing so” (BACB 2010 ).

Recognition of Need for Outside Consultation

Behavior analysts engaging in responsible evidence-based practice discriminate between behaviors and contexts that are within the scope of their training and those that are not, and respond differently based on this discrimination. For example, a behavior analyst who has been trained to provide assessment and intervention for severe problem behavior may not have the specific training to provide organizational behavior management services to a corporation; in this case, a behavior analyst with clinical expertise would make this discrimination and seek additional consultation or make appropriate referrals. This aspect of expertise is described in BACB ( 2010 ) Guidelines 1.02 and 2.02.

Data-Based Decision Making

Data-based decision making plays a central role in the practice of ABA and is an indispensable feature of clinical expertise. The process of data-based decision making includes identifying useful measurement pinpoints, constructing measurement systems, and graphing results, as well as identifying meaningful patterns in data, interpreting these patterns, and making appropriate responses to them (e.g., maintaining, modifying, replacing, or ending a program). The functional features of the case, the best available research evidence, and the new evidence obtained through progress monitoring must inform these judgments and are central to this model of EBP of ABA. BACB ( 2010 ) Guidelines 4.07 and 4.08 specify that behavior analysts collect data to assess progress and modify programs on the basis of data.

Ongoing Professional Development

Clinical expertise is not static; rather, it requires ongoing professional development. Clinical expertise in ABA requires ongoing contact with the research literature to ensure that practice reflects current knowledge about the most effective and efficient assessment and intervention procedures. The critical literature includes primary empirical research as well as reviews and syntheses such as those described in the section on “ Best Available Evidence ”. In addition, professional consensus on important topics for professional practice evolves over time. For example, in ABA, there has been increased emphasis recently on ethics and supervision competence. All of these dynamics point to the need for ongoing professional development. This is reflected in the requirement that certified behavior analysts “undertake ongoing efforts to maintain competence in the skills they use by reading the appropriate literature, attending conferences and conventions, participating in workshops, and/or obtaining Behavior Analyst Certification Board certification” (Guideline 1.03, BACB 2010 ).

Conclusions

We propose that EBP of ABA be understood as a professional decision-making framework that draws on the best available evidence, client values and context, and clinical expertise. We argue that this conception of EBP of ABA is more compatible with the basic tenets of ABA and more closely aligned with definitions of EBP in other fields than that provided by Smith ( 2013 ). It is noteworthy that this notion of EBP is not necessarily in conflict with many of the observations and arguments put forth by Smith ( 2013 ). His concerns were primarily about how to define and validate EST, which is an important way to inform practitioners about the best available evidence to integrate into their overall EBP.

Given the close alignment between the proposed framework of EBP of ABA and broadly accepted descriptions of behavior analytic practice, one might wonder whether EBP offers anything new. We believe that the EBP of ABA framework, offered here, has several important implications for our field. First, this framework draws together numerous elements of ABA practice into a single coherent system, which can help behavior analysts provide an explicit rationale for their decision-making to clients and other stakeholders. The EBP of ABA provides a decision-making framework that supports a cogent and transparent description of (a) the evidence considered, including direct and frequent measurement of the client’s behavior; (b) why this evidence was identified as the “best available” for the particular case; (c) how client values and contextual factors influenced the process; and (d) the ways in which clinical expertise was used to conceptualize the case and integrate the various considerations. This transparency and explicitness allows the behavior analyst to offer empirically based treatment recommendations while addressing the concerns raised by stakeholders. It also highlights the critical analysis required to be an effective behavior analyst. For example, if an EST is available and appropriate, the behavior analyst can describe the relevance and certainty of the evidence for this intervention. If no relevant EST is available, the behavior analyst can describe how the best available evidence supports the intervention and emphasize the importance of progress monitoring.

Second, the EBP framework prompts the behavior analyst to refer to the important client values that underlie the goals of intervention, the specific methods of intervention, and describe how the intervention is supported by features of the context. This requires the behavior analyst to explicitly recognize that the effectiveness of an intervention is always context dependent. By serving as a prompt, the EBP framework should increase behavior analysts’ adherence to this central tenet of ABA.

Third, by explicitly recognizing the role of clinical expertise, the framework gives the behavior analyst a way to talk about the complex skills required to make appropriate decisions about client needs. In addition, the fact that the proposed definition of EBP of ABA is so closely aligned with definitions in other professions such as medicine and psychology that it provides a common framework and language for communicating about a particular case that can enhance collaboration between behavior analysts and other professionals.

Fourth, this framework for EBP of ABA suggests further development of behavior analysis as well. Examination of the meaning of best available evidence encourages behavior analysts to continue to refine methods for systematically reviewing research literature and identifying ESTs. Further, behavior analysts could better support EBP if we developed methods for validating other units of intervention such as practice elements, kernels, and even the principles of behavior; when these are invoked to support interventions, they must be supported by a clearly specified research base.

Finally, the explicit recognition of the role of clinical expertise in the EBP of ABA has important implications for training behavior analysts. This framework suggests that decision-making is at the heart of EBP of ABA and could be an organizing theme for ABA training programs. Training programs could systematically teach students to articulate the chain of logic that is the basis for their treatment recommendations. The chain of logic would include statements about which research was considered and why, how the client’s values influenced decision-making, and how contextual factors influenced the selection and adaptation (if necessary) of the treatment. This type of training could be embedded in all instructional activities. Formally requiring students to articulate a rationale for the decisions and receiving feedback about their decisions would sharpen their clinical expertise.

In addition to influencing our behavior analytic practice, the EBP of ABA framework impacts our relationship with other members of the broader human service field as well as individuals and agencies that control contingencies relevant to practitioners and scientists. Methodologically rigorous reviews that identify ESTs and other treatments supported by the best available evidence are extremely important for working with organizations that control funding for behavior analytic research and practice. Federal funding for research and service provision is moving strongly towards EBP and ESTs. This trend is clear in education through the No Child Left Behind Act of 2001 , the Individuals with Disabilities Education Act of 2004 , the funding policies of IES, and the What Works Clearinghouse. The recent memorandum by the Director of the Office of Management and Budget (Zients 2012 ) makes it clear that the importance of EBP is not limited to a single discipline or to one political party. In addition, insurance companies are increasingly making reimbursement decisions based, in part, on whether or not credible scientific evidence supports the use of the treatment (Small 2004 ). The insurance companies have consistently adopted criteria for scientific evidence that are closely related to EST (Bogduk and Fraifeld 2010 ). As a result, reimbursement for ABA services may depend on the scientific credibility of EST reviews, a critical component of EBP. Methodologically rigorous reviews that identify ESTs within a broader framework of EBP appear to be critical for ABA to maintain and expand its access to federal funding and insurance reimbursement for services. Establishment of this literature base will require behavior analysts to develop appropriate methods for reviewing and summarizing research based on single-subject designs. IES has established such standards for reviewing studies, but to date, there are no accepted methods for calculating a measure of effect size as an objective basis for combining result across studies (Kratochwill et al. 2013 ). If behavior analysts develop such a measure, it would reflect a significant methodological advance as a field and it would increase the credibility of behavior analytic research with agencies that fund research and services.

EBP of ABA emphasizes the research-supported selection of treatments and data-driven decisions about treatment progress that have always been at the core of ABA. ABA’s long-standing recognition of the importance of social validity is reflected in the definition of EBP. This framework for EBP of ABA offers many positive professional consequences for scientists and practitioners while promoting the best of the behavior analytic tradition and making contact with developments in other disciplines and the larger context in which behavior analysts work.

• Albin RW, Lucyshyn JM, Horner RH, Flannery KB. Contextual fit for behavior support plans. In: Koegel LK, Koegel RL, Dunlap G, editors. Positive behavioral support: Including people with difficult behaviors in the community. Baltimore: Brookes; 1996. pp. 81–92. [ Google Scholar ]
• American Occupational Therapy Association Occupational therapy practice framework: domain and process (2nd ed.) American Journal of Occupational Therapy. 2008; 62 :625–683. doi: 10.5014/ajot.62.6.625. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• American Psychological Association (2005). Policy statement on evidence-based practice in psychology. http://www.apa.org/practice/resources/evidence/evidence-based-statement.pdf .
• American Psychological Association Presidential Task Force of Evidence-Based Practice Evidence-based practice in psychology. American Psychologist. 2006; 61 :271–285. doi: 10.1037/0003-066X.61.4.271. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• American Speech-Language-Hearing Association (2005). Evidence-based practice in communication disorders [position statement]. www.asha.org/policy .
• Baer DM, Wolf MM, Risley TR. Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis. 1968; 1 :91–97. doi: 10.1901/jaba.1968.1-91. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baer DM, Wolf MM, Risley TR. Some still-current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis. 1987; 20 :313–327. doi: 10.1901/jaba.1987.20-313. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Behavior Analyst Certification Board (2010). Guidelines for responsible conduct for behavior analysts. http://www.bacb.com/index.php?page=57 .
• Benazzi L, Horner RH, Good RH. Effects of behavior support team composition on the technical adequacy and contextual-fit of behavior support plans. The Journal of Special Education. 2006; 40 (3):160–170. doi: 10.1177/00224669060400030401. [ CrossRef ] [ Google Scholar ]
• Bogduk N, Fraifeld EM. Proof or consequences: who shall pay for the evidence in pain medicine? Pain Medicine. 2010; 11 (1):1–2. doi: 10.1111/j.1526-4637.2009.00770.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bushell D, Jr, Baer DM. Measurably superior instruction means close, continual contact with the relevant outcome data. Revolutionary! In: Gardner R III, Sainato DM, Cooper JO, Heron TE, Heward WL, Eshleman J, Grossi TA, editors. Behavior analysis in education: Focus on measurably superior instruction. Pacific Grove: Brooks; 1994. pp. 3–10. [ Google Scholar ]
• Carnine D. Expanding the notion of teachers’ rights: access to tools that work. Journal of Applied Behavior Analysis. 1992; 25 (1):13–19. doi: 10.1901/jaba.1992.25-13. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carr JE, Severtson JM, Lepper TL. Noncontingent reinforcement is an empirically supported treatment for problem behavior exhibited by individuals with developmental disabilities. Research in Developmental Disabilities. 2009; 30 :44–57. doi: 10.1016/j.ridd.2008.03.002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chorpita BF. The frontier of evidence-based practice. In: Kazdin AE, Weisz JR, editors. Evidence-based psychotherapies for children and adolescents. New York: Oxford; 2003. pp. 42–59. [ Google Scholar ]
• Chorpita BF, Daleiden EL, Weisz JR. Identifying and selecting the common elements of evidence based interventions: a distillation and matching model. Mental Health Services Research. 2005; 7 :5–20. doi: 10.1007/s11020-005-1962-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chorpita BF, Becker KD, Daleiden EL. Understanding the common elements of evidence-based practice: misconceptions and clinical examples. Journal of the American Academy of Child and Adolescent Psychiatry. 2007; 46 :647–652. doi: 10.1097/chi.0b013e318033ff71. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook BG, Cook SC. Unraveling evidence-based practices in special education. Journal of Special Education. 2013; 47 (2):71–82. doi: 10.1177/0022466911420877. [ CrossRef ] [ Google Scholar ]
• Cooper JO, Heron TE, Heward WL. Applied behavior analysis. 2. Upper Saddle River: Pearson; 2007. [ Google Scholar ]
• Detrich, R. (Chair) (2009). Evidence-based, empirically supported, best practice: What does it all mean? Symposium conducted at the annual meeting of the Association for Behavior Analysis International, Phoenix, AZ.
• Detrich R, Slocum TA, Spencer TD. Evidence-based education and best available evidence: Decision-making under conditions of uncertainty. In: Cook BG, Tankersley M, Landrum TJ, editors. Advances in learning and behavioral disabilities, 26. Bingly, UK: Emerald; 2013. pp. 21–44. [ Google Scholar ]
• Embry DD. Community-based prevention using simple, low-cost, evidence-based kernels and behavior vaccines. Journal of Community Psychology. 2004; 32 :575–591. doi: 10.1002/jcop.20020. [ CrossRef ] [ Google Scholar ]
• Embry DD, Biglan A. Evidence-based kernels: fundamental units of behavioral influence. Clinical Child and Family Psychology Review. 2008; 11 :75–113. doi: 10.1007/s10567-008-0036-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fisher WW, Piazza CC, Roane HS, editors. Handbook of applied behavior analysis. New York: Guilford Press; 2011. [ Google Scholar ]
• Fixsen DL, Naoom SF, Blase KA, Friedman RM, Wallace F. Implementation research: A synthesis of the literature (FMHI publication #231) Tampa: University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network; 2005. [ Google Scholar ]
• Goodheart CD. Evidence, endeavor, and expertise in psychology practice. In: Goodheart CD, Kazdin AE, Sternberg RJ, editors. Evidence-based psychotherapy: Where practice and research meet. Washington, D.C.: APA; 2006. pp. 37–61. [ Google Scholar ]
• Goodman KW. Ethics and evidence-based education: Fallibility and responsibility in clinical science. New York: Cambridge University Press; 2003. [ Google Scholar ]
• Heward WL, et al., editors. Focus on behavior analysis in education: Achievements, challenges, and opportunities. Upper Saddle River: Prentice Hall; 2005. [ Google Scholar ]
• Horner RH, Carr EG, Halle J, McGee G, Odom S, Wolery M. The use of single-subject research to identify evidence-based practice in special education. Exceptional Children. 2005; 71 (2):165–179. doi: 10.1177/001440290507100203. [ CrossRef ] [ Google Scholar ]
• Horner RH, Sugai G, Todd AW, Lewis-Palmer T. Schoolwide positive behavior support. In: Bambera LM, Kern L, editors. Individualized supports for students with problem behaviors: Designing positive behavior plans. New York: Guilford Press; 2005. pp. 359–390. [ Google Scholar ]
• Individuals with Disabilities Education Improvement Act of 2004, 70, Fed. Reg., (2005).
• Institute of Education Sciences, US. Department of Education. (n.d.). What Works Clearinghouse Procedures and Standards Handbook (No. Version 3.0). Washington DC. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v3_0_standards_handbook.pdf .
• Institute of Medicine . Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press; 2001. [ PubMed ] [ Google Scholar ]
• Johnston JM, Pennypacker HS. Strategies and tactics of behavioral research. 2. Hillsdale: Erlbaum; 1993. [ Google Scholar ]
• Jones RJ, Azrin NH. Behavioral engineering: stuttering as a function of stimulus duration during speech synchronization. Journal of Applied Behavior Analysis. 1969; 2 :223–229. doi: 10.1901/jaba.1969.2-223. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kazdin AE. Psychotherapy for children and adolescents: Directions for research and practice. New York: Oxford University Press; 2000. [ Google Scholar ]
• Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-case designs technical documentation. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf .
• Kratochwill TR, Hitchcock JH, Horner RH, Levin JR, Odom SL, Rindskopf DM, et al. Single-case intervention research design standards. Remedial & Special Education. 2013; 34 (1):26–38. doi: 10.1177/0741932512452794. [ CrossRef ] [ Google Scholar ]
• Madden GJ, Dube WV, Hackenberg TD, Hanley GP, Lattal KA, editors. American Psychological Association handbook of behavior analysis. Washington, DC: American Psychological Association; 2013. [ Google Scholar ]
• Maggin DM, O’Keeffe BV, Johnson AH. A quantitative synthesis of single-subject meta-analyses in special education, 1985–2009. Exceptionality. 2011; 19 :109–135. doi: 10.1080/09362835.2011.565725. [ CrossRef ] [ Google Scholar ]
• Maggin DM, Johnson AH, Chafouleas SM, Ruberto LM, Berggren M. A systematic evidence review of school-based group contingency interventions for students with challenging behavior. Journal of School Psychology. 2012; 50 :625–654. doi: 10.1016/j.jsp.2012.06.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McIntosh K, Filter KJ, Bennett JL, Ryan C, Sugai G. Principles of sustainable prevention: designing scale–up of School–wide Positive Behavior Support to promote durable systems. Psychology in the Schools. 2010; 47 (1):5–21. [ Google Scholar ]
• National Autism Center . National Standards Project: Findings and conclusions. Randolph: National Autism Center; 2009. [ Google Scholar ]
• No Child Left Behind Act of 2001, Pub. L. No. 107-110. (2002).
• Polsgrove L. Reflections on the past and future. Education and Treatment of Children. 2003; 26 :337–344. [ Google Scholar ]
• Riley-Tillman TC, Chafouleas SM. Using interventions that exist in the natural environment to increase treatment integrity and social influence in consultation. Journal of Educational & Psychological Consultation. 2003; 14 (2):139–156. doi: 10.1207/s1532768xjepc1402_3. [ CrossRef ] [ Google Scholar ]
• Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. British Medical Journal. 1996; 312 (7023):71. doi: 10.1136/bmj.312.7023.71. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sackett DL, Straus SE, Richardson WS, Rosenberg W, Haynes RB, editors. Evidence-based medicine: How to teach and practice EBM. Edinburgh: Livingstone; 2000. [ Google Scholar ]
• Shanahan, T., Callison, K., Carriere, C., Duke, N.K., Pearson, P.D., Schatschneider, C., et al. (2010). Improving reading comprehension in kindergarten through 3rd grade: A practice guide (NCEE 2010-4038). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. http://ies.ed.gov/ncee/wwc/publications/practiceguides . Accessed 12 Sept 2013
• Sidman M. Tactics of scientific research: Evaluating experimental data in psychology. New York: Basic Books; 1960. [ Google Scholar ]
• Slocum, T. A., & Wilczynski, S. (2008). The unit of analysis in evidence-based practice . Invited paper presented at the meeting the Association for Behavior Analysis International, Chicago, Il.
• Slocum TA, Detrich R, Spencer TD. Evaluating the validity of systematic reviews to identify empirically supported treatments. Education and Treatment of Children. 2012; 35 :201–234. doi: 10.1353/etc.2012.0009. [ CrossRef ] [ Google Scholar ]
• Slocum TA, Spencer TD, Detrich R. Best available evidence: three complementary approaches. Education and Treatment of Children. 2012; 35 :27–55. [ Google Scholar ]
• Small RH. Maximize the likelihood of reimbursement when appealing managed care medical necessity denials. Getting Paid in Behavioral Healthcare. 2004; 9 (12):1–3. [ Google Scholar ]
• Smith T. What is evidence-based behavior analysis? The Behavior Analyst. 2013; 36 :7–33. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Strain PS, Barton EE, Dunap G. Lessons learned about the utility of social validity. Education and Treatment of Children. 2012; 35 (2):183–200. doi: 10.1353/etc.2012.0007. [ CrossRef ] [ Google Scholar ]
• Wilczynski SM. Risk and strategic decision-making in developing evidence-based practice guidelines. Education and Treatment of Children. 2012; 35 :291–311. doi: 10.1353/etc.2012.0012. [ CrossRef ] [ Google Scholar ]
• Wolf M. Social validity: the case for subjective measurement, or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis. 1978; 11 :203–214. doi: 10.1901/jaba.1978.11-203. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zients, J. D. (2012). M-12-14. Memorandum to the heads of executive departs. From: Jeffrey D. Zients, Acting Director. Subject: use of evidence and evaluation in the 2014 Budget. www.whitehouse.gov/sites/default/files/omb/…/2012/m-12-14.pdf . Accessed 30 Sept 2012

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• Perspective
• Published: 20 March 2023

A manifesto for applying behavioural science

• Michael Hallsworth   ORCID: orcid.org/0000-0002-7868-4727 1  

Nature Human Behaviour volume  7 ,  pages 310–322 ( 2023 ) Cite this article

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Recent years have seen a rapid increase in the use of behavioural science to address the priorities of public and private sector actors. There is now a vibrant ecosystem of practitioners, teams and academics building on each other’s findings across the globe. Their focus on robust evaluation means we know that this work has had an impact on important issues such as antimicrobial resistance, educational attainment and climate change. However, several critiques have also emerged; taken together, they suggest that applied behavioural science needs to evolve further over its next decade. This manifesto for the future of applied behavioural science looks at the challenges facing the field and sets out ten proposals to address them. Meeting these challenges will mean that behavioural science is better equipped to help to build policies, products and services on stronger empirical foundations—and thereby address the world’s crucial challenges.

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There has been “a remarkable increase in behavioural studies and interventions in public policy on a global scale” over the past 15 years 1 . This growth has been built on developments taking place over many preceding decades. One was the increasing empirical evidence of the importance of non-conscious drivers of behaviour. While psychologists have studied these drivers since at least as far back as the work of William James and Wilhelm Wundt in the nineteenth century, they received renewed attention from the research agenda that showed how “heuristics and biases” influence judgement and decision-making 2 . These and other studies led many psychologists to converge on dual-process theories of behaviour that proposed that rapid, intuitive and non-conscious cognitive processes sit alongside deliberative, reflective and self-aware ones 3 .

These theories challenged explanations that foregrounded the role of conscious attitudes, motivations and intentions in determining actions 4 . One result was the creation of the field of behavioural economics, which developed new explanations for why observed behaviour diverged from existing economic models 5 . For example, the concept of “mental accounting” showed how people assign money to certain purposes and—contrary to standard economic theory—are reluctant to repurpose those sums, even when they might benefit from doing so 6 .

Behavioural economics may represent only one strand of applied behavioural science, but it has attracted substantial attention. By the mid-2000s, these advances had an increasingly receptive audience among some governments and policymakers 7 . The publication of the book Nudge in 2008 responded to this demand by using the evidence mentioned earlier to create practical policy solutions (Box 1 ) 8 . Then, in 2010, the UK government set up its Behavioural Insights Team 9 . The creation of the Behavioural Insights Team is notable because it became “a paradigmatic example for the translation of behavioural insights into public policy” that acted as “a blueprint for the establishment of similar units elsewhere” 10 , 11 , 12 . Similar initiatives were adopted by many public sector bodies at the local, national and supra-national levels and by private companies large and small 1 , 11 , 13 , 14 . The Organisation for Economic Development and Cooperation has labelled this creation of more than 200 dedicated public entities a “paradigm shift” 15 that shows that applied behavioural science has “taken root in many ways across many countries around the world and across a wide range of sectors and policy areas” 16 .

This history is necessarily selective; it does not attempt to cover the full range of work in the behavioural sciences. Rather, my focus is on the main ways that approaches often grouped under the term ‘behavioural insights’ have been applied to practical issues in the public and private sectors over the past 15 years 17 (see Box 1 for definitions of these and other terms). These approaches have been adopted in both developed and developing economies, and their precise forms of implementation have varied from context to context 18 . However, a crucial point to emphasize is that they have gone far beyond the self-imposed limits of nudges, even if that label is still used (often unhelpfully) as a blanket term. Instead, a broader agenda has emerged that explores how behavioural science can be integrated into core public and private sector activities such as regulation, taxation, strategy and operations. This broader agenda is reflected in the creation of research programmes on “behavioural public policy” 19 or “behavioural public administration” 20 .

Proponents of these approaches can point to improved outcomes in many areas, including health 21 , education 22 , sustainability 23 and criminal justice 24 . Yet criticisms have emerged alongside these successes. For example, there is an ongoing debate about how publication bias may have inflated the published effect sizes of nudge interventions 25 , 26 . Other criticisms target the goals, assumptions and techniques associated with recent applications of behavioural science (Box 2 ).

This Perspective attempts to respond to these criticisms by setting out an agenda to ensure that applied behavioural science can fulfil its potential in the coming decades. It does so by offering ten proposals, as summarized in Table 1 . These proposals fall into three categories: scope (the range and scale of issues to which behavioural science is applied), methods (the techniques and resources that behavioural science deploys) and values (the principles, ideals and standards of conduct that behavioural scientists adopt). These proposals are the product of a non-systematic review of relevant literature and my experience of applying behavioural science. They are not an attempt to represent expert consensus; they aim to provoke debate as well as agreement.

Figure 1 shows how each proposal aims to address one or more of the criticisms set out in Box 2 . Figure 1 also indicates how responsibilities for implementing the proposals are allocated among four major groups in the behavioural science ecosystem: practitioners (individuals or teams who apply behavioural science findings in practical settings), the clients who commission these practitioners (for example, public or private sector organizations), academics working in the behavioural sciences (including disciplines such as anthropology, economics and sociology) and funders who support the work of these academics. These groups constitute the ‘we’ referred to in the rest of the paper, which summarizes a full-length, in-depth report available at www.bi.team .

The left side shows common criticisms made of the behavioural insights approach. The middle column presents ten proposals to improve the way behavioural science is applied. These proposals are organized into three categories (scope, methods and values), which are represented by red, blue and yellow, respectively. The arrows from the criticisms to the proposals show which of the latter attempt to address the former. The matrix on the right shows the four main groups involved with implementing the proposals: practitioners, clients, academics and funders. The dots in each column indicate that the relevant group will need to make a substantive contribution to achieving the goals of the proposal in the corresponding row.

Box 1 Glossary of main terms

Behavioural science . In its broadest sense, a discipline that uses scientific methods to generate and test theories that explain and predict the behaviour of individuals, groups and populations. This piece focuses particularly on the implications of dual-process theories of behaviour. Behavioural science is different from ‘the behavioural sciences’, which refers to a broader group of any scientific disciplines that study behaviour.

Behavioural insights . The application of findings from behavioural science to analyse and address practical issues in real-world settings, usually coupled with a rigorous evaluation of the effects of any interventions. In the current piece, this term is used interchangeably with ‘applied behavioural science’.

Behavioural economics . The application of findings from behavioural science to the field of economics to create explanations for economic behaviour that often diverge from the principles of neoclassical economic theory.

Nudge . The design of choices so that non-conscious cognitive processes lead individuals to select the option that leaves them better off, as judged by themselves. Nudges do not involve coercion or any substantial change to economic incentives, leaving people with a meaningful ability to choose a different option from the one that the choice architect intends.

Box 2 Criticisms of the behavioural insights approach

Limited impact . The approach has focused on more tractable and easy-to-measure changes at the expense of bigger impacts; it has just been tinkering around the edges of fundamental problems 29 , 50 , 172 .

Failure to reach scale . The approach promotes a model of experimentation followed by scaling, but it has not paid enough attention to how successful scaling happens—and the fact that it often does not happen 18 .

Mechanistic thinking . The approach has promoted a simple, linear and mechanistic approach to understanding behaviour that ignores second-order effects and spillovers (and employs evaluation methods that assume a move from A to B against a static background) 29 , 62 , 173 .

Flawed evidence base . The replication crisis has challenged the evidence base underpinning the behavioural insights approach, adding to existing concerns such as the duration of its interventions’ effects 79 , 174 .

Lack of precision . The approach lacks the ability to construct precise interventions and establish what works for whom, and when. Instead, it relies either on overgeneral frameworks or on disconnected lists of biases 80 , 92 , 94 .

Overconfidence . The approach can encourage overconfidence and overextrapolation from its evidence base, particularly when testing is not an option 175 .

Control paradigm . The approach is elitist and pays insufficient attention to people’s own goals and strategies; it uses concepts such as irrationality to justify attempts to control the behaviour of individuals, since they lack the means to do so themselves 176 , 177 .

Neglect of the social context . The approach has a limited, overly cognitive and individualistic view of behaviour that neglects the reality that humans are embedded in established societies and practices 125 , 178 , 179 .

Ethical concerns . The behavioural insights approach will face more ethics, transparency and privacy conundrums as it attempts more ambitious and innovative work 143 , 145 , 154 .

Homogeneity of participants and perspectives . The range of participants in behavioural science research has been narrow and unrepresentative 164 ; homogeneity in the locations and personal characteristics of behavioural scientists influences their viewpoints, practices and theories 124 , 166 .

Use behavioural science as a lens

The early phase of the behavioural insights movement was marked by scepticism about whether effects obtained in laboratories would translate to real-world settings 27 . In response, practitioners developed standard approaches that could demonstrate a clear causal link between an intervention and an outcome 28 . In practice, these approaches directed attention towards how the design of specific aspects of a policy, product or service influences discrete behaviours by actors who are considered mostly in isolation 29 .

These standard approaches are strong and have produced valuable results in many contexts around the world 20 , 30 . However, in the aggregate, they have also fostered a perspective centred on the metaphor of behavioural science as a specialist tool. This view mostly limits behavioural science to the role of fixing concrete aspects of predetermined interventions rather than aiding the consideration of broader policy goals 31 .

Over time, this view has created a self-reinforcing perception that only certain kinds of tasks are suitable for behavioural scientists 29 . Opportunities, skills and ambitions have been constricted as a result; a rebalancing is needed. Behavioural science also has much to say about pressing societal issues such as discrimination, pollution and economic mobility and the structures that produce them 32 , 33 . These ambitions have always been present in the behavioural insights movement 34 , but the factors just outlined acted against their being realized more fully 35 .

The first step towards achieving these ambitions is to replace the dominant metaphor of behavioural science as a tool. Instead, behavioural science should be understood as a lens that can be applied to any public or private issue. This change offers several advantages:

A lens metaphor shows that behavioural science can enhance the use of standard policy options (for example, revealing new ways of structuring taxes) rather than just acting as an alternative to them.

A lens metaphor conveys that the uses of behavioural science are not limited to creating new interventions. A behavioural science lens can, for example, help to reassess existing actions and understand how they may have unintended effects. It emphasizes the behavioural diagnosis of a situation or issue rather than pushing too soon to define a precise target outcome and intervention 31 .

Specifying that this lens can be applied to any action conveys the error of separating ‘behavioural’ and ‘non-behavioural’ issues: most of the goals of private and public action depend on certain behaviours happening (or not). Behavioural science should therefore be integrated into an organization’s core activities rather than acting as an optional specialist tool 36 .

It may seem odd to start with a change of metaphor, but the primary problem here is one of perception. Behavioural science itself shows us the power of framing: the metaphors we use shape the way we behave and therefore can be agents of change 37 . Metaphors are particularly important in this case because the task of broadening the use of behavioural science requires making a compelling case to decision makers 38 . The metaphor of behavioural science as a tool has established credibility and acceptance in a defined area; expanding beyond that area is the task for the next decade.

Build behavioural science into organizations

The second proposal is to broaden the scope of how behavioural science is used in organizations. Given that many dedicated behavioural science teams exist worldwide, it is understandable that much attention has been paid to the question of how they should be set up successfully. However, this focus has diverted attention from considering how to use behavioural science to shape organizations themselves 39 . We need to talk less about how to set up a dedicated behavioural science team and more about how behavioural science can be integrated into an organization’s standard processes. For example, as well as trying to ensure that a departmental budget includes provisions for behavioural science, why not use behavioural science to improve the way this budget is created (for example, are managers anchored to outdated spending assumptions) 40 ?

The overriding message here is for greater focus on the organizational changes that indirectly apply or support behavioural science principles, rather than just thinking through how the direct and overt use of behavioural science can be promoted in an organization. One advantage to this approach is that it can help organizations to address problems with scaling interventions 36 . If some of the barriers to scaling concern cognitive biases in organizations, these changes could minimize the effect of such biases 41 . Rather than starting with a behavioural science project and then trying to scale it, we could start by looking at operations at scale and understanding how they can be influenced.

It is useful to understand how this approach maps onto existing debates about how to set up a behavioural function in organizations. Doing so reveals six main scenarios, as shown in Table 2 . In the ‘baseline’ scenario, there is limited awareness of behavioural science in the organization, and its principles are not incorporated into processes. In the ‘nudged organization’, behavioural science awareness is still low, but its principles have been used to redesign processes to create better outcomes for staff or service users. In ‘proactive consultancy’, leaders may have set up a dedicated behavioural team without grafting it onto the organization’s standard processes. This lack of institutional grounding puts the team in a less resilient position, meaning that it must always search for new work. In ‘call for the experts’, an organization has concentrated behavioural expertise, but there are also prompts and resources that allow this expertise to be integrated into business as usual. Expertise is not widespread, but access to it is. Processes stimulate demand for behavioural expertise that the central team can fulfil. In ‘behavioural entrepreneurs’, there is behavioural science capacity distributed throughout the organization, through either direct capacity building or recruitment. The problem is that organizational processes do not support these individual pockets of knowledge. Finally, a ‘behaviourally enabled organization’ is one where there is knowledge of behavioural science diffused throughout the organization, which also has processes that reflect this knowledge and support its deployment.

Most discussions make it seem like the meaningful choice is between the different columns in Table 2 —how to organize dedicated behavioural science resources. Instead, the more important move is from the top row to the bottom row: moving from projects to processes, from commissions to culture. A useful way of thinking about this task is about building or upgrading the “choice infrastructure” of the organization 42 . In other words, we should place greater focus on the institutional conditions and connections that support the direct and indirect ways that behavioural science can infuse organizations.

Working out how best to build the choice infrastructure in organizations should be a major priority for applied behavioural science. Already we can see that some features will be crucial: reducing the costs of experimentation, creating a system that can learn from its actions, and developing new and better ways of using behavioural science principles to analyse the behavioural effects of organizational processes, rules, incentives, metrics and guidelines 36 .

See the system

Many important policy challenges emerge from complex adaptive systems, where change often does not happen in a linear or easily predictable way, and where coherent behaviour can emerge from interactions without top-down direction 43 . There are many examples of such systems in human societies, including cities, markets and political movements 44 . These systems can create “wicked problems”—such as the COVID-19 pandemic—where ideas of success are contested, changes are nonlinear and difficult to model, and policies have unintended consequences 45 .

This reality challenges the dominant behavioural science approach, which usually assumes stability over time, keeps a tight focus on predefined target behaviours and predicts linear effects on the basis of a predetermined theory of change 46 . The result, some argue, is a failure to understand how actors are acting and reacting in a complex system that leads policymakers to conclude they are being irrational—and then actually disrupt the system in misguided attempts to correct perceived biases or inefficiencies 47 , 48 , 49 .

These criticisms may overstate the case, but they point to a way forward. Behavioural science can be improved by using aspects of complexity thinking to offer new, credible and practical ways of addressing major policy issues. The first step is to reject crude distinctions of ‘upstream’ versus ‘downstream’ or the ‘individual frame’ versus the ‘system frame’ 50 . Instead, complex adaptive systems show that higher-level features of a system can actually emerge from the lower-level interactions of actors participating in the system 44 . When they become the governing features of the system, they then shape the lower-level behaviour until some other aspect emerges, and the fluctuations continue. An example might be the way that new coronavirus variants emerged in particular settings and then went on to change the course of the whole pandemic, requiring new overall strategic responses.

In other words, we are dealing with “cross-scale behaviours” 49 . For example, norms, rules, practices and culture itself can emerge from aggregated social interactions; these features then shape cognition and behavioural patterns in turn 51 . Recognizing cross-scale behaviours means that behavioural science could:

Identify “leverage points” where a specific shift in behaviour will produce wider system effects 52 . One option is to identify when and where tipping points are likely to occur in a system and then either nudge them to occur or not, depending on the policy goal 53 . For example, if even a subset of consumers decides to switch to a healthier version of a food product, this can have broader effects on a population’s health through the way the food system responds by restocking and product reformulation 54 .

Model the collective implications of individuals using simple heuristics to navigate a system. For example, new models show how small changes to simple heuristics that guide savings (in this case, how quickly households copy the savings behaviours of neighbours) can lead to the sudden emergence of inequalities in wealth 55 .

Find targeted changes to features of a system that create the conditions for wide-ranging shifts in behaviour to occur. For example, a core driver of social media behaviours is the ease with which information can be shared 46 . Even minor changes to this parameter can drive widespread changes—some have argued that such a change is what created the conditions leading to the Arab Spring, for example 56 .

This approach also suggests that a broader change in perspective is needed. We need to realize the flaws in launching interventions in isolation and then moving on when a narrowly defined goal has been achieved. Instead, we need to see the longer-term impact on a system of a collection of different policies with varying goals 57 . The best approach may be “system stewardship”, which focuses on creating the conditions for behaviours and indirectly steering adaptation towards overall goals 58 .

Of course, not every problem will involve a complex adaptive system; for simple issues, standard approaches to applying behavioural science work well. Behavioural scientists should therefore develop the skills to recognize the type of system that they are facing (see the system) and then choose their approach accordingly. These skills can be developed through agent-based simulations 59 , immersive technologies 60 or just basic checklists 61 .

Put randomized controlled trials in their place

Randomized controlled trials (RCTs) have been a core part of applied behavioural science, and they work well in relatively simple and stable contexts. But they can fare worse in complex adaptive systems, whose many shifting connections can make it difficult to keep a control group isolated and where a narrow focus on predetermined outcomes may neglect others that are important but difficult to predict 43 , 62 .

We can strengthen RCTs to deal better with complexity. We can try to gain a better understanding of the system interactions and anticipate how they may play out, perhaps through “dark logic” exercises that try to trace potential harms rather than just benefits 63 . For example, we might anticipate that sending parents text messages encouraging them to talk to their children about the school science curriculum may achieve this outcome at the expense of other school-supporting behaviours—as turned out to be the case 64 . Engaging the people who will implement and participate in an intervention will be a key part of this effort.

Another option is to set up RCTs to measure diffusion and contagion in networks, either by creating separate online environments or by randomizing real-world clusters, such as separate villages 65 , 66 . Finally, we can build feedback and adaptation into the design of the RCT and the intervention, allowing adjustments to changing conditions 67 , 68 . Options include using two-stage trial protocols 69 , evolutionary RCTs 70 , sequential multiple assignment randomized trials 71 and ‘bandit’ algorithms that identify high-performing interventions and allocate more people to them 72 .

Behavioural science can also be used to enhance alternative ways of measuring impacts—in particular, agent-based modelling, which tries to simulate the interactions between the different actors in a system 73 . The agents in these models are mostly assumed to be operating on rational choice principles 74 , 75 . There is therefore an opportunity to build in more evidence about the drivers of behaviour—for example, habits and social comparisons 49 .

Replication, variation and adaptation

The ‘replication crisis’ of the past decade has seen intense debate and concern about the reliability of behavioural science findings. Poor research practices were a major cause of the replication crisis; the good news is that many have improved as a result 76 , 77 . Now there are sharper incentives to preregister analysis plans, greater expectations that data and code will be freely shared, and wider acceptance of post-publication review of findings 78 .

Behavioural scientists need to secure and build on these advances to move towards a future where appropriately scoped meta-analyses of high-quality studies (including deliberate replications) are used to identify the most reliable interventions, develop an accurate sense of the likely size of their effects and avoid the weaker options. We have a responsibility to discard ideas if solid evidence now shows that they are shaky, and to offer a realistic view of what behavioural science can accomplish 18 .

That responsibility also requires us to have a hard conversation about heterogeneity in results: the complexity of human behaviour creates so much statistical noise that it is often hard to detect consistent signals and patterns 79 . The main drivers of heterogeneity are that contexts influence results and that the effect of an intervention may vary greatly between groups within a population 80 , 81 . For example, choices of how to set up experiments vary greatly between studies and researchers, in ways that often go unnoticed 82 . A recent study ran an experiment to measure the impact of these contextual factors. Participants were randomly allocated to studies designed by different research teams to test the same hypothesis. For four of the five research questions, studies actually produced effects in opposing directions. These “radically dispersed” results indicate that “idiosyncratic choices in stimulus design have a very large effect on observed results” 83 . These factors complicate the idea of replication itself: a ‘failed’ replication may not show that a finding was false but rather show how it exists under some conditions and not others 84 .

These challenges mean that applied behavioural scientists need to set a much higher bar for claiming that an effect holds true across many unspecified settings 85 . There is a growing sense that interventions should be talked about as hypotheses that were true in one place and that may need adapting to be true elsewhere 18 , 86 .

Narrative changes need to be complemented by specific proposals. The first concerns data collection: behavioural scientists should expand studies to include (and thus examine) a wider range of contexts and participants and gather richer data about them. To date, only a small minority of behavioural studies have provided enough information to see how effects vary 87 . Moreover, the gaps in data coverage may result from and create systemic issues in society: certain groups may be excluded or may have their data recorded differently from others 88 . Coordinated multi-site studies will be needed to collect enough data to explore heterogeneity systematically; crowdsourced studies offer particular promise for testing context and methods 83 . Realistically, this work is going to require a major investment in research infrastructure to set up standing panels of participants, coordinate between institutions, and reduce barriers to data collection and transfer 80 . These efforts cannot be limited to just a few countries.

Behavioural scientists also need to get better at judging how strongly an intervention’s results were linked to its context and therefore how much adaptation it needs 81 . We should use and modify frameworks from implementation science to develop such judgement 89 . Finally, we need to codify and cultivate the practical skills that successfully adapt interventions to new contexts; expertise in behavioural science should not be seen as simply knowing about concepts and findings in the abstract. It is therefore particularly valuable to learn from practitioners how they adapted specific interventions to new contexts. These accounts are starting to emerge, but they are still rare 18 , since researchers are incentivized to claim universality for their results rather than report and value contextual details 82 .

Beyond lists of biases

The heterogeneity in behavioural science findings also means that our underlying theories need to improve: we are lacking good explanations for why findings vary so much 84 . This need for better theories can be seen as part of a wider ‘theory crisis’ in psychology, which has thrown up two big concerns for behavioural science 90 , 91 .

The first stems from the fact that theories of behaviour often try to explain phenomena that are complex and wide-ranging 92 . If you are trying to show how emotion and cognition interact (for example), this involves many causes and interactions. Trying to cover this variability can produce descriptions of relationships and definitions of constructs that are abstract and imprecise 85 . The result is theories that are vague and weak, since they can be used to generate many different hypotheses—some of which may actually contradict each other 90 . That makes theories hard to disprove, and so weak theories stumble on, unimproved 93 .

The other concern is that theories can make specific predictions, but they are disconnected from each other—and from a deeper, general framework that can provide broader explanations (such as evolutionary theory) 94 . The main way this issue affects behavioural science is through heuristics and biases. Examples of individual biases are accessible, popular and how many people first encounter behavioural science. These ideas are incredibly useful, but they have often been presented as lists of standalone curiosities in a way that is incoherent, reductive and deadening. Presenting lists of biases does not help us to distinguish or organize them 95 , 96 , 97 . Such lists can also create overconfident thinking that targeting a specific bias (in isolation) will achieve a certain outcome 98 .

Perhaps most importantly, focusing on lists of biases distracts us from answering core underlying questions. When does one or another bias apply? Which are widely applicable, and which are highly specific? How does culture or life experience affect whether a bias influences behaviour or not 99 , 100 ? These are highly practical questions when one is faced with tasks such as taking an intervention to new places.

The concern for behavioural science is that it uses both these high-level frameworks (such as dual-process theories) and jumbled collections of heuristics and biases, with little in the middle to draw both levels together 94 . Recent years have seen valuable advances in connecting and systematizing theories 101 , 102 . At the same time, there are various ongoing attempts to create strong theories: “coherent and useful conceptual frameworks into which existing knowledge can be integrated” 93 (see also refs. 91 , 103 , 104 ). Naturally, such work should continue, but I think that applied behavioural science will benefit particularly from theories that are practical. By this I mean:

They fill the gap between day-to-day working hypotheses and comprehensive and systematic attempts to find universal underlying explanations.

They are based on data rather than being derived from pure theorizing 105 .

They can generate testable hypotheses, so they can be disproved 106 .

They specify the conditions under which a prediction applies or does not 85 .

They are geared towards realistic adaptation by practitioners and offer “actionable steps toward solving a problem that currently exists in a particular context in the real world” 107 .

Resource rationality may be a good example of a practical theory. It starts from the basis that people make rational use of their limited cognitive resources 108 . Given that there is a cost to thinking, people will look for solutions that balance choice quality with effort. Resource rationality can offer a “unifying framework for a wide range of successful models of seemingly unrelated phenomena and cognitive biases” that can be used to build models for how people act 108 .

A recent study has shown how these models not only can predict how people will respond to different kinds of nudges in certain contexts but also can be integrated with machine learning to create an automated method for constructing “optimal nudges” 109 . Such an approach could reveal new kinds of nudges and make creating them much more efficient. More reliable ways of developing personalized nudges are also possible. These are all highly practical benefits coming from applying a particular theory.

Predict and adjust

Hindsight bias is what happens when we feel ‘I knew it all along’, even if we did not 110 . When the results of an experiment come in, hindsight bias may mean that behavioural scientists are more likely to think that they had predicted them or quickly find ways of explaining why they occurred. Hindsight bias is a big problem because it breeds overconfidence, impedes learning, dissuades innovation and prevents us from understanding what is truly unexpected 111 , 112 .

In response, behavioural scientists should establish a standard practice of predicting the results of experiments and then receiving feedback on how their predictions performed. Hindsight bias can flourish if we do not systematically capture expectations or priors about what the results of a study will be 113 . Making predictions provides regular, clear feedback of the kind that is more likely to trigger surprise and reassessment rather than hindsight bias 114 . Establishing the average expert prediction—which may be different from the null hypothesis in an experiment—clearly reveals when results challenge the consensus 115 .

There are existing practices to build on here, such as the practice of preregistering hypotheses and trial protocols and the use of a Bayesian approach to make priors explicit. Indeed, more and more studies are explicitly integrating predictions 116 , 117 . However, barriers lie in the way of further progress. People may not welcome the ensuing challenge to their self-image, predicting may seem like one thing too many on the to-do list, and the benefits lie in the future. Some responses to these challenges are to make predicting easy by incorporating it into standard processes; minimize threats to predictors’ self-image (for example, by making and feeding back predictions anonymously) 118 ; give concrete prompts for learning and reflection, to disrupt the move from surprise to hindsight bias 119 ; and build learning from prediction within and between institutions.

Be humble, explore and enable

This proposal is made up of three connected ideas. First, behavioural scientists need to become more aware of the limits of their knowledge and to avoid fitting behaviours into pre-existing ideas around biases or irrationality. Second, they should broaden the exploratory work they conduct, in terms of gaining new types of qualitative data and recognizing how experiences vary by group and geography. Finally, they should develop new approaches to enable people to apply behavioural science themselves—and adopt new criteria for judging when these approaches are appropriate.

Humility is important because behavioural scientists (like other experts) may overconfidently rely on decontextualized principles that do not match the real-world setting for a behaviour 29 . Deeper inquiry can reveal reasonable explanations for what seem to be behavioural biases 120 . In response, those applying behavioural science should avoid using the term ‘irrationality’, which can limit attempts to understand actions in context; acknowledge that diagnoses of behaviour are provisional and incomplete (epistemic humility) 121 ; and design processes and institutions to counteract overconfidence 122 .

How do we conduct these deeper inquiries? Three areas demand particular focus in the future. First, pay greater attention to people’s goals and strategies and their own interpretations of their beliefs, feelings and behaviours 123 . Second, reach a wider range of experiences, including marginalized voices and communities, understanding how structural inequalities can lead to expectations and experiences varying greatly by group and geography 124 . Third, recognize how apparently universal cognitive processes are shaped by specific contexts, thereby unlocking new ways for behavioural science to engage with values and culture 125 , 126 . For example, one influential view of culture is that it influences action “not by providing the ultimate values toward which action is oriented but by shaping a repertoire or ‘toolkit’ of habits, skills, and styles” 127 . There are similarities here to the heuristics-and-biases toolkit perspective on behaviour: behavioural scientists could start explaining how and when certain parts of the toolkit become more or less salient.

More can and should be done to broaden ownership of behavioural science approaches. Many (but far from all) behavioural science applications have been top-down, with a choice architect enabling certain outcomes 8 , 128 . One route is to enable people to become more involved in designing interventions that affect them—and “nudge plus” 129 , “self-nudges” 130 and “boosts” 131 have been proposed as ways of doing this. Reliable criteria are needed to decide when enabling approaches may be appropriate, including whether the opportunity to use an enabling approach exists; ability and motivation; preferences; learning and setup costs; equity impacts; and effectiveness, recognizing that evidence on this point is still emerging 132 , 133 .

But these new approaches should not be seen simplistically as enabling alternatives to disempowering nudges 134 . Instead, we need to consider how far the person performing the behaviour is involved in shaping the initiative itself, as well as the level and nature of any capacity created by the intervention. People may be heavily engaged in selecting and developing a nudge intervention that nonetheless does not trigger any reflection or build any skills 135 . Alternatively, a policymaker may have paternalistically assumed that people want to build up their capacity to perform an action, when in fact they do not. This is the real choice to be made.

A final piece missing from current thinking is that enabling people can lead to a major decentring of the use of behavioural science. If more people are enabled to use behavioural science, they may decide to introduce interventions that influence others 136 . Rather than just creating self-nudges through altering their immediate environments, they may decide that wider system changes are needed instead. A range of people could be enabled to create nudges that generate positive societal change (with no central actors involved). This points towards a future where policy or product designers act less like (choice) architects and more like facilitators, brokers and partnership builders 137 .

Data science for equity

Recent years have seen growing interest in using new data science techniques to reliably analyse the heterogeneity of large datasets 138 , 139 . Machine learning is claimed to offer more sophisticated, reliable and data-driven ways of detecting meaningful patterns in datasets 140 , 141 . For example, a machine learning approach has been shown to be more effective than conventional segmentation approaches at analysing patterns of US household energy usage to reduce peak consumption 142 .

A popular idea is to use such techniques to better understand what works best for certain groups and thereby tailor an offering to them 143 . Scaling an intervention stops being about a uniform roll-out and instead becomes about presenting recipients with the aspects that are most effective for them 144 .

This vision is often presented as straightforward and obviously desirable, but it runs almost immediately into ethical quandaries and value judgements. People are unlikely to know what data have been used to target them and how; the specificity of the data involved may make manipulation more likely, since it may exploit sensitive personal vulnerabilities; and expectations of universality and non-discrimination in public services may be violated 143 , 145 .

Closely related to manipulation concerns is the fear that data science will open up new opportunities to exploit, rather than to help, the vulnerable 146 . One aspect is algorithmic bias. Models using data that reflect historical patterns of discrimination can produce results that reinforce these outcomes 147 . Since disadvantaged groups are more likely to be subject to the decisions of algorithms, there is a particular risk that inequalities will be perpetuated—although some studies argue that algorithms are actually less likely to be biased than human judgement 148 , 149 .

There is also emerging evidence that people often object to personalization. While they support some personalized services, they consistently oppose advertising that is customized on the basis of sensitive information—and they are generally against the collection of the information that personalization relies on 150 . To navigate this landscape, behavioural scientists need to examine four factors:

Who does the personalization target, and using what criteria? Many places have laws or norms to ensure equal treatment based on personal characteristics. When does personalization violate those principles?

How is the intervention constructed? To what extent do the recipients have awareness of the personalization, choice over whether it occurs, control over its level or nature, and the opportunity to give feedback on it 151 ?

When is it directed? Is it at a time when the participant is vulnerable? Would they probably regret it later, if they had time to reflect?

Why is personalization happening? Does it aim to exploit and harm or to support and protect, recognizing that those terms are often contested?

Taking these factors into account, I propose that the main opportunity is for data science to identify the ways in which an intervention or situation appears to increase inequalities, and reduce them 152 . For example, groups that are particularly likely to miss a filing requirement could be offered pre-emptive help. Algorithms can be used to better explain the causes of increased knee pain experienced in disadvantaged communities, thereby giving physicians better information to act on 153 .

I call this idea data science for equity. It addresses the ‘why’ factor by using data science to support, not exploit. ‘Data science for equity’ may seem like a platitude, but it is a very real choice: the combination of behavioural and data science is powerful and has been used to create harm in the past. Moreover, it needs to be complemented by attempts to increase agency (the ‘how’ factors), as in a recent study that showed how boosts can be used to help people to detect micro-targeting of advertising 154 , and studies that obtain more data on which uses of personalization people find acceptable.

No “view from nowhere”

The final proposal is one of the most wide-ranging, challenging and important. For the philosopher Thomas Nagel, the “view from nowhere” was an objective stance that allowed us to “transcend our particular viewpoint” 155 . Taking such a stance may not be possible for behavioural scientists. We bring certain assumptions and ways of seeing to what we do; we are always situated in, embedded in and entangled with ideas and situations 124 . We cannot assume that there is some set-aside position from which to observe the behaviour of others; no objective observation deck outside society exists 156 .

Behavioural scientists are defined by having knowledge, skills and education; many of them can use these resources to shape public and private actions. They are therefore in a privileged position, but they may not see the extent to which they hold elite positions that stop them from understanding people who think differently (for example, those who are sceptical of education) 157 . The danger is that elites place their group values and preferences on others, while thinking that they are adopting a view from nowhere 158 , 159 . This does not mean that they can never act or opine, but rather that they need to carefully understand their own positionality and those of others before doing so.

There have been repeated concerns that the field is still highly homogeneous in other ways as well. Gender, race, physical abilities, sexuality and geography also influence the viewpoints, practices and theories of behavioural scientists 160 , 161 . Only a quarter of the behavioural insights teams catalogued in a 2020 survey were based in the Global South 162 . An over-reliance on using English in cognitive science has led to the impact of language on thought being underestimated 163 . The past decade has shown how behaviours can vary greatly from culture to culture, even as psychology has tended to generalize from relatively small and unrepresentative samples 164 . Behavioural science studies often present data from Western, educated, industrialized, rich and democratic samples as more generalizable to humans as a whole 165 . So, rather than claiming that science is value-free, we need to find realistic ways of acknowledging and improving this reality 166 .

A starting point is for behavioural scientists to cultivate self-scrutiny by querying how their identities and experiences contribute to their stance on a topic. Hypothesis generation could particularly benefit from this exercise, since arguably it is closely informed by the researcher’s personal priorities and preferences 167 . Behavioural scientists could be actively reflecting on interventions in progress, including what factors are contributing to power dynamics 168 . Self-scrutiny may not be enough. We should also find more ways for people to judge researchers and decide whether they want to participate in research—going beyond consent forms. If they do participate, there are many opportunities to combine behavioural science with co-design 128 .

Finally, we should take actions to increase diversity (of several kinds) among behavioural scientists, teams, collaborations and institutions. Doing this requires addressing barriers such as the lack of professional networks connecting the Global North and Global South, and the time needed to build understanding of the tactics required to write successful grant applications from funders 169 . In many countries, much more could be done to increase the ethnic and racial diversity of the behavioural science field—for example, through support for starting and completing PhDs or through reducing the substantial racial gaps present in much public funding of research 170 , 171 .

Applied behavioural science has seen rapid growth and meaningful achievements over the past decade. Although the popularity of nudging provided its initial impetus, an ambition soon formed to apply a broader range of techniques to a wider range of goals. However, a set of credible critiques have emerged as levels of activity have grown. As Fig. 1 indicates, there are proposals that can address these critiques (and progress is already being made on some of them). When considered together, these proposals present a coherent vision for the scope, methods and values of applied behavioural science.

This vision is not limited to technical enhancements for the field; it also covers questions of epistemology, identity, politics and praxis. A common theme throughout the ten proposals is the need for self-reflective practice that is aware of how its knowledge and approaches have originated and how they are situated. In other words, a main priority for behavioural scientists is to recognize the various ways that their own behaviour is being shaped by structural, institutional, environmental and cognitive factors.

Realizing these proposals will require sustained work and experiencing the discomfort of disrupting what may have become familiar and comfortable practices. That is a particular problem because incentives for change are often weak or absent. Improving applied behavioural science has some characteristics of a social dilemma: the benefits are diffused across the field as a whole, while the costs fall on any individual party who chooses to act (or act first). Practitioners are often in competition. Academics often want to establish a distinctive research agenda. Commissioners are often rewarded for risk aversion. Impaired coordination is particularly problematic because coordination forms the basis for several necessary actions, such as the multi-site studies to measure heterogeneity.

Solving these problems will be hard. Funders need to find mechanisms that adequately reward coordination and collaboration by recognizing the true costs involved. Practitioners need to perceive the competitive advantages of adopting new practices and be able to communicate them to clients. Clients themselves need to have a realistic sense of what can be achieved but still be motivated to commit resources. Stepping back, the starting point for addressing these barriers needs to be a change in the narrative about what the field does and could do—a new set of ambitions to aim for. This manifesto aims to help to shape such a narrative.

Straßheim, H. The rise and spread of behavioral public policy: an opportunity for critical research and self-reflection. Int. Rev. Public Policy 2 , 115–128 (2020).

Article   Google Scholar  

Gilovich, T., Griffin, D. & Kahneman, D. (eds) Heuristics and Biases: The Psychology of Intuitive Judgment (Cambridge Univ. Press, 2002).

Lieberman, M. D. Social cognitive neuroscience: a review of core processes. Annu. Rev. Psychol. 58 , 259–289 (2007).

Article   PubMed   Google Scholar  

Ajzen, I. The theory of planned behaviour. Organ. Behav. Hum. Decis. Process. 50 , 179–211 (1991).

Thaler, R. Misbehaving: The Making of Behavioral Economics (W.W. Norton, 2015).

Thaler, R. Mental accounting and consumer choice. Mark. Sci. 4 , 199–214 (1985).

Pykett, J., Jones, R. & Whitehead, M. (eds) Psychological Governance and Public Policy: Governing the Mind, Brain and Behaviour (Routledge, 2017).

Thaler, R. & Sunstein, S. Nudge: Improving Decisions about Health, Wealth, and Happiness (Yale Univ. Press, 2008).

Hallsworth, M. & Kirkman, E. Behavioral Insights (MIT Press, 2020).

Oliver, A. The Origins of Behavioural Public Policy (Cambridge Univ. Press, 2017).

Straßheim, H. & Beck, S. in Handbook of Behavioural Change and Public Policy (eds Beck, S. & Straßheim, H.) 1–22 (Edward Elgar, 2019).

Ball, S. & Feitsma, J. The boundaries of behavioural insights: observations from two ethnographic studies. Evid. Policy 16 , 559–577 (2020).

Afif, Z., Islan, W., Calvo-Gonzalez, O. & Dalton, A. Behavioural Science Around the World: Profiles of 10 Countries (World Bank, 2018).

Science that can change the world. Nat. Hum. Behav . https://doi.org/10.1038/s41562-019-0642-2 (2019).

Behavioural Insights and New Approaches to Policy Design: The Views from the Field (OECD, 2015).

Behavioural Insights and Public Policy: Lessons from Around the World (OECD, 2015).

Feitsma, J. N. P. The behavioural state: critical observations on technocracy and psychocracy. Policy Sci. 51 , 387–410 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Mažar, N. & Soman, D. (eds) Behavioral Science in the Wild (Univ. of Toronto Press, 2022).

Oliver, A. Towards a new political economy of behavioral public policy. Public Adm. Rev. 79 , 917–924 (2019).

Grimmelikhuijsen, S., Jilke, S., Olsen, A. L. & Tummers, L. Behavioral public administration: combining insights from public administration and psychology. Public Adm. Rev. 77 , 45–56 (2017).

Cadario, R. & Chandon, P. Which healthy eating nudges work best? A meta-analysis of field experiments. Mark. Sci. 39 , 465–486 (2020).

Damgaard, M. T. & Nielsen, H. S. Nudging in education. Econ. Educ. Rev. 64 , 313–342 (2018).

Ferrari, L., Cavaliere, A., De Marchi, E. & Banterle, A. Can nudging improve the environmental impact of food supply chain? A systematic review. Trends Food Sci. Technol. 91 , 184–192 (2019).

Article   CAS   Google Scholar  

Fishbane, A., Ouss, A. & Shah, A. K. Behavioral nudges reduce failure to appear for court. Science 370 , eabb6591 (2020).

Article   CAS   PubMed   Google Scholar  

Mertens, S., Herberz, M., Hahnel, U. J. & Brosch, T. The effectiveness of nudging: a meta-analysis of choice architecture interventions across behavioral domains. Proc. Natl Acad. Sci. USA 119 , e2107346118 (2022).

Maier, M. et al. No evidence for nudging after adjusting for publication bias. Proc. Natl Acad. Sci. USA 119 , e2200300119 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Levitt, S. D. & List, J. A. What do laboratory experiments measuring social preferences reveal about the real world? J. Econ. Perspect. 21 , 153–174 (2007).

Hansen, P. G. Tools and Ethics for Applied Behavioural Insights: The BASIC Toolkit (OECD, 2019).

Schmidt, R. & Stenger, K. Behavioral brittleness: the case for strategic behavioral public policy. Behav. Public Policy , https://doi.org/10.1017/bpp.2021.16 (2021).

United Nations Behavioural Science Report https://www.uninnovation.network/assets/BeSci/UN_Behavioural_Science_Report_2021.pdf (United Nations Innovation Network, 2021).

Hansen, P. G. What are we forgetting? Behav. Public Policy 2 , 190–197 (2018).

Van Rooij, B. & Fine, A. The Behavioral Code: The Hidden Ways the Law Makes Us Better or Worse (Beacon, 2021).

Andre, P., Haaland, I., Roth, C. & Wohlfart, J. Narratives about the Macroeconomy CEBI Working Paper Series (Center for Economic Behavior and Inequality, 2021).

Dolan, P., Hallsworth, M., Halpern, D., King, D. & Vlaev, I. MINDSPACE: Influencing Behaviour Through Public Policy (Institute for Government and Cabinet Office, 2010).

Meder, B., Fleischhut, N. & Osman, M. Beyond the confines of choice architecture: a critical analysis. J. Econ. Psychol. 68 , 36–44 (2018).

Soman, D. & Yeung, C. (eds) The Behaviorally Informed Organization (Univ. of Toronto Press, 2020).

Thibodeau, P. H. & Boroditsky, L. Metaphors we think with: the role of metaphor in reasoning. PLoS ONE 6 , e16782 (2011).

Feitsma, J. Brokering behaviour change: the work of behavioural insights experts in government. Policy Polit. 47 , 37–56 (2019).

Battaglio, R. P. Jr, Belardinelli, P., Bellé, N. & Cantarelli, P. Behavioral public administration ad fontes : a synthesis of research on bounded rationality, cognitive biases, and nudging in public organizations. Public Adm. Rev. 79 , 304–320 (2019).

Cantarelli, P., Bellé, N. & Belardinelli, P. Behavioral public HR: experimental evidence on cognitive biases and debiasing interventions. Rev. Public Pers. Adm. 40 , 56–81 (2020).

Mayer, S., Shah, R. & Kalil, A. in The Scale-Up Effect in Early Childhood and Public Policy: Why Interventions Lose Impact at Scale and What We Can Do about It (eds List, J. et al.) 41–57 (Routledge, 2021).

Schmidt, R. A model for choice infrastructure: looking beyond choice architecture in behavioral public policy. Behav. Public Policy , https://doi.org/10.1017/bpp.2021.44 (2022).

Magenta Book 2020: Supplementary Guide: Handling Complexity in Policy Evaluation (HM Treasury, 2020).

Boulton, J. G., Allen, P. M. & Bowman, C. Embracing Complexity: Strategic Perspectives for an Age of Turbulence (Oxford Univ. Press, 2015).

Angeli, F., Camporesi, S. & Dal Fabbro, G. The COVID-19 wicked problem in public health ethics: conflicting evidence, or incommensurable values? Hum. Soc. Sci. Commun. 8 , 1–8 (2021).

Google Scholar  

Bak-Coleman, J. B. et al. Stewardship of global collective behavior. Proc. Natl Acad. Sci. USA 118 , e2025764118 (2021).

Dunlop, C. A. & Radaelli, C. M. in Nudge and the Law: A European Perspective (eds Alemanno, A. & Simony, A. L.) 139–158 (Hart, 2015).

Scott, J. C. Seeing Like a State (Yale Univ. Press, 1998).

Schill, C. et al. A more dynamic understanding of human behaviour for the Anthropocene. Nat. Sustain. 2 , 1075–1082 (2019).

Chater, N. & Loewenstein, G. The i-Frame and the s-Frame: how focusing on individual-level solutions has led behavioral public policy astray. Behav. Brain Sci. https://doi.org/10.1017/S0140525X22002023 (2022).

DiMaggio, P. & Markus, H. R. Culture and social psychology: converging perspectives. Soc. Psychol. Q. 73 , 347–352 (2010).

Abson, D. J. et al. Leverage points for sustainability transformation. Ambio 46 , 30–39 (2017).

Andreoni, J., Nikiforakis, N. & Siegenthaler, S. Predicting social tipping and norm change in controlled experiments. Proc. Natl Acad. Sci. USA 118 , e2014893118 (2021).

Hallsworth, M. Rethinking public health using behavioural science. Nat. Hum. Behav. 1 , 612 (2017).

Asano, Y. M., Kolb, J. J., Heitzig, J. & Farmer, J. D. Emergent inequality and business cycles in a simple behavioral macroeconomic model. Proc. Natl Acad. Sci. USA 118 , e2025721118 (2021).

Jones-Rooy, A. & Page, S. E. The complexity of system effects. Crit. Rev. 24 , 313–342 (2012).

Hawe, P., Shiell, A. & Riley, T. Theorising interventions as events in systems. Am. J. Community Psychol. 43 , 267–276 (2009).

Hallsworth, M. System Stewardship: The Future of Policymaking? (Institute for Government, 2011).

Rates, C. A., Mulvey, B. K., Chiu, J. L. & Stenger, K. Examining ontological and self-monitoring scaffolding to improve complex systems thinking with a participatory simulation. Instr. Sci. 50 , 199–221 (2022).

Fernandes, L., Morgado, L., Paredes, H., Coelho, A. & Richter, J. Immersive learning experiences for understanding complex systems. In iLRN 2019 London-Workshop, Long and Short Paper, Poster, Demos, and SSRiP Proceedings from the Fifth Immersive Learning Research Network Conference 107–113 http://hdl.handle.net/10400.2/8368 (Verlag der Technischen Universität Graz, 2019).

Annex 1 Checklist for Assessing the Level of Complexity of a Program (International Initiative for Impact Evaluation), https://www.3ieimpact.org/sites/default/files/2021-07/complexity-blg-Annex1-Checklist_assessing_level_complexity.pdf (2021).

Deaton, A. & Cartwright, N. Understanding and misunderstanding randomized controlled trials. Soc. Sci. Med. 210 , 2–21 (2018).

Bonell, C., Jamal, F., Melendez-Torres, G. J. & Cummins, S. ‘Dark logic’: theorising the harmful consequences of public health interventions. J. Epidemiol. Community Health 69 , 95–98 (2015).

Robinson, C. D., Chande, R., Burgess, S. & Rogers, T. Parent engagement interventions are not costless: opportunity cost and crowd out of parental investment. Educ. Eval. Policy Anal. 44 , 170–177 (2021).

Centola, D. How Behaviour Spreads: The Science of Complex Contagions (Princeton Univ. Press, 2018).

Kim, D. A. et al. Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. Lancet 386 , 145–153 (2015).

Berry, D. A. Bayesian clinical trials. Nat. Rev. Drug Discov. 5 , 27–36 (2006).

Marinelli, H. A., Berlinski, S. & Busso, M. Remedial education: evidence from a sequence of experiments in Colombia. J. Hum. Resour . 0320-10801R2 (2021).

Anders, J., Groot, B. & Heal, J. Running RCTs with complex interventions. The Behavioural Insights Team https://www.bi.team/blogs/running-rcts-with-complex-interventions/ (1 November 2017).

Volpp, K. G., Terwiesch, C., Troxel, A. B., Mehta, S. & Asch, D. A. Making the RCT more useful for innovation with evidence-based evolutionary testing. Healthcare 1 , 4–7 (2013).

Kidwell, K. M. & Hyde, L. W. Adaptive interventions and SMART designs: application to child behavior research in a community setting. Am. J. Eval. 37 , 344–363 (2016).

Caria, S., Kasy, M., Quinn, S., Shami, S. & Teytelboym, A. An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan. Warwick Economics Research Papers No. 1335 (2021).

The Complexity Evaluation Toolkit v.1.0, https://www.cecan.ac.uk/wp-content/uploads/2020/08/EPPN-No-03-Agent-Based-Modelling-for-Evaluation.pdf (CECAN, 2021).

Schluter, M. et al. A framework for mapping and comparing behavioural theories in models of social–ecological systems. Ecol. Econ. 131 , 21–35 (2017).

Wijermans, N., Boonstra, W. J., Orach, K., Hentati-Sundberg, J. & Schlüter, M. Behavioural diversity in fishing—towards a next generation of fishery models. Fish Fish. 21 , 872–890 (2020).

Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22 , 1359–1366 (2011).

Nelson, L. D., Simmons, J. & Simonsohn, U. Psychology’s Renaissance. Annu. Rev. Psychol. 69 , 511–534 (2018).

Frias-Navarro, D., Pascual-Llobell, J., Pascual-Soler, M., Perezgonzalez, J. & Berrios-Riquelme, J. Replication crisis or an opportunity to improve scientific production? Eur. J. Educ. 55 , 618–631 (2020).

Stanley, T. D., Carter, E. C. & Doucouliagos, H. What meta-analyses reveal about the replicability of psychological research. Psychol. Bull. 144 , 1325–1346 (2018).

Bryan, C. J., Tipton, E. & Yeager, D. S. Behavioural science is unlikely to change the world without a heterogeneity revolution. Nat. Hum. Behav. 5 , 980–989 (2021).

Van Bavel, J. J., Mende-Siedlecki, P., Brady, W. J. & Reinero, D. A. Contextual sensitivity in scientific reproducibility. Proc. Natl Acad. Sci. USA 113 , 6454–6459 (2016).

Brenninkmeijer, J., Derksen, M., Rietzschel, E., Vazire, S. & Nuijten, M. Informal laboratory practices in psychology. Collabra Psychol . 5, 45 (2019).

Landy, J. F. et al. Crowdsourcing hypothesis tests: making transparent how design choices shape research results. Psychol. Bull. 146 , 451 (2020).

McShane, B. B., Tackett, J. L., Böckenholt, U. & Gelman, A. Large-scale replication projects in contemporary psychological research. Am. Stat. 73 , 99–105 (2019).

Sanbonmatsu, D. M., Cooley, E. H. & Butner, J. E. The impact of complexity on methods and findings in psychological science. Front. Psychol. 11 , 580111 (2021).

Cartwright, N. & Hardie, J. Evidence-Based Policy: A Practical Guide to Doing It Better (Oxford Univ. Press, 2012).

Yeager, D. To change the world, behavioral intervention research will need to get serious about heterogeneity. OSF https://osf.io/zuh93/ (2020).

Snow, T. Mind the gap between the truth and data. Nesta https://www.nesta.org.uk/blog/mind-gap-between-truth-and-data/ (9 October 2019).

Damschroder, L. J. et al. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement. Sci. 4 , 50 (2009).

Oberauer, K. & Lewandowsky, S. Addressing the theory crisis in psychology. Psychon. Bull. Rev. 26 , 1596–1618 (2019).

Borsboom, D., van der Maas, H. L., Dalege, J., Kievit, R. A. & Haig, B. D. Theory construction methodology: a practical framework for building theories in psychology. Perspect. Psychol. Sci. 16 , 756–766 (2021).

Sanbonmatsu, D. M. & Johnston, W. A. Redefining science: the impact of complexity on theory development in social and behavioral research. Perspect. Psychol. Sci. 14 , 672–690 (2019).

Fried, E. I. Theories and models: what they are, what they are for, and what they are about. Psychol. Inq. 31 , 336–344 (2020).

Muthukrishna, M. & Henrich, J. A problem in theory. Nat. Hum. Behav. 3 , 221–229 (2019).

Schimmelpfennig, R. & Muthukrishna, M. Cultural evolutionary behavioural science in public policy. Behav. Public Policy https://doi.org/10.1017/bpp.2022.40 (2023)

Kwan, V. S., John, O. P., Kenny, D. A., Bond, M. H. & Robins, R. W. Reconceptualizing individual differences in self-enhancement bias: an interpersonal approach. Psychol. Rev. 111 , 94 (2004).

Mezulis, A. H., Abramson, L. Y., Hyde, J. S. & Hankin, B. L. Is there a universal positivity bias in attributions? A meta-analytic review of individual, developmental, and cultural differences in the self-serving attributional bias. Psychol. Bull. 130 , 711 (2004).

Smets, K. There is more to behavioral economics than biases and fallacies. Behavioral Scientist http://behaviouralscientist.org/there-is-more-to-behavioural-science-than-biases-and-fallacies/ (24 July 2018).

Rand, D. G. Cooperation, fast and slow: meta-analytic evidence for a theory of social heuristics and self-interested deliberation. Psychol. Sci. 27 , 1192–1206 (2016).

Gelfand, M. J. Rule Makers, Rule Breakers: How Tight and Loose Cultures Wire Our World (Constable & Robinson, 2018).

West, R. et al. Development of a formal system for representing behaviour-change theories. Nat. Hum. Behav. 3 , 526–536 (2019).

Hale, J. et al. An ontology-based modelling system (OBMS) for representing behaviour change theories applied to 76 theories. Wellcome Open Res 5 , 177 (2020).

van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci. 16 , 682–697 (2021).

Smaldino, P. E. How to build a strong theoretical foundation. Psychol. Inq. 31 , 297–301 (2020).

Abner, G. B., Kim, S. Y. & Perry, J. L. Building evidence for public human resource management: using middle range theory to link theory and data. Rev. Public Pers. Adm. 37 , 139–159 (2017).

Moore, L. F., Johns, G. & Pinder, C. C. in Middle Range Theory and the Study of Organizations (eds Pinder, C. C. & Moore, L. F.) 1–16 (Martinus Nijhoff, 1980).

Berkman, E. T. & Wilson, S. M. So useful as a good theory? The practicality crisis in (social) psychological theory. Perspect. Psychol. Sci. 16 , 864–874 (2021).

Lieder, F. & Griffiths, T. L. Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 43 , e1 (2020).

Callaway, F., Hardy, M. & Griffiths, T. Optimal nudging for cognitively bounded agents: a framework for modeling, predicting, and controlling the effects of choice architectures. Preprint at https://doi.org/10.31234/osf.io/7ahdc (2022).

Roese, N. J. & Vohs, K. D. Hindsight bias. Perspect. Psychol. Sci. 7 , 411–426 (2012).

Henriksen, K. & Kaplan, H. Hindsight bias, outcome knowledge and adaptive learning. Qual. Saf. Health Care 12 , ii46–ii50 (2003).

Bukszar, E. & Connolly, T. Hindsight bias and strategic choice: some problems in learning from experience. Acad. Manage. J. 31 , 628–641 (1988).

DellaVigna, S., Pope, D. & Vivalt, E. Predict science to improve science. Science 366 , 428–429 (2019).

Munnich, E. & Ranney, M. A. Learning from surprise: harnessing a metacognitive surprise signal to build and adapt belief networks. Top. Cogn. Sci. 11 , 164–177 (2019).

PubMed   Google Scholar  

Deshpande, M. & Dizon-Ross, R. The (Lack of) Anticipatory Effects of the Social Safety Net on Human Capital Investment Working Paper, https://faculty.chicagobooth.edu/-/media/faculty/rebecca-dizon-ross/research/ssi_rct.pdf (Chicago Booth, 2022).

DellaVigna, S. & Linos, E. RCTs to scale: comprehensive evidence from two nudge units. Econometrica 90 , 81–116 (2022).

Dimant, E., Clemente, E. G., Pieper, D., Dreber, A. & Gelfand, M. Politicizing mask-wearing: predicting the success of behavioral interventions among Republicans and Democrats in the U.S. Sci. Rep. 12 , 7575 (2022).

Ackerman, R., Bernstein, D. M. & Kumar, R. Metacognitive hindsight bias. Mem. Cogn. 48 , 731–744 (2020).

Pezzo, M. Surprise, defence, or making sense: what removes hindsight bias? Memory 11 , 421–441 (2003).

Dorison, C. A. & Heller, B. H. Observers penalize decision makers whose risk preferences are unaffected by loss–gain framing. J. Exp. Psychol. 151 , 2043–2059 (2022).

Porter, T. et al. Predictors and consequences of intellectual humility. Nat. Rev. Psychol. 1 , 524–536 (2022).

Egan, M., Hallsworth, M., McCrae, J. & Rutter, J. Behavioural Government: Using Behavioural Science to Improve How Governments Make Decisions (Behavioural Insights Team, 2018).

Walton, G. M. & Wilson, T. D. Wise interventions: psychological remedies for social and personal problems. Psychol. Rev. 125 , 617 (2018).

Lewis, N. A. Jr What counts as good science? How the battle for methodological legitimacy affects public psychology. Am. Psychol. 76 , 1323 (2021).

Lamont, M., Adler, L., Park, B. Y. & Xiang, X. Bridging cultural sociology and cognitive psychology in three contemporary research programmes. Nat. Hum. Behav. 1 , 866–872 (2017).

Vaisey, S. Motivation and justification: a dual-process model of culture in action. Am. J. Sociol. 114 , 1675–1715 (2009).

Swidler, A. Culture in action: symbols and strategies. Am. Sociol. Rev. 51 , 273–286 (1986).

Richardson, L. & John, P. Co-designing behavioural public policy: lessons from the field about how to ‘nudge plus’. Evid. Policy 17 , 405–422 (2021).

Banerjee, S. & John, P. Nudge plus: incorporating reflection into behavioral public policy. Behav. Public Policy , https://doi.org/10.1017/bpp.2021.6 (2021).

Reijula, S. & Hertwig, R. Self-nudging and the citizen choice architect. Behav. Public Policy 6 , 119–149 (2022).

Hertwig, R. & Grüne-Yanoff, T. Nudging and boosting: steering or empowering good decisions. Perspect. Psychol. Sci. 12 , 973–986 (2017).

Hertwig, R. When to consider boosting: some rules for policy-makers. Behav. Public Policy 1 , 143–161 (2017).

Grüne-Yanoff, T., Marchionni, C. & Feufel, M. A. Toward a framework for selecting behavioural policies: how to choose between boosts and nudges. Econ. Phil. 34 , 243–266 (2018).

Sims, A. & Müller, T. M. Nudge versus boost: a distinction without a normative difference. Econ. Phil. 35 , 195–222 (2019).

Sunstein, C. R. Choosing Not to Choose: Understanding the Value of Choice (Oxford Univ. Press, 2015).

Miller, G. A. Psychology as a means of promoting human welfare. Am. Psychol. 24 , 1063 (1969).

Bason, C. Leading Public Sector Innovation: Co-creating for a Better Society (Policy Press, 2018).

Big-data studies of human behaviour need a common language. Nature 595 , 149–150 (2021).

Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12 , 1100–1122 (2017).

Künzel, S. R., Sekhon, J. S., Bickel, P. J. & Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc. Natl Acad. Sci. USA 116 , 4156–4165 (2019).

Wager, S. & Athey, S. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113 , 1228–1242 (2018).

Todd-Blick, A. et al. Winners are not keepers: characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States. Energy Res. Soc. Sci. 70 , 101595 (2020).

Mills, S. Personalized nudging. Behav. Public Policy 6 , 150–159 (2022).

Soman, D. & Hossain, T. Successfully scaled solutions need not be homogenous. Behav. Public Policy 5 , 80–89 (2021).

Möhlmann, M. Algorithmic nudges don’t have to be unethical. Harvard Business Review, 22 April (2021).

Susser, D., Roessler, B., & Nissenbaum, H. Online manipulation: hidden influences in a digital world. 4 Georget. Law Technol. Rev. 1 (2019).

Abbasi, M., Fridler, A., Schneidegger, C. & Venkatasubramanian, S. Fairness in representation: quantifying stereotyping as a representational harm. In SIAM International Conference on Data Mining , SDM 2019, (eds. Berger-Wolf, T. & Chawla, N.) 801–809 (Society for Industrial and Applied Mathematics, 2019).

Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (St. Martin’s, 2018).

Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366 , 447–453 (2019).

Kozyreva, A., Lorenz-Spreen, P., Hertwig, R., Lewandowsky, S. & Herzog, S. M. Public attitudes towards algorithmic personalization and use of personal data online: evidence from Germany, Great Britain, and the United States. Hum. Soc. Sci. Commun. 8 , 117 (2021).

Kotamarthi, P. This is personal: the do’s and don’ts of personalization in tech. Decision Lab https://thedecisionlab.com/insights/technology/this-is-personal-the-dos-and-donts-of-personalization-in-tech (2022).

Matz, S. C., Kosinski, M., Nave, G. & Stillwell, D. J. Psychological targeting as an effective approach to digital mass persuasion. Proc. Natl Acad. Sci. USA 114 , 12714–12719 (2017).

Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S. & Obermeyer, Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat. Med. 27 , 136–140 (2021).

Lorenz-Spreen, P. et al. Boosting people’s ability to detect microtargeted advertising. Sci. Rep. 11 , 15541 (2021).

Nagel, T. The View from Nowhere (Oxford Univ. Press, 1986).

Sugden, R. The behavioural economist and the social planner: to whom should behavioural welfare economics be addressed? Inquiry 56 , 519–538 (2013).

Liscow, Z. D. & Markovits, D. Democratizing behavioural economics. Yale J. Regul. 39 , 1217–1290 (2022).

Bergman, P., Lasky-Fink, J. & Rogers, T. Simplification and defaults affect adoption and impact of technology, but decision makers do not realize it. Organ. Behav. Hum. Decis. Process. 158 , 66–79 (2020).

Pereira, M. M. Understanding and reducing biases in elite beliefs about the electorate. Am. Polit. Sci. Rev. 115 , 1308–1324 (2021).

Roberts, S. O., Bareket-Shavit, C., Dollins, F. A., Goldie, P. D. & Mortenson, E. Racial inequality in psychological research: trends of the past and recommendations for the future. Perspect. Psychol. Sci. 15 , 1295–1309 (2020).

Lepenies, R. & Małecka, M. in Handbook of Behavioural Change and Public Policy (eds Beck, S. & Straßheim, H.) 344–360 (Edward Elgar, 2019).

Common Thread. From Idea to Immunization:A Blueprint to Building a BI Unit in the Global South https://gocommonthread.com/work/global-gavi/bi (2022).

Blasi, D. E., Henrich, J., Adamou, E., Kemmerer, D. & Majid, A. Over-reliance on English hinders cognitive science. Trends Cogn. Sci. 26 , 1153–1170 (2022).

Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33 , 61–83 (2010).

Cheon, B. K., Melani, I. & Hong, Y. Y. How USA-centric is psychology? An archival study of implicit assumptions of generalizability of findings to human nature based on origins of study samples. Soc. Psychol. Pers. Sci. 11 , 928–937 (2020).

Dupree, C. H. & Kraus, M. W. Psychological science is not race neutral. Perspect. Psychol. Sci. 17 , 270–275 (2022).

Mullainathan, S. Keynote address to the Society of Judgment and Decision Making Annual Conference (2022).

A Guidebook for Community Organizations, Researchers, and Funders to Help Us Get from Insufficient Understanding to More Authentic Truth https://chicagobeyond.org/researchequity/ (Chicago Beyond, 2018).

Asman, S., Casarotto, C., Duflo, A. & Rajkotia, R. Locally-grounded research: strengthening partnerships to advance the science and impact of development research. Innovations for Poverty Action https://www.poverty-action.org/blog/locally-grounded-research-strengthening-partnerships-advance-science-and-impact-development (28 September 2021).

The PhD Project, https://phdproject.org/ (PhD Project, accessed 9 December 2022).

Erosheva, E. A. et al. NIH peer review: criterion scores completely account for racial disparities in overall impact scores. Sci. Adv. 6 , eaaz4868 (2020).

Marteau, T. M. et al. Judging nudging: can nudging improve population health? BMJ 342 , d228 (2011).

Lambe, F. et al. Embracing complexity: a transdisciplinary conceptual framework for understanding behavior change in the context of development-focused interventions. World Dev. 126 , 104703 (2020).

Shrout, P. E. & Rodgers, J. L. Psychology, science, and knowledge construction: broadening perspectives from the replication crisis. Annu. Rev. Psychol. 69 , 487–510 (2018).

IJzerman, H. et al. Use caution when applying behavioural science to policy. Nat. Hum. Behav. 4 , 1092–1094 (2020).

Grüne-Yanoff, T. Old wine in new casks: libertarian paternalism still violates liberal principles. Soc. Choice Welf. 38 , 635–645 (2012).

Rizzo, M. J. & Whitman, G. Escaping Paternalism: Rationality, Behavioral Economics, and Public Policy (Cambridge Univ. Press, 2020).

Ewert, B. Moving beyond the obsession with nudging individual behaviour: towards a broader understanding of behavioural public policy. Public Policy Adm. 35 , 337–360 (2020).

Leggett, W. The politics of behaviour change: nudge, neoliberalism and the state. Policy Polit. 42 , 3–19 (2014).

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Acknowledgements

I thank L. Tublin for her editorial support. I also thank S. Banerjee, E. Berkman, A. Buttenheim, F. Callaway, J. Collins, J. Doctor, A. Gyani, D. Halpern, P. John, T. Marteau, M. Muthukrishna, D. Perera, D. Perrott, K. Ruggeri, R. Schmidt, D. Soman, H. Strassheim, C. Sunstein and members of the Behavioural Insights Team for their feedback on previous drafts.

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Hallsworth, M. A manifesto for applying behavioural science. Nat Hum Behav 7 , 310–322 (2023). https://doi.org/10.1038/s41562-023-01555-3

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Real-world effectiveness and predictors of nurse-led individual cognitive behavioral therapy for mental disorders: an updated pragmatic retrospective cohort study.

1. Introduction

2. materials and methods, 2.1. study design, 2.2. setting, 2.3. participants, 2.4. intervention, 2.5. clinical outcomes, 2.6. sample size, 2.7. analysis, 3.1. participant flow, 3.2. baseline sociodemographic and clinical characteristics, 3.3. intervention received, 3.4. clinical outcomes, 3.5. predictors of positive clinical significance, 4. discussion, 4.1. sample characteristics compared to our earlier study, 4.2. real-world effectiveness compared to our earlier study and studies in other countries, 4.3. predictor of intervention outcome, 4.4. limitations, 4.5. future implications, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, appendix a. the relationship between phq-9 and gad-7 scores before and after nurse-led individual cbt stratified by clinical significance.

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• GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 2022 , 9 , 137–150. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hofmann, S.G.; Asnaani, A.; Vonk, I.J.; Sawyer, A.T.; Fang, A. The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses. Cognit. Ther. Res. 2012 , 36 , 427–440. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McHugh, R.K.; Whitton, S.W.; Peckham, A.D.; Welge, J.A.; Otto, M.W. Patient preference for psychological vs. pharmacologic treatment of psychiatric disorders: A meta-analytic review. J. Clin. Psychiatry 2013 , 74 , 595–602. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Department of Health. From Values to Action: The Chief Nursing Officer’s Review of Mental Health Nursing. Available online: https://wels.open.ac.uk/research-project/caren/node/2929 (accessed on 10 April 2024).
• Horatio: European Psychiatric Nurses. Psychiatric/Mental Health Nursing and Psychotherapy: The Position of Horatio: European Psychiatric Nurses. Available online: https://www.horatio-eu.com/_files/ugd/6e82ff_8df8315614e848018e566a3287063d0d.pdf (accessed on 10 April 2024).
• The Psychological Professions Network. Implementing the NHS Long Term Plan: Maximising the Impact of the Psychological Professions. Available online: https://www.ppn.nhs.uk/resources/ppn-publications/29-implementing-the-nhs-long-term-plan-maximising-the-impact-of-the-psychological-professions/file (accessed on 10 April 2024).
• Haraguchi, T.; Yoshinaga, N.; Hayashi, Y.; Nagai, M. Has the 2016 expansion of mental disorders covered under national health insurance increased the use of cognitive behavioral therapy in Japan? An analysis of the National Open Data Base. Psychiatry Clin. Neurosci. 2021 , 75 , 322–323. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hayashi, Y.; Yoshinaga, N.; Sasaki, Y.; Tanoue, H.; Yoshimura, K.; Kadowaki, Y.; Arimura, Y.; Yanagita, T.; Ishida, Y. How was cognitive behavioural therapy for mood disorder implemented in Japan? A retrospective observational study using the nationwide claims database from FY2010 to FY2015. BMJ Open 2020 , 10 , e033365. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Curran, J.; Brooker, C. Systematic review of interventions delivered by UK mental health nurses. Int. J. Nurs. Stud. 2007 , 44 , 479–509. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• de Pinho, L.M.G.; Sequeira, C.; Sampaio, F.M.C.; Rocha, N.B.; Ozaslan, Z.; Ferre-Grau, C. Assessing the efficacy and feasibility of providing metacognitive training for patients with schizophrenia by mental health nurses: A randomized controlled trial. J. Adv. Nurs. 2021 , 77 , 999–1012. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Espie, C.A.; MacMahon, K.M.; Kelly, H.L.; Broomfield, N.M.; Douglas, N.J.; Engleman, H.M.; McKinstry, B.; Morin, C.M.; Walker, A.; Wilson, P. Randomized clinical effectiveness trial of nurse-administered small-group cognitive behavior therapy for persistent insomnia in general practice. Sleep 2007 , 30 , 574–584. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van der Zweerde, T.; Lancee, J.; Slottje, P.; Bosmans, J.E.; Van Someren, E.J.W.; van Straten, A. Nurse-Guided Internet-Delivered Cognitive Behavioral Therapy for Insomnia in General Practice: Results from a Pragmatic Randomized Clinical Trial. Psychother. Psychosom. 2020 , 89 , 174–184. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tolin, D.F.; McKay, D.; Forman, E.M.; Klonsky, E.D.; Thombs, B.D. Empirically Supported Treatment: Recommendations for a New Model. Clin. Psychol. Sci. Pract. 2015 , 22 , 317–338. [ Google Scholar ] [ CrossRef ]
• Westen, D.; Bradley, R. Empirically Supported Complexity:Rethinking Evidence-Based Practice in Psychotherapy. Curr. Dir. Psychol. Sci. 2005 , 14 , 266–271. [ Google Scholar ] [ CrossRef ]
• Patsopoulos, N.A. A pragmatic view on pragmatic trials. Dialogues Clin. Neurosci. 2011 , 13 , 217–224. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hans, E.; Hiller, W. A meta-analysis of nonrandomized effectiveness studies on outpatient cognitive behavioral therapy for adult anxiety disorders. Clin. Psychol. Rev. 2013 , 33 , 954–964. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hunsley, J.; Lee, C.M. Research-informed benchmarks for psychological treatments: Efficacy studies, effectiveness studies, and beyond. Prof. Psychol. Res. Pract. 2007 , 38 , 21–33. [ Google Scholar ] [ CrossRef ]
• Stewart, R.E.; Chambless, D.L. Cognitive-behavioral therapy for adult anxiety disorders in clinical practice: A meta-analysis of effectiveness studies. J. Consult. Clin. Psychol. 2009 , 77 , 595–606. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Konstantinou, G.N. Pragmatic trials: How to adjust for the ‘Hawthorne effect’? Thorax 2012 , 67 , 562. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McCambridge, J.; Witton, J.; Elbourne, D.R. Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects. J. Clin. Epidemiol. 2014 , 67 , 267–277. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Clark, D.M. Realizing the Mass Public Benefit of Evidence-Based Psychological Therapies: The IAPT Program. Annu. Rev. Clin. Psychol. 2018 , 14 , 159–183. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wakefield, S.; Kellett, S.; Simmonds-Buckley, M.; Stockton, D.; Bradbury, A.; Delgadillo, J. Improving Access to Psychological Therapies (IAPT) in the United Kingdom: A systematic review and meta-analysis of 10-years of practice-based evidence. Br. J. Clin. Psychol. 2021 , 60 , 1–37. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Baigent, M.; Smith, D.; Battersby, M.; Lawn, S.; Redpath, P.; McCoy, A. The Australian version of IAPT: Clinical outcomes of the multi-site cohort study of NewAccess. J. Ment. Health 2023 , 32 , 341–350. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Knapstad, M.; Nordgreen, T.; Smith, O.R.F. Prompt mental health care, the Norwegian version of IAPT: Clinical outcomes and predictors of change in a multicenter cohort study. BMC Psychiatry 2018 , 18 , 260. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yoshinaga, N.; Tanoue, H.; Hayashi, Y. Naturalistic outcome of nurse-led psychological therapy for mental disorders in routine outpatient care: A retrospective chart review. Arch. Psychiatr. Nurs. 2022 , 40 , 43–49. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. BMJ 2007 , 335 , 806–808. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Japan Society for Eating Disorders. A Brief Manual of Enhanced Cognitive Behaviour Therapy (CBT-E) for Eating Disorders. Available online: http://www.jsed.org/wp-content/uploads/2019/05/cbt-e.pdf (accessed on 10 April 2024).
• Japanese Ministry of Health, Labour and Welfare. Mental Health. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/hukushi_kaigo/shougaishahukushi/kokoro/index.html (accessed on 10 April 2024).
• Kroenke, K.; Spitzer, R.L.; Williams, J.B. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 2001 , 16 , 606–613. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Muramatsu, K.; Miyaoka, H.; Kamijima, K.; Muramatsu, Y.; Tanaka, Y.; Hosaka, M.; Miwa, Y.; Fuse, K.; Yoshimine, F.; Mashima, I.; et al. Performance of the Japanese version of the Patient Health Questionnaire-9 (J-PHQ-9) for depression in primary care. Gen. Hosp. Psychiatry 2018 , 52 , 64–69. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Umegaki, Y.; Todo, N. Psychometric properties of the Japanese CES-D, SDS, and PHQ-9 depression scales in university students. Psychol. Assess. 2017 , 29 , 354–359. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Doi, S.; Ito, M.; Takebayashi, Y.; Muramatsu, K.; Horikoshi, M. Factorial Validity and Invariance of the 7-Item Generalized Anxiety Disorder Scale (GAD-7) Among Populations with and Without Self-Reported Psychiatric Diagnostic Status. Front. Psychol. 2018 , 9 , 1741. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Spitzer, R.L.; Kroenke, K.; Williams, J.B.; Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 2006 , 166 , 1092–1097. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Herdman, M.; Gudex, C.; Lloyd, A.; Janssen, M.; Kind, P.; Parkin, D.; Bonsel, G.; Badia, X. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual. Life Res. 2011 , 20 , 1727–1736. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shiroiwa, T.; Fukuda, T.; Ikeda, S.; Igarashi, A.; Noto, S.; Saito, S.; Shimozuma, K. Japanese population norms for preference-based measures: EQ-5D-3L, EQ-5D-5L, and SF-6D. Qual. Life Res. 2016 , 25 , 707–719. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• van Hout, B.; Janssen, M.F.; Feng, Y.S.; Kohlmann, T.; Busschbach, J.; Golicki, D.; Lloyd, A.; Scalone, L.; Kind, P.; Pickard, A.S. Interim scoring for the EQ-5D-5L: Mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health 2012 , 15 , 708–715. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sheehan, K.H.; Sheehan, D.V. Assessing treatment effects in clinical trials with the discan metric of the Sheehan Disability Scale. Int. Clin. Psychopharmacol. 2008 , 23 , 70–83. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yoshida, T.; Otsubo, T.; Tsuchida, H.; Wada, Y.; Kamijima, K.; Fukui, K. The Japanese version of the Sheehan Disability Scale (SDISS): Development, reliability and validity. Jpn. J. Clin. Psychopharmacol. 2004 , 7 , 1645–1653. [ Google Scholar ]
• Clark, D.M.; Canvin, L.; Green, J.; Layard, R.; Pilling, S.; Janecka, M. Transparency about the outcomes of mental health services (IAPT approach): An analysis of public data. Lancet 2018 , 391 , 679–686. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Concato, J.; Peduzzi, P.; Holford, T.R.; Feinstein, A.R. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J. Clin. Epidemiol. 1995 , 48 , 1495–1501. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Peduzzi, P.; Concato, J.; Feinstein, A.R.; Holford, T.R. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J. Clin. Epidemiol. 1995 , 48 , 1503–1510. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Peduzzi, P.; Concato, J.; Kemper, E.; Holford, T.R.; Feinstein, A.R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 1996 , 49 , 1373–1379. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vittinghoff, E.; McCulloch, C.E. Relaxing the rule of ten events per variable in logistic and Cox regression. Am. J. Epidemiol. 2007 , 165 , 710–718. [ Google Scholar ] [ CrossRef ]
• Goldberg, S.B.; Babins-Wagner, R.; Rousmaniere, T.; Berzins, S.; Hoyt, W.T.; Whipple, J.L.; Miller, S.D.; Wampold, B.E. Creating a climate for therapist improvement: A case study of an agency focused on outcomes and deliberate practice. Psychotherapy 2016 , 53 , 367–375. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stein, D.M.; Lambert, M.J. Graduate training in psychotherapy: Are therapy outcomes enhanced? J. Consult. Clin. Psychol. 1995 , 63 , 182–196. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chan, S.W.; Adams, M. Service use, drop-out rate and clinical outcomes: A comparison between high and low intensity treatments in an IAPT service. Behav. Cogn. Psychother. 2014 , 42 , 747–759. [ Google Scholar ] [ CrossRef ]
• Clark, D.M. Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: The IAPT experience. Int. Rev. Psychiatry 2011 , 23 , 318–327. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Griffiths, C.A.; Griffiths, L.J. Recovery and reliable change rates for patients scoring severe on depression, anxiety or impaired functioning in a psychological therapies service: IAPT. Ment. Health Rev. J. 2015 , 20 , 28–35. [ Google Scholar ] [ CrossRef ]
• Gyani, A.; Shafran, R.; Layard, R.; Clark, D.M. Enhancing recovery rates: Lessons from year one of IAPT. Behav. Res. Ther. 2013 , 51 , 597–606. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Richards, D.A.; Borglin, G. Implementation of psychological therapies for anxiety and depression in routine practice: Two year prospective cohort study. J. Affect. Disord. 2011 , 133 , 51–60. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• von Brachel, R.; Hirschfeld, G.; Berner, A.; Willutzki, U.; Teismann, T.; Cwik, J.C.; Velten, J.; Schulte, D.; Margraf, J. Long-Term Effectiveness of Cognitive Behavioral Therapy in Routine Outpatient Care: A 5- to 20-Year Follow-Up Study. Psychother. Psychosom. 2019 , 88 , 225–235. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gaskell, C.; Simmonds-Buckley, M.; Kellett, S.; Stockton, C.; Somerville, E.; Rogerson, E.; Delgadillo, J. The Effectiveness of Psychological Interventions Delivered in Routine Practice: Systematic Review and Meta-analysis. Adm. Policy Ment. Health 2023 , 50 , 43–57. [ Google Scholar ] [ CrossRef ]
• Green, S.A.; Honeybourne, E.; Chalkley, S.R.; Poots, A.J.; Woodcock, T.; Price, G.; Bell, D.; Green, J. A retrospective observational analysis to identify patient and treatment-related predictors of outcomes in a community mental health programme. BMJ Open 2015 , 5 , e006103. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stochl, J.; Soneson, E.; Stuart, F.; Fritz, J.; Walsh, A.E.L.; Croudace, T.; Hodgekins, J.; Patel, U.; Russo, D.A.; Knight, C.; et al. Determinants of patient-reported outcome trajectories and symptomatic recovery in Improving Access to Psychological Therapies (IAPT) services. Psychol. Med. 2022 , 52 , 3231–3240. [ Google Scholar ] [ CrossRef ]
• Verbist, I.; Huey, D.; Bennett, H. Predicting treatment outcomes in improving access to psychological therapy (IAPT) services. Ment. Health Rev. J. 2022 , 27 , 114–131. [ Google Scholar ] [ CrossRef ]
• Barker, P.; Buchanan-Barker, P. Mental health in an age of celebrity: The courage to care. Med. Humanit. 2008 , 34 , 110–114. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cameron, D.; Kapur, R.; Campbell, P. Releasing the therapeutic potential of the psychiatric nurse: A human relations perspective of the nurse-patient relationship. J. Psychiatr. Ment. Health Nurs. 2005 , 12 , 64–74. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fisher, J.E. The use of psychological therapies by mental health nurses in Australia. J. Psychiatr. Ment. Health Nurs. 2014 , 21 , 264–270. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Health and Global Policy Institute. Recommendations on the Future Dissemination of CBT and Support Methods Based on CBT Concepts. Available online: https://hgpi.org/en/research/mh-activity1-1.html (accessed on 10 April 2024).
• Mathieson, F.; Collings, S.; Dowell, A.; Goodyear-Smith, F.; Stanley, J.; Hatcher, S. Collaborative research: A case example of dissemination of CBT in primary care. Cogn. Behav. Therap 2013 , 6 , e4. [ Google Scholar ] [ CrossRef ]

Share and Cite

Yoshinaga, N.; Obara, Y.; Kawano, N.; Kondo, K.; Hayashi, Y.; Nakai, M.; Takeda, R.; Tanoue, H. Real-World Effectiveness and Predictors of Nurse-Led Individual Cognitive Behavioral Therapy for Mental Disorders: An Updated Pragmatic Retrospective Cohort Study. Behav. Sci. 2024 , 14 , 604. https://doi.org/10.3390/bs14070604

Yoshinaga N, Obara Y, Kawano N, Kondo K, Hayashi Y, Nakai M, Takeda R, Tanoue H. Real-World Effectiveness and Predictors of Nurse-Led Individual Cognitive Behavioral Therapy for Mental Disorders: An Updated Pragmatic Retrospective Cohort Study. Behavioral Sciences . 2024; 14(7):604. https://doi.org/10.3390/bs14070604

Yoshinaga, Naoki, Yoko Obara, Naohisa Kawano, Kazuki Kondo, Yuta Hayashi, Michikazu Nakai, Ryuichiro Takeda, and Hiroki Tanoue. 2024. "Real-World Effectiveness and Predictors of Nurse-Led Individual Cognitive Behavioral Therapy for Mental Disorders: An Updated Pragmatic Retrospective Cohort Study" Behavioral Sciences 14, no. 7: 604. https://doi.org/10.3390/bs14070604

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50+ Research Topics for Psychology Papers

How to Find Psychology Research Topics for Your Student Paper

• Specific Branches of Psychology
• Topics Involving a Disorder or Type of Therapy
• Human Cognition
• Human Development
• Critique of Publications
• Famous Experiments
• Historical Figures
• Specific Careers
• Case Studies
• Literature Reviews
• Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

I can always tell when a student really cares about the topic they chose; it comes through in the writing. My advice is to choose a topic that genuinely interests you, so you’ll be more motivated to do thorough research.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

• Prejudice and discrimination (i.e., homophobia, sexism, racism)
• Social cognition
• Person perception
• Social control and cults
• Persuasion, propaganda, and marketing
• Attraction, romance, and love
• Nonverbal communication
• Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

• Eating disorders
• Borderline personality disorder
• Seasonal affective disorder
• Schizophrenia
• Antisocial personality disorder
• Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

• False memories
• Speech disorders
• Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

• Language acquisition
• Media violence and children
• Learning disabilities
• Gender roles
• Child abuse
• Prenatal development
• Parenting styles
• Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

• The Milgram Obedience Experiment
• The Stanford Prison Experiment
• The Little Albert Experiment
• Pavlov's Conditioning Experiments
• The Asch Conformity Experiment
• Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

• Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
• Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Understanding behavior to understand behavior change: a literature review

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• https://doi.org/10.1080/13504620802148881

Introduction

History of behaviorism and behavior theories, challenges to changing behavior: lessons for the field, acknowledgements.

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One view of environmental education suggests that its goal is to ‘develop a world population that … has the knowledge, skills, attitudes, motivations and commitment to work individually and collectively towards solutions of current problems and the prevention of new ones’ (UNESCO‐UNEP Citation 1976 ). Embedded within this charge is the teaching of skills and motivations to implement skills, where a skill refers to performance of an act acquired through extended practice and training (Ericsson and Oliver Citation 1995 ). However, it is often difficult to articulate clearly what skills we teach in conservation education and environmental education focusing on behavior change or influence. It can be equally challenging to describe the behaviors we are ultimately seeking, identified in the Tbilisi Declaration as ‘new patterns of behavior’ (UNESCO Citation 1978 ). At a basic level, it is important to explore the grounding for teaching toward behavior – often referred to as behavior change – that supports the work of the field. This literature review attempts to provide a foundation for behavior‐related discussions in environmental and conservation education. A number of the behavior theories, concepts and models discussed in this review have been explored extensively elsewhere; therefore, this review is not exhaustive, but rather is intended to be broadly representative of the literature.

• behavior theory
• behavior change
• conservation education

There are many debates about the purpose of education and these debates can be seen very clearly within the larger field of environmental education. Some argue that the ultimate purpose of education is to affect individuals’ behaviors and that conservation education, among other areas, specifically calls for behavioral change. Others contend that the primary role of education is to facilitate an individual’s intellectual capability and not to impose on individuals how they should live. To that end, environmental education represents a process for intellectual growth using environment as the context. Of course, there are extreme positions on this single polarity, numerous positions between, and other disagreements over the nature of the field that create similar tension. It is not the purpose of this article to settle this important debate and perhaps‐necessary polarity in environmental education. Rather, the purpose is to better inform the dialogue and practice of environmental education through a reflection on the complex field of behavioral psychology and how it can inform and has influenced the research and practice of environmental education in different settings.

The study of behavior within the field of psychology grew out of opposition to the initial view that psychology should only deal with internal mental processes. Early psychologists studied mental processes in an attempt to correlate thoughts and feeling with definite conditions of the brain (Wilson and Keil Citation 1999 ). In 1913, John B. Watson’s seminal paper Psychology as the Behaviorist Views It proposed that psychology should be based on studying visible behaviors rather than using introspection to understand non‐visible mental processes (Hineline Citation 1992 ). Watson encouraged a move away from the earlier methods and tenets of mentalism – a branch of psychology focusing on mental perception and thought processes rather than overt behavior (Roeckelein Citation 1998 ). The enduring mark of behaviorism is evident today in the notion that complex behaviors can be distilled into the basic elements of stimulus and response. In other words, behaviorism broadly defined, postulates that an individual develops all aspects of behavior through experiences related to the connection between environmental stimuli and responses to those stimuli (Tomporowski Citation 2003 ). Those responses may include cognitive elements, unobservable mental processes and choice in mediating the behavior of the individual.

Although there were earlier efforts in behaviorism, most notably the work of Ivan Pavlov (classical conditioning) and Edward Thorndike (instrumental learning), the field was truly defined by Watson’s work. After receiving the first Ph.D. in psychology in the US, Watson proposed a field of study based in the natural science traditions of logical positivism (Reber and Reber Citation 2001 ; Roeckelein Citation 1998 ; Squire Citation 1992 ). This epistemological frame led him to pursue the search for lawful relationships between behavior and the observable social and physical environment (Jensen Citation 2006 ). Such views were significant in breaking from the work of Freud, Jung and other psychologists who focused on psychotherapy. Initially, behaviorism studied only specific muscular and glandular responses. Though a classic behaviorist, Skinner expanded his area of study to focus on the effects acts have upon the environment (Epstein Citation 1991 ; Reber and Reber Citation 2001 ).

Looking at behavior’s effects on the environment led to the subfield of neo‐behaviorism, championed by Hull and Spence (Hineline Citation 1992 ). Their research focused on chains of mediating events that occur between an individual’s behavior and the environment. Discussions on neobehaviorism also include labels such as logical behaviorism, informal behaviorism (liberal stimulus‐response theory) and radical behaviorism – all of which support the general tenet that whatever cannot be observed and measured is not worth studying or, in the case of radical behaviorism, does not exist (Roeckelein Citation 1998 ; Suppes Citation 1975 ).

It is important to note that there are branches of behaviorism far broader than the classical behaviorist theory and even the neo‐behaviorist schools of thought, and there is a tremendous disservice to behaviorism when constrained to the narrow and simplistic philosophy of Skinner (Staddon Citation 2004 ). There are branches of behaviorism that operate on the assumption that not all behaviors are visible – there are cognitive and affective behaviors that are learned, practiced and perfectable. Neo‐cognitive behaviorism, for example, suggests that there are both conditioned (negative) and unconditioned (natural) levels of awareness and that there are factors that determine if an individual is acting at either of those levels of perception, affect and behavior at any given time (Kelley Citation 1993 ). Another example is Relational Frame Theory, which is a cognitive and behavioral analysis of complex human behavior (McIlvane Citation 2003 ). Tolman built an elaborate model of learning based on behaviorism that included internal mental processes affecting the stimulus response relationship such as purpose, expectation and cognition (Hineline Citation 1992 ). The various branches of behaviorism have led to tremendous innovations in creating observable and measurable elements from affective and cognitive processes.

Early in the twentieth century, James ( Citation 1912 ) suggested another approach to behavior, epitomized in his pragmatist doctrine of radical empiricism, which focuses on role of experience and the connections among experiences. James argued that experience is unified rather than dichotomized, rejecting the dominant turn‐of‐the‐century belief that the mind is separate from the world; he called the melding of these two ‘pure experience’ (Chemero Citation 2003 ). In rejecting the classic ‘stimulus‐response’ theory, which is built on the mind/world dichotomy, James ( Citation 1912 ) emphasizes the importance of experience and perception as mediators between the elements of stimulus and response.

Dewey ( Citation 1905 ) also rejected the dichotomies that separated mind from body, world and action, championing the role of lived experience in his adherence to ‘immediate empiricism’. This focus on experience, and the role prior experiences play in predicting and forming future behaviors, has influenced the field of environmental education as many of his ideas subsequently informed social behaviorism, which emphasizes the centrality of attitudes in relation to behavior (Garrison Citation 1995 ).

In a seminal 1896 paper, The Reflex Arc Concept in Psychology , Dewey argued that the stimulus‐response model of behavior was an oversimplification and based on a false dichotomy of mind/body (Dewey Citation 1896 ). The dominant belief of the day was that a stimulus would occur and the body, through a series of nerve channels, would respond (Rogers Citation 1962 ), but Dewey described a ‘reflex‐circuit,’ spurred by an organism seeking a stimulus (initiation) and then interpreting the stimulus, producing a response (Feffer Citation 1993 ; Rogers Citation 1962 ). In other words, organisms do not passively wait for a stimulus; rather, there is an initiation whereby organisms seek out a stimulus, producing a response, and interpretation occurs at a stage between the stimulus and the response (Dewey, Citation 1896 ). Thus, rather than forming an arc, with two discrete endpoints, behavior is more appropriately described in the form of a circuit. This concept, while quite interesting and relevant to the environmental education field, became entwined with the tenets of functional psychology, which buckled to the emerging behaviorist movement and thus has been obscured in psychology in general, and environmental psychology in particular (Ballantyne Citation 1996 ).

Using the broader notion of behaviorism, several concepts have entered the lexicon of psychology, motivation, education and the general vernacular. Most common are the ideas of stimulus and response, including conditional (controlled) and unconditional (random or not controlled) stimuli. This language is central to all behaviorist theories: stimulus to organism to response (S‐O‐R) is the basic structure upon which all behaviorism is built. Even cognitive behaviorism, which studies how the mind processes information leading to behaviors, looks at how memory leads to motor responses that are based on environmental inputs (Tomporowski Citation 2003 ). In other words, many behaviors can be performed without thought because those behaviors are learned within specific, relatively stable environments.

Avoidance learning emerged from the application of operant training procedures. In avoidance learning, somewhat predictable aversive elements fail to occur depending on whether a specified response occurs (Squire Citation 1992 ). Studies of active and passive contingency later led to the clinical tools of behavioral modification.

Latent inhibition, which is part of classical conditioning, is a concept familiar to environmental educators even if the phrase is not: when the individual is pre‐exposed or repeatedly exposed to the conditional stimulant (in this case, an environmental message), there is a retardation of the conditioning or a slowing of receiving the message (Pearce and Hall Citation 1992 ). Therefore, with each subsequent harping on environmental messages, we run the risk of desensitizing the audience to future environmental messages.

Another familiar concept in environmental education is second‐order conditioning. Second‐order conditioning occurs when one stimulus is paired with an unconditioned stimulus until the desired conditioned response is elicited. At that point, the second stimulus becomes paired with the first stimulus. The unconditioned response weakens or disappears over time if there is no continual reinforcement of the conditioned response. The lesson for environmental education is that desired behaviors need to be specifically targeted and paired with learning events. The more distant or removed the desired behavior is from the learning event, the more likely the message being conveyed will be lost over time (Pearce and Hall Citation 1992 ).

When teaching people about recycling, for example, some environmental educators might encourage them to recycle and teach them to use a recycling bin to separate plastic, paper and glass. The eventual desired outcome for that approach to teaching behavior is that the individual will recycle used items; however, this desired behavior risks breaking down if the person becomes reliant on the bin rather than committed to the recycling behavior. If that person is in a situation without the recycling bin, he or she may decide to throw recyclable items in the trash because the first‐order behavior (sorting items into bins) is absent.

Other concepts that have become common in education include overt behaviors – those that can be observed, and covert behaviors – those that are private (including thoughts and emotions). In addition, the study of behavioral antecedents and consequences (Spiegler and Guevremont Citation 2003 ) has led to a better understanding of how to sustain behaviors. One example is extinction, or the decaying of a response, which occurs when the stimulus is removed and reinforcement withheld. Another is the inhibitory situation that emerges when there is a negative correlation between conditioned and unconditioned stimuli, which confounds the conditioned or desired stimuli. Consequently, the behavioral response to the conditioned stimulus wanes. All of these concepts are often applied in education even though the psychological terms are not as common and educators may not be familiar with the basic research (Reber and Reber Citation 2001 ).

In classical behaviorism, behaviors are what can be seen. In environmental and conservation education, we use messaging, communication and educational strategies to instigate and encourage particular behaviors, many of which may be cognitive behaviors, emotions, attitudes and intentions. From this point forward, this paper will use the term ‘behavior’ in the broadest sense in order to incorporate important learnings from the wide array of behaviorist theories.

Regardless of the type of behavior or the framework used, many environmental educators often make the mistake of focusing specifically on the behavioral outcomes rather than the steps required to reach those outcomes. In doing so, it is possible to disregard a legitimate means of achieving the desired conservation state: Frick asserts that ‘[d]ifferent conservation behaviors have different conservation potentials’ (Frick, Kaiser, and Wilson Citation 2004 , 1599), although those different behaviors may achieve the same goal. McKenzie‐Mohr and Smith ( Citation 1999 ) explain that a behavior is a specific action, while ‘most environmental activities are made up of several discernible behaviors’ (cited in Monroe Citation 2003 , 115). Therefore, it is necessary to be able to parse the discernible behaviors in order to compare direct actions. To change behaviors, we must consider each of the individual behaviors and actions that add up to the larger environmental behavior we encourage people to undertake. In dissecting behaviors into habits, tasks and skills, opportunities exist for changing the larger behaviors (Monroe Citation 2003 ).

Hierarchy of behaviors

Organizing behaviors based on how conscious the individual is of each behavior provides a useful hierarchical structure. Certain behaviors – such as the muscle behaviors of breathing and digestion – are subcortal or done continually without conscious thought. These behaviors are also referred to as reflexive behaviors, which are mechanical responses that are usually species‐specific and show little variability (Reber and Reber Citation 2001 ). Other behaviors are intuitive , or based on natural reactions and instinctive response to stimuli, and are often neuro‐physiologically based (Santrock Citation 2000 ).

By contrast, conscious behaviors require explicit thought to complete. New skills require conscious effort, as do changes in settings or environment. Basically, at some point, all of our behaviors require a conscious decision and many can, in different situations, require conscious reflection. The classic example of this occurs when one awakens during the night in a strange hotel room and runs into furniture or walls. Even so, few of the behaviors people apply each day are conscious – most are post‐conscious, which means that they were once learned behaviors. Through repetition, those behaviors become embedded in daily routines and are performed without thinking.

People make countless decisions each day, making it impossible to be conscious of every behavior. However, few behaviors are isolated; most are embedded in patterns and routines that become automatic and fixed (Wittig and Belkin Citation 1990 ). These patterns and routines are organized serially and vary depending on an individual’s level of proficiency with performing each of the skills that comprise the behaviors (Annett Citation 1995 ). One common illustration is driving a car. This activity requires a driver to make hundreds of decisions every minute, but most of those decisions are routine , which means that the behaviors are serially organized and controlled while also taking into account unpredictable features such as an erratic driver or a deer on the road. If every behavioral choice required an individual’s undivided attention, we would become immobilized by the overwhelming number of decisions. Therefore, developing routines serves an important role, particularly as life becomes increasingly complex.

Routines are sequences of a series of habits; habits are learned acts. Originally, habits were defined only by motor patterns and physical responses, but the concept has now expanded to also include perceptual, cognitive and affective habits (Reber and Reber Citation 2001 ). Sutherland ( Citation 1996 ) describes habits as a persistent pattern of learned behaviors and the sequence of responses in a routine. Over time, habits and routines lead to a long‐term tendency to respond in a certain way (Wittig and Belkin Citation 1990 ), which is also called a default action. These types of well‐practiced behaviors recur because the processing that initiates and controls performance becomes automatic. When behaviors are not well‐learned or when they occur in unstable or unpredictable contexts, people must make conscious decisions to perform the desired behavior (Ouellette and Wood Citation 1998 ).

In essence, few behaviors are conscious and most are learned habits. Those habits – or isolated acts – are sequenced into routines that allow the individual to consciously apply thought when necessary. The challenge, then, for educators seeking behavior change is not to change the behavior, but rather to change the routine that exists around that behavior. In other words, changing behaviors is not about changing one act; it is about altering the routines in which the acts are embedded.

Many environmental activists strive to make conservation actions routine, default actions supported by social norms. In behavior theory, these types of behaviors are called causuistic . If most people looked askance at driving a car to work, for example, then walking, riding a bike, or using public transportation would be the causuistic behavior. Causuistic behaviors are often considered to be subconscious as they relate to societal – not individual – norms and values. Although some conservation education behaviors may appear to fit into the causuistic mode, it is important to consider the role of agency regarding an individual’s behavioral choices or decisions and the rich dialogue in the environmental education community around the challenges of identifying behavioral outcomes that are inviolate in all situations and for all individuals. Footnote 1

Similar to causuistic behaviors, but meaningful to conservation action based on historic approaches, are rule‐governed behaviors. Rule‐governed behaviors can be described by clearly articulated rules that arise from an authority figure or from a social construct. Ultimately long‐term in nature, rule‐governed behaviors start as short‐term, proximately reinforced behaviors: they are socially acceptable, the individual receives approval for applying the behavior, there is general acceptance of the behavior and they are rewarded with reinforcers (including money) (Baum Citation 2005 ). Rule‐governed behaviors can become causuistic when the reinforcers become societal and expected.

Although it may appear that environmental educators want conservation actions to become causuistic – or automatic and socially reinforced – environmental education also encourages critical thinking, which runs contrary to the subconscious aspect of causuistic behaviors. With critical thinking, we desire behaviors to be post‐conscious rather than subconscious. Footnote 2 This means that one’s actions should be conscious enough that individuals are able to identify a behavior that can or should change when situations or circumstances change. New technologies, for example, may render obsolete old ways of being environmentally appropriate; new materials sometimes supplant older, environmentally unfriendly materials; or a change in geographic location can affect what is considered environmentally appropriate. Fulcher’s taxonomy of behaviors ( cf. Wittig and Belkin Citation 1990 ) starts with impulse as being the base, default behavior and then moves to routine and casuistic behaviors. Moving beyond causuistic, thoughtful behavior includes the ability to change behaviors based on skills of transfer, knowledge and attitude.

Behaviors are rooted in skills, which cluster to become tasks (Norton Citation 1997 ). It is difficult to teach behavior, per se, as behaviors are complex combinations of skills, as discussed previously. The teaching of individual skills, while possible, involves teaching about the affect and cognition that support the behavior as well as the skill.

The affective and cognitive interplay in behavior

Behavior does not refer only to a physical activity, but rather represents a complex intermingling of affective and cognitive processes that guide decisions in the short‐ and long‐term. To fully understand the mechanisms behind behaviors and, by extension, to more effectively move people toward environmentally friendly behavior, it is critical to explore the interplay among the cognitive and affective components, which are nearly inseparable. Within this structure, attitudes both toward the environment and toward environmental behavior may be instrumental in predicting and influencing environmental behaviors, which makes environmental attitudes a frequently studied concept (Kaiser et al. Citation 1999 ). Knowledge about environmental behaviors is also complex: Frick et al. ( Citation 2004 ) note that behavioral cognition as it relates to environmental knowledge relies not only on system knowledge – or understanding the ecological issue – but also action‐related knowledge (what can be done) and effectiveness knowledge (comparative benefits of different actions).

The definition and formation of attitudes represent contested territories as differing opinions exist on what attitudes are and how attitudes are formed. Rosenburg and Hovland (1960) define attitudes as ‘predispositions to respond to some class of stimuli with certain classes of responses’ (p. 3) and recognize attitudes as having behavioral, affective and cognitive facets. Petty and Cacioppo ( Citation 1981 ) suggest that ‘the term attitude should be used to refer to a general and enduring positive or negative feeling about some person, object or issue’ (p. 7). They distinguish attitudes from beliefs by saying that beliefs are ‘reserved for the information that a person has about other people, objects, and issues’) (p. 7).

Although attitudes are inarguably an important consideration when addressing behavior, many findings are inconclusive, while others are contradictory. Little consensus exists on how, and to what extent, attitudes affect and can predict environmental behavior. In a meta‐analysis of more than 100 environmental‐behavior studies, Hines, Hungerford and Tomera ( Citation 1986 ) found moderate to substantial correlations (r = .49) between pro‐environmental attitudes and pro‐environmental behaviors. Footnote 3 However, more important than – but also strongly interactive with – attitudes were the cognitive aspects of knowledge of issues and knowledge of action strategies. The psychological constructs of locus of control and individual sense of responsibility are also important, as well as whether an individual communicates a willingness or intention to undertake a behavior (Kollmuss and Agyeman Citation 2002 ).

Delving more deeply into the apparent inconsistency among environmental attitudes and behaviors reveals several factors that alter the attitude‐behavior relationship: attitude specificity, normative influences and attitude accessibility (Bell et al. Citation 1996 ). With regard to attitude specificity, while generally positive attitudes toward the environment do not predict whether an individual will take specific environmental behaviors, specific attitudes toward particular problems do have predictive value (Bell et al. Citation 1996 ). A general pro‐environmental outlook, for example, does not ensure that a person will purchase a fuel‐efficient vehicle, but a specific concern with climate change may link with behaviors to mitigate that effect, including driving a vehicle that minimizes carbon dioxide emissions. Normative influences – or the social pressures around certain attitudes and behaviors – are important in several models that explore the attitude‐behavior interface, including Fishbein and Ajzen’s Theories of Reasoned Action and Planned Behavior ( Citation 1975 ). Finally, attitude accessibility, or the frequency with which an individual is given the opportunity to express and act upon an attitude, helps strengthen (or weaken) attitudes toward certain attitude objects. Consequently, individuals are more likely to take behaviors to protect or improve the object (in this case, the environment or a particular environmental concern) if the attitude is frequently reinforced and the association between the attitude and attitude object is strengthened (Bell et al. Citation 1996 ). Building on these constructs from Bell et al., environmental education approaches including service learning, action research and action learning may help reinforce attitudes and the subsequent relationship between those attitudes and the desired behaviors.

Despite the continuing debates about precisely how attitude links with environmental behavior, most studies suggest that attitudes do have some impact; therefore, it is important to explore and understand the mechanisms by which attitudes may be changed, particularly when pursuing behavior change. A number of theories exist about how attitude change occurs, and those theories are relevant to environmental education because of the potential connection to changed behaviors. Manstead ( Citation 1990 ) suggests that many theories agree on one to three basic pathways for changing attitudes, which are: (1) directly experiencing the attitude object, which in the case of environmental education may be a woodlot behind one’s home or an endangered species in a nearby creek; (2) persuasive communications, such as environmental education programs or social marketing designed to change attitudes on specific issues or behaviors; and (3) induced behavior change, which includes offering financial or other incentives.

Regarding the use of incentives, some researchers believe that attitudes follow behaviors and, therefore, if we use incentives to induce behavior, attitudes will follow. This occurs through the mechanism of cognitive dissonance, which develops when ‘a person has two contradictory cognitions, or beliefs, at the same time’ (Morris and Maisto Citation 2006 , 462). To resolve the dissonance – or to bring the thoughts, actions and feelings into alignment – an individual must either change his or her attitude or the behavior. As changing the attitude is easier than changing the behavior, the process of cognitive dissonance is one that is believed to change attitudes (Bell et al. Citation 1996 ; Morris and Maisto Citation 2006 ). To retain harmony between attitudes and behaviors, individuals are thought to sustain the behavior that expresses the underlying attitude. In the environmental education arena, this translates into pro‐environmental behaviors, such as recycling, linking with and reinforcing positive attitudes toward resource conservation and reuse.

Overall, research has consistently demonstrated that general pro‐environmental attitudes alone rarely lead to specific behavioral changes (Bell et al. Citation 1996 ; Monroe Citation 2003 ). The question arises, then, as to why so many environmental education programs continue to focus solely on general environmental literacy, which Disinger and Roth ( Citation 1992 ) define as the capacity to perceive and interpret the relative health of environmental systems and take appropriate actions to maintain, restore or protect these systems. In addition, these same programs tend to measure literacy solely on the basis of either attitude or cognition (Volk and McBeth Citation 1998 ). Bell et al. ( Citation 1996 ) postulate that ‘[o]ne attraction of attitude change is its potential for generalizability. That is, behavioral change would be efficient if we could change a few global attitudes, which might then promote a variety of responsible behaviors across a number of settings. If they worked, such broad programs would be more efficient than those … tailored to dozens of different situations’ (p. 536). However, we know that specific pro‐environmental attitudes based on specific relationships with the environment or an environmental issue, building on already‐developed self‐esteem and locus of control and requiring mastered, or master‐able, skills, are most effective in promoting behavior change. Therefore, focusing on developing skills that build on pro‐environmental attitudes is a critical step toward changing or reinforcing behavior as most effective behavior change efforts contain attitude arguments, educational information (cognition), behavioral skill arguments and behavioral skills training (Albarracin et al. Citation 2005 ).

By contrast, Kaiser et al. ( Citation 1999 ) posit that environmental attitude can be a ‘powerful predictor of ecological behavior’ and that earlier study findings were inconclusive because of omissions in structural models used to explore the attitude‐behavior link. They propose a fused model that includes environmental knowledge, environmental values and ecological behavior intentions as the foundation of attitudes, which they indicate can then be used to predict behavior somewhat reliably (Kaiser et al. Citation 1999 ).

Constellations: groups of behaviors

People often perform a variety of individual behaviors that are conceptually held together for each person as grouped, or constellations, of behaviors. Footnote 4 While some behaviors support each other and often occur concurrently, they are not necessarily indicators of other behaviors or consistent with an overall pattern of behavior that may be considered to be, for example, environmentally friendly or health‐conscious. Behaviors that environmental professionals see as logically fitting together, such as the suite of ‘environmentally responsible behaviors’ that are often cited, Footnote 5 may not necessarily mesh in someone else’s world view. Someone who is motivated by financial concerns might use compact fluorescent bulbs, turn off lights, and conserve water because these behaviors have a direct financial impact; they are money‐saving behaviors. However, that same individual may also buy conventionally grown produce, non‐recycled paper towels and toilet paper, mainstream cleaning products and other low‐cost items, motivated by the same financial concern. While these behaviors may initially appear inconsistent to environmental professionals who expect a constellation of environmental behaviors to occur simultaneously, looking more deeply at motivations, rather than simply the resulting behaviors, reveals that the inconsistency should not be at all surprising.

Another reason that constellations of behaviors that may be expected to occur together do not is that certain people may be motivated by issues , while others feel more empowered and are motivated to take action within particular settings . An individual who is interested in addressing climate change, for example, may choose to drive a hybrid car, minimize airplane travel, financially support groups that address climate change and install high‐efficiency home heating/cooling systems, among other actions. However, that same person may not be as concerned about toxic chemicals in the environment, so may not purchase organic produce or non‐toxic cleaning products. By contrast, someone who feels particularly empowered, or has a strong internal locus of control, within the realm of the household may take every action possible at that scale: they may purchase recycled paper products, use non‐toxic cleaning products, use compact fluorescent bulbs, recycle and conduct all of the behaviors that are deemed environmentally responsible and appropriate within a home setting. However, they may not feel that their actions outside the home are as powerful, or they may have other obligations or situations that do not make it easy to change existing behavioral patterns in that realm.

There are also certain identities that may make someone more likely to undertake a constellation of expected or rational‐seeming behaviors. Those identities may not necessarily indicate an interest in a particular issue or a commitment to a certain setting, but they may be consistent with one’s self‐identity as well as group or social identity. While an individual with an identity that says she or he is ‘an environmentalist’ may take certain behaviors consistent with that identity, if probed too deeply, inconsistencies in behaviors may become apparent as the rationale and motivation for the behavior may be related more to external appearances rather than internal motivations. Internal motivators, linked with deeply held values or comprehensive knowledge of an issue and the impacts of a particular action, can lead to dramatically different behavioral outcomes than external motivators, such as prestige or group belonging.

Various approaches have been used to incorporate elements of identities into potential clusters of behaviors. The Biodiveristy Project ( Citation 1999 ), for example, used a marketing approach to construct a taxonomy of individuals who are likely to be open to biodiversity messages and action. Jurin and Fortner ( Citation 2002 ) included elements of identity in their study of how students did or did not see themselves as ‘environmental’ and correlated their perceptions to behaviors and intents. While identities can be leveraged with regard to conservation behaviors, it must be recognized that behaviors motivated by external factors can lack reliability, particularly in the light of changing circumstances, new facts or other factors.

Teaching of skills

Skills are the building blocks to performing acts or tasks that seek to achieve a goal. Motor skills are those that require voluntary body or limb movement to be properly performed. As such, skills have purpose, are not reflexes and have specificity to accomplishing a particular task (Magill Citation 2000 ). An action differs from a skill or act in that multiple actions may achieve the same goal; however, a skill is specific. Multiple skills or acts are required to complete an action, and a ‘right’ or ‘better’ way of performing the act exists.

Skills, by definition, are learned and usually are purposefully taught (Annett Citation 1995 ). Skills are incremental, domain specific Footnote 6 and practicable, though practice must be meaningful and lead to success of the desired action or goal over time (Good and Brophy Citation 1990 ). Skills, however, are not the result of merely repeating an act. Learning a skill is a process resulting in a relatively consistent change in behavior based on experience (Tomporowski Citation 2003 ). This view of skill learning has three components: learning is based on behavior changes being consistent on different occasions and under different conditions; learning occurs within an individual and the behavior is the use of a skill or set of skills that demonstrate the learning; and learning occurs only with experience and practice . Skills are often categorized as motor skills, cognitive skills and emotional behavior.

Cognitive skills can be taught with varied emphasis on perceptual, cognitive and motor processes (Ericsson and Oliver Citation 1995 ). Ericsson and Oliver note that skills with cognitive components include skills in sports, surgery and the arts. Other psychologists believe cognitive skills are necessary for applications of all behaviors that require conscious thought. More commonly, the word skills invokes the concept of motor skills, of which there are three classifications. First, skills are classified by the precision of the movement – gross to fine motor skills. Second, skills can be defined by the beginning and end points where discrete skills are placed into a series to become serial motor skills. The third way of classifying skills is by the stability of the environment and leading from closed to open skills. A stable environment requires closed skills, or those in which the individual initiates the behavior. Changing or unpredictable environments require open skills as the individual must apply the skill based on what is happening at that moment and react to temporal circumstances (Magill Citation 2000 ).

Because behaviors are contextually based and represent a compilation of skills, they are not directly teachable, although skills are. Fletcher ( Citation 1934 ) noted that if education is to deal with learning in its broadest sense, then learning must take into account more than the laws of mental and physical habit formation. Learning skills requires attention, alertness, and preparation – including affective and cognitive preparation. Magill ( Citation 2000 ) suggests that perfecting a skill requires knowledge of the results, a transfer of learning, practice and motivation. Annett ( Citation 1995 ) notes that the definition of a skill includes achievability of the desired goal with economy of time and effort acquired by training and practice.

The typical steps involved in teaching skills include: (1) demonstration, (2) practice, (3) feedback and (4) corrective action (Nilson Citation 1991 ). Practice, which is the key to learning skills, occurs while the learner performs the skill and task as the educator observes and provides feedback. Practice and feedback steps are usually performed simultaneously as skills are developed through repeated attempts at performance under realistic conditions (Sisson Citation 2001 ). Graeff, Elder and Booth ( Citation 1993 ) have a slightly different skills‐training sequence, consisting of five steps: (1) instruction; (2) demonstration; (3) practice; (4) feedback and reinforcement; and (5) homework (referring to continued practice and efforts toward economy in performance).

Tomporowski ( Citation 2003 ) notes that there are a variety of factors that influence the learning of skills, including definition of the task; ability to practice the skill; success of prior experience with the skill; and context or subject, which are dependent upon the individual’s developmental, motivational and mental characteristics. As skills develop, complexity and rapidity are incorporated (Nilson Citation 1991 ). The ultimate goal is to transfer the skill by focusing on the elements of non‐specificity of the skill (Annett Citation 1995 ). To create sustainable behavior change through skills, ongoing feedback on skill‐specific items is necessary while a skill is being learned, as is continually demonstrating how those skills relate to specific behavioral goals (Wexley and Latham Citation 1991 ).

In essence, then, skills are learnable, practicable, and perfectable. Clusters of skills (or acts) become tasks that serve actions and, ultimately, enough actions achieve the goals. This leads back to the article’s initial observations: what do we mean by ‘behavior change’ with regard to environmental and conservation education, and how do we achieve it? Skills are teachable, routines serve to embed skills and habits in individuals’ life patterns and ways of doing things, and actions are goal‐focused. It is not possible to change behaviors by simply stating the need; rather, behavior change requires additional messaging, either through marketing or education.

Models of behavior change

Teaching skills must involve interrupting one routine of behavior and replacing old skills that occur within that routine with new skills. Those new skills must be embedded in either a modified or a new routine. Conservation behaviors are complex and embedded in a variety of routines and across situations and contexts, making them difficult to alter and transfer to other situations. For several decades, health education has studied how and whether different approaches to behavior change work. Through these studies, and some in other fields, a number of models have been developed to guide practitioners and academics.

Communication/persuasion model

The communication/persuasion model posits that communication can change attitudes and behaviors that are linked in the same causal chain (McGuire 1964). In this model, inputs include the source, the message itself, the channel, the recommended change or behavior and the destination. Outputs of the model are changes in specific cognition and observed behaviors (Graeff et al. Citation 1993 ). This model is widely used in communications and media studies. The greatest challenges to the model are ensuring the causal chain is maintained and the message is continued.

In environmental education, the communications/persuasion model is frequently used. We assume that inputs lead to the desired cognitive outcomes and thus to the desired behaviors. One of the challenges is that, as in other fields, causal chains are extremely difficult to determine given the many exposures and competing sources of information to environmental messages.

Diffusion of innovation

In 1962, Everett Rogers introduced the concept of diffusion of innovation ( Citation 2003 ). The theory purports that change spreads in a population through a normal distribution of willingness to accept new ideas. The labels for the distribution within a larger population include innovators (2.5% of the population), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%). At the individual level, behavioral adoption occurs through the stages of knowledge, persuasion, decision, implementation and confirmation (Rogers Citation 2003 ).

Diffusion theory has been widely applied and studied (Rogers Citation 1968 ; Rogers and Shoemaker Citation 1971 ; Rogers Citation 1972 ) and was popularized with the general public in Malcolm Gladwell’s 2000 bestselling book The Tipping Point: How Little Things Can Make a Big Difference . According to diffusion theory, behaviors are affected across a community through change agents. Geller et al. ( Citation 1990 ) identified four elements that would affect a change agent’s own behavior while diffusing innovation: involvement, social support, response information and intrinsic control.

In environmental education, the diffusion model is useful as it stresses that each of the four types of distribution groups requires different motivations to adopt a behavior. Those motivations are influenced by social context, social processes and social support (Frank, Zhao, and Borman Citation 2004 ) and are most marked between the categories of early adopters and the early majority (Woodell and Garofoli 2003).

Health Belief Model

Beliefs help shape behavior. While enduring, beliefs are not fixed individual characteristics, but rather are acquired through primary socialization (Sheeran and Abraham Citation 1996 ). The Health Belief Model focuses on two aspects of an individual’s views of health and behavior: threat perception and behavioral evaluation (Janz and Becker Citation 1984 ). Threat perception – or perceived risk appraisal – is based on one’s perceived susceptibility to illness and the anticipated severity of the consequences of such an illness. The Health Belief Model suggests that, as an individual’s assessed level of risk increases, the likelihood the individual will adopt recommended prevention behaviors increases (Mattson Citation 1999 ). Behavioral evaluation, also called coping appraisal (Zak‐Place and Stern Citation 2004 ), relates to the belief that an available course of action will be beneficial and the anticipated barriers or costs of taking action do not outweigh the benefits (Rosenstock Citation 1990 ).

In addition to these four core components of the model, there are demographic, socio‐psychological and structural variables, as well as ‘cues to action’ (Mattson Citation 1999 ). Winfield and Whaley ( Citation 2002 ) describe cues to action as the stimuli necessary to initiate or trigger engagement in the desired, healthy actions. Cues include, for example, media campaigns or the illness of a family member or close friend. The Health Belief Model has been widely used in health education to predict behavior change. Various tools have been developed around the model (cf, Scandell and Wlazelek Citation 2002 ; Winfield and Whaley Citation 2002 ; Wallace Citation 2002 ) and research continues to reveal validity in the model.

Over time, much of the work of environmental and conservation education has been framed to address the four core components of the Health Belief Model. The concepts of issue relevance and cues to action, for example, are prevalent in the Guidelines for Excellence of the National Project for Excellence in Environmental Education (North American Association for Environment Education Citation 2004 ). Making the concepts explicit and focusing on the secondary variables may benefit environmental education efforts for conservation action.

Integrated Model of Behavioral Prediction

The Integrated Model of Behavioral Prediction is based on previously described behavioral theories (Kasprzyk, Montano and Fishbein Citation 1998 ). The integrated model incorporates elements of the Theory of Reasoned Action, Theory of Planned Behavior, Social Cognitive Theory, Theory of Interpersonal Relations and Subjective Culture, the Information‐Motivation‐Behavioral Skills Model and the Health Belief Model (Danter Citation 2005 ). The Integrated Model illuminates three core concepts shared across these theories: perceptions about outcomes of performing the behavior, the social support for the behavior and the effect of the environment or the situation on behavior performance (Kasprzk et al. Citation 1998 ).

While the Integrated Model has undergone a series of adaptations, it generally includes external variables as well as behavioral, normative and efficacy beliefs related to attitudes and social norms, which support intentions. Intentions are modified by skills and environmental factors to lead to the outcome behavior (Fishbein Citation 2000 ). Past behavior, intervention or media exposure, and abilities have been added to the model (Fishbein et al. Citation 2003 ). Danter ( Citation 2005 ) notes that the integrated model’s strength is derived from the fact that the model’s variables change according to the behavior in question in combination with the specific population being targeted. Like the Theory of Reasoned Action and the Theory of Planned Behavior, the Integrated Model is valuable for studying behavior on a theoretical level, but its complexity makes it difficult to implement.

Locus of control

The psychological construct of locus of control predicts that an individual’s behavior is guided by his or her perception that a certain behavior will lead to an expected reinforcement (Rotter Citation 1954 , Citation 1966 ). Lever, Pinol and Urlade ( Citation 2005 ) explain that, from the individual’s perspective, locus of control is the motivating force that leads the individual to act in a particular manner. The outcomes of the selected actions will be determined either as a consequence of behavior (internal) or as a result of circumstances unrelated to actions (external).

The locus of control concept has been widely discussed and leveraged within the environmental education arena. Footnote 7 Locus of control is based on internal versus external control, referring to the degree to which an individual believes that a desired outcome can be achieved through one’s own behavior or personal characteristics. If the desired outcome occurs, that outcome serves as a reinforcement of the belief in one’s internal efficacy.

Locus of control also considers the degree to which individuals expect that reinforcement or outcome is a matter of chance, luck or fate; under the control of (powerful) others; or simply unpredictable (Rotter Citation 1990 ). An important construct of this theory is that of efficacy – a person’s belief that he or she possess the competency to perform the behaviors necessary to achieve the desired outcome (Meier Citation 1991 ). Self‐efficacy as described by Bandura ( Citation 1977 ) posits that a person’s expectations related to his or her efficacy beliefs influences whether that person undertakes a new behavior and, if so, how likely it is that the behavior will be maintained. A variety of studies have shown that income, prior levels of achievement and educational level are often predictors of locus of control, and health locus of control is one of the most widely researched constructs related to prediction of health behavior (Norman and Bennett Citation 1996 ).

Some environmental education studies have recommended a focus on helping learners develop internal loci of control (e.g. Riechard and Peterson Citation 1998 ; Hwan, Kimand, and Jeng Citation 2000 ; Yerkes and Beiederman Citation 2003 ). One of the challenges with targeting locus of control is that accurate measurement of locus of control related to a specific behavior must be created for each individual situation or behavior. Footnote 8

Responsible environmental behavior

Hungerford and Volk ( Citation 1990 ) challenged the myth that knowledge or affect alone can lead to behavior change (as presented in Ramsey and Rickson Citation 1977 ). As a result of the Hungerford and Volk critiques, environmental education researchers were spurred to explore alternative models leading to responsible environmental behavior. One particularly provocative and thoughtful model was created by Hines. The Hines Model, based on behavior‐change and environmental education literature, focuses on additional conditions including personality factors, knowledge of issues and possession of skills for taking action. All of these elements combine in an intention to act, but the ultimate behavior is mediated by situational factors (Hungerford and Volk Citation 1990 ).

Hines’s work, along with studies by Hungerford, Volk, Tomera and others, sparked a tremendous interest in researching factors leading to environmentally responsible behaviors. Footnote 9 Thanks to these seminal efforts, discussions of responsible environmental behavior have become part of the fabric of the environmental education field. Ultimately, research into responsible environmental behavior suggests that environmental citizenship behavior is based on three levels of variables: (1) entry level, including sensitivity, ecological knowledge, androgyny and attitudes; (2) ownership, including knowledge of issues, personal investment, knowledge of consequences and commitment; and (3) empowerment, including environmental action skills, locus of control and intention to act (Hungerford, Citation 1996 ).

Social learning

The concept of self‐efficacy is included in many models of behavior change. Self‐efficacy influences whether behaviors are initiated and the level of effort necessary to maintain the behavior (Bandura Citation 1977 ). Because much of self‐efficacy is learned through social contexts, the theory of social learning explores the development of that sense of efficacy. Social learning addresses real‐life problems, takes place in communities and occurs within a specific context that is not necessarily institutional (Zepke Citation 2005 ). The social‐learning literature suggests that individuals engaged in the learning process become a learning community that is partially based on both cooperative and collaborative learning endeavors (Leach and Knight Citation 2003 ). Social learning is therefore ‘situated’ in the community and the context.

Situated learning is grounded in everyday situations, recognizing that knowledge is context‐bound and not usually generalizable. Organized learning as a social process within learning communities – or communities of practice – must lead to action (Lave and Wenger Citation 1991 ). Bandura, in describing situated cognition, notes that the level and strength of self‐efficacy is altered whenever psychological procedures (including education) are applied (Bandura Citation 1977 ). He describes how cognitive skills and self‐control are used through moral justifications and devices to rationalize behaviors (Bandura Citation 1978 ).

Social learning theories hold important implications for environmental education as they suggest that behaviors are learned from others in the situated context in which the behaviors can be used. Some environmental educators believe effective change will result from increasing locus of control, which leads to greater feelings of self‐efficacy; social learning suggests we do so considering more fully the community of practice in which the behavior will be used.

Social marketing

Social marketing affects social change by applying commercial marketing techniques and analysis, along with elements of behavioral psychology, to social issues. Andreasen ( Citation 1994 ) defines social marketing as the ‘adaptation of commercial marketing technologies to programs designed to influence the voluntary behavior of target audiences to improve their personal welfare and that of the society of which they are a part’ (p. 110). Rather than encouraging a general pro‐environmental attitude or overall literacy, social marketing targets particular behaviors to change or reinforce by creating optimal conditions for action. Social marketing has been widely applied, particularly in the field of community health education. Extensive research on the impacts of social marketing both have included programs focused on topics such as: reduced drinking in college groups (Granfield Citation 2002 ; Glider et al. Citation 2001 ; Thombs et al. Citation 1996 ), family involvement in education (Sensiper Citation 1999 ), teen driving safety (Smith Citation 2006 ), use of female condoms (Artz et al. Citation 2005 ) and use of bicycle helmets (Ludwig, Buchholz, and Clarke Citation 2005 ), among others.

Social marketing follows a process similar to commercial marketing: target audiences are defined, barriers are identified and programs are designed to reach the target audience by using specific ‘behavior‐change tools’ (McKenzie‐Mohr and Smith Citation 1999 ). Communications with the target audience include information on the issue and behavior, the consequences of the behavior and the benefits of taking the behavior (Monroe Citation 2003 ). McKenzie‐Mohr, who coined the term community‐based social marketing, also emphasizes the importance of piloting projects and evaluating the results in order to revise initiatives as necessary to achieve the most powerful behavior‐change result, again focused on one specific behavior.

Some environmental educators, such as zoo educators in the US, have embraced social marketing, seeing opportunities for combining social‐marketing strategies for short‐term, specific behavior changes with environmental education strategies for longer‐term, more general attitudinal and behavioral outcomes. Monroe describes this dichotomy and partnership as the ‘two avenues for encouraging conservation behaviors’, with social marketing representing the ‘specific route of changing behavior’ and education following the ‘general route of cultivating environmental literacy’ (Monroe Citation 2003 , p. 113).

Theory of Reasoned Action

The Theory of Reasoned Action is one of the dominant behavior models used in environmental education. The Theory of Reasoned Action assumes that human behavior is grounded in rational thought, and the model uses the Principle of Compatibility, which predicts that attitudes reflect behavior only to the extent that the two refer to the same valued, outcome state of being (evaluative disposition) (Ajzen and Fishbein Citation 1980 ). Behaviors include the action, the target, the context and the time frame (Danter Citation 2005 ). A change in any of those four elements will alter the behavior in question (Fishbein et al. Citation 2001 ).

The Theory of Planned Behavior (Ajzen Citation 1991 ), which grew out of the Theory of Reasoned Action, suggests that human behavior is influenced by three belief constructs: beliefs about consequences, expectations of important others and things that may support or prevent the behavior (Ajzen Citation 2002 ). Following a meta‐analysis of research using the Theory of Planned Behavior, Staats ( Citation 2003 ) noted that a strong premise of the theory is that, at the conceptual level, links among influences on behavior and their effect are captured through one of the components of the model or relationships in the model.

The Theory of Reasoned Action and the Theory of Planned Behavior form the base of many environmental education studies about behavior adoption. The adaptive ability of the model to reflect any changes in context, environment and content proves both useful to the validity, while also cumbersome to the general applicability of the model (Danter Citation 2005 ).

Transtheoretical or Stages‐of‐Change Model

The Transtheoretical Model of behavior change suggests that there are stages of change and that change can be explained not through a particular theory, but through multiple theories. The most widely cited Transtheoretical Model is that of Prochaska ( Citation 1979 ), which includes five stages: pre‐contemplation, contemplation, preparation, action and maintenance of a behavior. These stages describe levels of motivational readiness to actively pursue behavior changes or new behaviors, and the stages occur along a continuum. The two key predictors of transitions between stages are self‐efficacy and the decisional balance, or the pros and cons associated with a particular behavior (Armitage et al. Citation 2004 ).

The Transtheoretical Model also describes processes of change, which are ten strategies people may use to progress from one stage to another or to prevent regression to an earlier stage (Prochaska et al. Citation 1994 ). Those strategies include: consciousness‐raising, social liberation, emotional arousal, self‐reevaluation, commitment and helping relationships, among others (Prochaska et al. Citation 1994 ). Various studies using the Transtheoretical Model examine the relationships among stages of change, demographic variables, self‐efficacy, decisional balance and processes of change in order to better understand how to intervene to facilitate change within a larger population (e.g. Cardinal and Kosma Citation 2004 ; Cardinal, Tuominen, and Rintala Citation 2004 ; Omar‐Fauzee Citation 2002 ).

Understanding how and why behaviors occur is perhaps the greatest barrier to affecting behavioral outcomes in educational programs. Human behavior and motivation are enormously complex, which can make their study overwhelming. From the structure of behavioral beliefs held by an individual (Ajzen Citation 2002 ) to the now well‐accepted constructs of barriers (Rosenstock Citation 1966 ), facilitating conditions (Triandis Citation 1977 ), and self‐efficacy (Bandura Citation 1977 ), the multiplicity of internal and external conditions affecting an individual’s choice to perform an act are tremendous. Even so, research in environmental education and other fields suggests general lessons that can be applied to encourage conservation‐related actions.

First, it must be explicitly noted that people act in ways that are usually consistent with how they express their values, beliefs, understandings, culture, socialization, enculturation, upbringing and training. Behaviors are not static, and people are continually adapting behaviors for myriad reasons. Indeed, even on environmental issues, people constantly take action and behave in certain ways – although those ways may not always reflect the most appropriate or effective environmental choices (Clover Citation 2002 ). In addressing environmental behaviors, it is important that environmental educators understand individual motivations and differences in behavior rather than assuming a single, ‘right’ or even ‘best’ behavior (Heimlich and Harako Citation 1994 ).

The behavior‐change research focuses on causality – what drives an individual to adapt, adopt or assume a behavior? Most research asks the causal question: What can an educator or communicator do to change the learner’s or target audience’s behavior(s), regardless of the type of behavior being taught?

Clearly, one of the greatest challenges to affecting behaviors is the consistency of educational and marketing messages. Various studies that examine increased participation in an array of actions have found that participation can sometimes be increased, at least temporarily, through the use of explicit goals or incentives. Footnote 10 Stern and Oskamp ( Citation 1987 ) reported that such techniques typically motivate between 10 and 15% of the people eligible or targeted for the behavior. It is also fairly well known that enthusiasm for a new behavior or action tends to wane and participation decays in the absence of continual reinforcement. In one meta‐study, 31 experiments were reviewed and the findings indicated that, while short‐term outcomes were promising, very few of the behaviors demonstrated response maintenance after messages were discontinued (Porter, Leeming, and Dwyer Citation 1995 ).

Continuing promise is in the research on commitment, which tends to predict greater likelihood of action. Early work in this area was done by psychologists focusing on achievement motivation and the cognitive and conative aspects of commitment (Cobern et al. Citation 1995 ). Danter ( Citation 2005 ) found that commitment of action by teachers at the end of a workshop had a tremendously high level of prediction of actual implementation of ideas from the training. Cobern et al. ( Citation 1995 ) conducted a study comparing two types of commitment strategies. The study found that individuals with stronger commitments (i.e. commitments to more than one action) maintained the behaviors at a statistically significant level, even a year after the study period. Those individuals also successfully recruited other people to serve as agents of change.

Research reveals that education and marketing campaigns are relatively effective in changing or adopting simple behaviors that require little confidence or skill. However, behaviors that demand lifestyle and habit changes require a greater sense of self‐efficacy and are more complex (Winfield and Whaley Citation 2002 ). Some of the environmental education research continues to explore models for behavior change that lead to predicable changes (Kollmuss and Agyeman Citation 2002 ) and are layered with assumptions of the educator being able to manipulate variables. Other research approaches behavior from the perspective of individual obstacles to achieving behaviors that are both personally and environmentally beneficial and include multiple ways of knowing Footnote 11 as part of the equation (Clover Citation 2002 ).

If environmental education is to produce a citizenry capable of making sound decisions and acting on those decisions in a way that is environmentally and personally sustainable, it is imperative that the field avoids unilateral assumptions. It is necessary to understand that, related to behaviors, individuals are not all alike; they are not motivated by the same things nor are they equally capable of altering routines. People may act in ways we believe are not environmentally appropriate and yet believe themselves to be committed to the environment. Nearly 20 years after Hungerford and Volk ( Citation 1990 ) challenged the 1970s belief of linear causality of affect and knowledge leading to behavior, the field continues to struggle with the perception that telling someone to behave in a certain way and providing sound reasoning to support that command equals teaching behavior . It is our hope that grounding our practice in decades of research related to behavioral theories will propel the field forward and ultimately lead to better learning through educational efforts.

This article grew out of research funded by the EPA’s Environmental Education and Training Partnership (EETAP) and administered through the National Audubon Society.

Notes on contributors

Joe E. Heimlich is Specialist, Environmental Science OSU Extension and Associate Professor in the School of Environment and Natural Resources at OSU. He is also a Senior Research Associate with the Institute for Learning Innovation. His work focuses on environmental learning in free‐choice settings, especially in adults.

Nicole M. Ardoin is an assistant professor of environmental education at Stanford University. Nicole is currently completing her Ph.D. in Social Ecology at the Yale School of Forestry & Environmental Studies, where her research focuses on sense of place and environmental behavior at an ecoregional scale. Her background working for various environmental organizations has led to an interest in motivations for conservation‐related behaviors.

1. For a further discussion, see Robottom and Hart ( Citation 1993 and Citation 1995 ).

2. Post‐conscious behaviors are those that were once learned but have now become routine. They can be easily retrieved to the conscious level. Subconscious behaviors, by contrast, arise from the deepest level of consciousness and occur without active thought.

3. Care should be taken in interpreting these numbers as they are reported as descriptive correlationals and not variance.

4. The section on ‘behavior constellations’ was inspired and informed by a February 2007 discussion with Na’ilah Nasir from the Stanford School of Education.

5. Widely cited environmentally responsible behaviors include recycling, turning off lights, conserving water, riding the bus, walking or biking to work, using reusable shopping bags, among others.)

6. Skills are considered domain specific because, although the muscle movement may be transferable, the specific skill is unique to the task. For example, a move en pointe is unique to ballet; dribbling a basketball is unique to that sport; and cursive writing of the alphabet is unique to the physical act of writing.

7. See Smith‐Sebasto ( Citation 1992 ) for a summary of the literature chronicling the locus‐of‐control concept.

8. Smith‐Sebasto worked with Fortner ( Citation 1994 ) to create a generalizable measurement tool that addresses locus of control toward environmental actions.

9. Various factors related to responsible environmental behavior have been studied by Hsu and Roth ( Citation 1999 ), Marcinkowski ( Citation 1989 ), Ramsey ( Citation 1989 ), Sia, Hungerford and Tomera ( Citation 1985 , Citation 1986 ) and Sivek ( Citation 1989 ) among others.

10. See Nickerson ( Citation 2003 ) for a review of this research related to recycling.

11. Ways of knowing assumes that there are multiple, inherent strategies individuals and cultures use to create meaning. As conservation is culturally embedded, there are different ways in which people come to know what they believe is true.

Related Research Data

• Ajzen, I., 2002. "Attitudes". In: Fernandez Ballesteros, R., ed. Encyclopedia of psychological assessment . Vol. 1. London: Sage; 2002. pp. 110–115.   Google Scholar
• Ajzen, I., 1991. The theory of planned behavior, Organizational Behavior and Human Decision Processes 50 (1991), pp. 179–211.   Web of Science ® Google Scholar
• Ajzen, I., and Fishbein, M., 1980. Understanding attitudes and predicting social behavior . Englewood Cliffs, NJ: Prentice‐Hall; 1980.   Google Scholar
• Albarracin, D., Zanna, M.P., Johnson, B.T., and Kumkale, G.T., 2005. "Attitudes: Introduction and scope". In: Albarraci, D., Johnson, B.T., and Zanna, M.P., eds. The Handbook of Attitudes . NJ: Lawerence Erlbaum Associates Publishers; 2005. pp. 3–19.   Google Scholar
• Andreasen, A.R., 1994. Social marketing: Its definition and domain, Journal of Public Policy and Marketing 13 (1) (1994), pp. 108–114.   Web of Science ® Google Scholar
• Annett, J., 1995. "Motor skills". In: Mackintosh, N.J., and Colman, A.M., eds. Learning and Skill . London: Longman; 1995. pp. 56–75.   Google Scholar
• Armitage, C.J., Sheeran, P., Conner, M., and Arden, M.A., 2004. Stages of change or changes of stage? Predicting transitions in Transtheoretical Model stages in relation to healthy food choice, Journal of Consulting and Clinical Psychology 72 (3) (2004), pp. 491–499.   PubMed Web of Science ® Google Scholar
• Artz, L., Macaluso, M., Kelaghan, J., Austin, H., Fleenor, M., Robey, L., Hook, E.W., and Brill, I., 2005. An intervention to promote the female condom to sexually transmitted disease clinic patients, Behavior Modification 29 (2) (2005), pp. 318–369.   PubMed Web of Science ® Google Scholar
• Ballantyne, P.F., 1996. Dewey’s muffled call for a larger unit of psychological analysis . Presented at Paper presented at the American Psychological Association meeting. Toronto, Canada, .   Google Scholar
• Bandura, A., 1978. "Reflections on self‐efficacy". In: Rachman, S., ed. Advances in Behavior Research and Therapy . Oxford: Pergamon; 1978. pp. 237–269, Volume One.   Google Scholar
• Bandura, A., 1977. Self‐efficacy: Toward a unifying theory of behavior change, Psychological Review 84 (2) (1977), pp. 191–215.   PubMed Web of Science ® Google Scholar
• Baum, W.M., 2005. Understanding behaviorism: Behavior, culture, and education . Holden, MA: Blackwell; 2005.   Google Scholar
• Bell, P.A., Greene, T.C., Fisher, J., and Baum, A., 1996. Environmental psychology . Fort Worth, TX: Harcourt Brace; 1996.   Google Scholar
• The Biodiversity Project, 1999. Life. Nature. The public. Making the connection . Madison, WI: Author; 1999.   Google Scholar
• Cardinal, B.J., and Ksoma, M., 2004. Self‐efficacy and the stages and processes of change associated with adopting and maintaining muscular fitness‐promoting behaviors, Research Quarterly for Exercise and Sport 75 (2) (2004), pp. 186–197.   PubMed Web of Science ® Google Scholar
• Cardinal, B.J., Tuominen, K.J., and Rintala, P., 2004. Cross‐cultural comparison of American and Finnish college students’ exercise behavior using Transtheoretical Model constructs, Research Quarterly for Exercise and Sport 75 (1) (2004), pp. 92–102.   PubMed Web of Science ® Google Scholar
• Chemero, A., 2003. An outline of a theory of affordances, Ecological Psychology 15 (2) (2003), pp. 181–195.   Web of Science ® Google Scholar
• Clover, D., 2002. Traversing the gap: Conceientizacion, educative‐activism in environmental adult education, Environmental Education Research 8 (3) (2002), pp. 315–323.   Google Scholar
• Cobern, M.K., Porter, B.E., Leeming, F.C., and Dwyer, W.O., 1995. The effect of commitment on adoption and diffusion of grass cycling, Environment and Behavior 27 (2) (1995), pp. 213–232.   Web of Science ® Google Scholar
• Danter, E.H., 2005. The Intention‐Behavior Gap: To what degree does Fishbein’s Integrated Model of Behavioral Prediction predict whether teachers implement material learned in a professional development workshop? . The Ohio State University; 2005, Unpublished Doctoral Dissertation.   Google Scholar
• Dewey, J., 1905. The postulate of immediate empiricism, Journal of Philosophy, Psychology and Scientific Methods 2 (15) (1905), pp. 393–399.   Google Scholar
• Dewey, J., 1896. The reflex arc concept in psychology, Psychological Review 3 (1896), pp. 357–370.   Google Scholar
• Disinger, J.F., and Roth, C.F., 1992. Environmental literacy . Columbus, OH: Clearinghouse for Science, Mathematics, and Environmental Education; 1992, ERIC Digest 351201.   Google Scholar
• Epstein, R., 1991. Skinner, creativity, and the problem of spontaneous behavior, Psychological Science 2 (6) (1991), pp. 362–370.   Web of Science ® Google Scholar
• Ericsson, K.A., and Oliver, W.L., 1995. "Cognitive skills". In: Mackintosh, N.J., and Colman, A.M., eds. Learning and Skill . London: Longman; 1995. pp. 37–55.   Google Scholar
• Feffer, A., 1993. The Chicago pragmatists and American progressivism . Ithaca, NY: Cornell University Press; 1993.   Google Scholar
• Fishbein, M., 2000. The role of theory in HIV prevention, AIDS Care 12 (3) (2000), pp. 273–278.   PubMed Web of Science ® Google Scholar
• Fishbein, M., Triandis, H.C., Kanfer, F.H., Becker, M., Middlestadt, S.E., and Eichler, A., 2001. "Factors influencing behavior and behavior change". In: Baum, A., Revenson, T.A., and Singer, J.E., eds. Handbook of Health Psychology . Mahwah, NJ: Lawrence Erlbaum; 2001. pp. 3–16.   Google Scholar
• Fishbein, M., and Ajzen, I., 1975. Belief, attitude, intention, and behavior: An introduction to theory and research . Reading, MA: Addison‐Wesley; 1975.   Google Scholar
• Fishbein, M., Hennessy, M., Yzer, M., and Douglas, J., 2003. Can we explain why some people do and some people do not act on their intentions?, Psychology, Health and Medicine 8 (1) (2003), pp. 3–18.   PubMed Google Scholar
• Fletcher, J.M., 1934. The role of feelings and emotion in education . Garden City, NY: Doubleday, Doran and Company; 1934.   Google Scholar
• Frank, K.A., Zhao, Y., and Borman, K., 2004. Social capital and the diffusion of innovations within organizations: The case of computer technology in schools, Sociology of Education 77 (2004), pp. 148–171.   Web of Science ® Google Scholar
• Frick, J., Kaiser, F.G., and Wilson, M., 2004. Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample, Personality and Individual Differences 37 (8) (2004), pp. 1597–1613.   Web of Science ® Google Scholar
• Garrison, J., 1995. Deweyan pragmatism and the epistemology of contemporary social constructivism, American Educational Research Journal 32 (4) (1995), pp. 716–740.   Web of Science ® Google Scholar
• Geller, E.S., Berry, T.D., Ludwig, T.D., Evans, R.E., Gilmore, M.R., and Clarke, S.W., 1990. A conceptual framework for developing and evaluating behavior change interventions for injury control, Health Education Research: Theory and Practice 5 (1990), pp. 125–137.   Google Scholar
• Glider, P., Midyett, S.J., Mills‐Novoa, B., Johannessen, K., and Collins, C., 2001. Challenging the collegiate right of passage: A campus‐wide social marketing campaign to reduce binge drinking, Journal of Drug Education 31 (2) (2001), pp. 207–220.   PubMed Web of Science ® Google Scholar
• Good, T.L., and Brophy, J.E., 1990. Educational psychology: A realistic approach . New York: Longman; 1990.   Google Scholar
• 1993. Graeff, J.A., Elder, J.P., and Booth, E.M., eds. Communication for health and behavior change: A developing country perspective . San Francisco: Jossey‐Bass; 1993.   Google Scholar
• Granfield, R., 2002. Believe it or not: Examining the emergence of new drinking norms in college, Journal of Alcohol and Drug Education 47 (2) (2002), pp. 18–31.   Google Scholar
• Heimlich, J.E., and Harako, A., 1994. Teacher values in teaching recycling, Environmental Education and Information 13 (1) (1994), pp. 21–30.   Google Scholar
• Hineline, P.N., 1992. A self‐interpretive behavior analysis, American Psychologist 47 (11) (1992), pp. 1274–1286.   PubMed Web of Science ® Google Scholar
• Hines, J.M., Hungerford, H.R., and Tomera, A.N., 1986. Analysis and synthesis of research on responsible environmental behavior: A meta‐analysis, Journal of Environmental Education 18 (2) (1986), pp. 1–8.   Google Scholar
• Hsu, S‐J., and Roth, R.E., 1999. Predicting Taiwanese secondary teachers’ responsible environmental behavior through environmental literacy variables, Journal of Environmental Education 30 (4) (1999), pp. 11–18.   Google Scholar
• Hungerford, H.R., 1996. The development of responsible environmental citizenship: A critical challenge, Journal of Interpretation Research 1 (1) (1996), pp. 25–37.   Google Scholar
• Hungerford, H.R., and Volk, T.L., 1990. Changing learner behavior through environmental education, Journal of Environmental Education 21 (3) (1990), pp. 8–21.   Google Scholar
• Hwang, Y‐H., Kim, S‐I., and Jeng, J‐M., 2000. Examining the causal relationships among selected antecedents of responsible environmental behavior, Journal of Environmental Education 31 (4) (2000), pp. 19–25.   Google Scholar
• James, W., 1912. Radical empiricism . New York: Longmans: Green & Co; 1912.   Google Scholar
• Janz, N.K., and Becker, M.H., 1984. The health belief model: A decade later, Health Education Quarterly 11 (1) (1984), pp. 1–47.   PubMed Google Scholar
• Jensen, R., 2006. Behaviorism, latent learning, and cognitive maps: Needed revisions in introductory psychology textbooks, Behavior Analyst 29 (2) (2006), pp. 187–209.   PubMed Web of Science ® Google Scholar
• Jurin, R.R., and Fortner, R.W., 2002. Symbolic beliefs as barriers to environmentally responsible behavior, Environmental Education Research 8 (4) (2002), pp. 373–394.   Google Scholar
• Kaiser, F.G., Wolfing, S., and Fuhrer, U., 1999. Environmental attitude and ecological behavior, Journal of Environmental Psychology 19 (1999), pp. 1–19.   Web of Science ® Google Scholar
• Kaspryzk, D., Montano, D.E., and Fishbein, M., 1998. Application of an integrated behavioral model to predict condom use: A prospective study among high HIV risk groups, Journal of Applied Social Psychology 28 (17) (1998), pp. 1557–1583.   Web of Science ® Google Scholar
• Kelley, T.M., 1993. Neo‐cognitive learning theory: Implications for prevention and early intervention strategies with at‐risk youth, Adolescence 20 (110) (1993), pp. 439–461.   Google Scholar
• Kollmuss, A., and Agyeman, J., 2002. Mind the gap: Why do people act environmentally and what are the barriers to pro‐environmental behavior?, Environmental Education Research 8 (3) (2002), pp. 239–260.   Google Scholar
• Lave, J., and Wenger, E., 1991. Situated learning . New York: Cambridge University Press; 1991.   Google Scholar
• Leach, L., and Knight, M., 2003. "Collaboration for learning and teaching". In: Zepke, N., Nugent, D., and Leach, L., eds. Reflection to transformation: A self‐help book for teachers . Palmerston North: Dunmore: New Zealand; 2003. pp. 139–154.   Google Scholar
• Lever, J.P., Pinol, N.L., and Urlade, J.H., 2005. Poverty, psychological resources, and subjective well‐being, Social Indicators Research 73 (2005), pp. 375–408.   Web of Science ® Google Scholar
• Ludwig, T.D., Buchholz, C., and Clarke, S.W., 2005. Using social marketing to increase the use of helmets among cyclists, Journal of American College Health 54 (1) (2005), pp. 51–58.   PubMed Web of Science ® Google Scholar
• Magill, R.A., 2000. Motor learning: Concepts and applications . Dubuque, Iowa: William C. Brown; 2000.   Google Scholar
• Manstead, A.S.R., 1990. "Attitudes". In: Eysenck, M.W., ed. The Blackwell dictionary of cognitive psychology . Cambridge, MA: Blackwell Basil; 1990.   Google Scholar
• Marcinkowski, T.J., 1989. An analysis of correlates and predictors of responsible environmental behavior . Southern Illinois University; 1989, Unpublished Doctoral Dissertation.   Google Scholar
• Mattson, M., 1999. Toward a reconceptualization of communication cues to action in the Health Belief Model: HIV test counselling, Communication Monographs 66 (3) (1999), pp. 240–265.   PubMed Web of Science ® Google Scholar
• McGuire, W.J., 1972. "Social psychology. New Horizons in Psychology". In: Dodwell, P.C., ed. Oxford, England: Penguin; 1972.   Google Scholar
• McKenzie‐Mohr, D., and Smith, W., 1999. Fostering sustainable behavior: An introduction to community‐based social marketing . Gabriola Island, BC: New Society; 1999.   Google Scholar
• McIlvane, W.J., 2003. A stimulus in need of a response: A review of Relational Frame theory: A post‐Skinnerian account of human language and cognition: Comment, Analysis of Verbal Behavior 19 (2003), pp. 29–37.   PubMed Google Scholar
• Meier, S.T., 1991. Vocational behavior, 1988–1990: Vocational choice, decision‐making, career development interventions and assessment, Journal of Vocational Behavior 39 (2) (1991), pp. 131–181.   Web of Science ® Google Scholar
• Monroe, M., 2003. Two avenues for encouraging conservation behaviors, Human Ecology Review 10 (2) (2003), pp. 113–125.   Google Scholar
• Morris, C.G., and Maisto, A.A., 2006. Understanding psychology . Upper Saddle River, NJ: Pearson Prentice Hall; 2006.   Google Scholar
• Nickerson, R.S., 2003. Psychology and environmental change . Mahwah, NJ: Lawrence Erlbaum; 2003.   Google Scholar
• Nilson, C.D., 1991. How to manage training: A guide to administration, design, and delivery . New York: AMACOM; 1991.   Google Scholar
• Norman, P., and Bennett, P., 1996. "Health locus of control". In: Conner, M., and Norman, P., eds. Predicting health behaviour: Research and practice with social cognition models . Buckingham, UK: Open University Press; 1996. pp. 62–94.   Google Scholar
• North American Association for Environmental Education, 2004. Guidelines for Excellence Series Set . Washington, DC: NAAEE; 2004, Produced by the National Project for Excellence in Environmental Education.   Google Scholar
• Norton, R.E., 1997. DACUM Handbook (2nd edition): Leadership training series, No. 67 . Columbus, OH: Center on Education and Training for Employment; 1997.   Google Scholar
• Omar‐Fauzee, M.S., 2002. An examination of the Transtheoretical Model, ICHPER‐SD Journal 38 (4) (2002), pp. 48–64.   Google Scholar
• Ouellette, J.A., and Wood, W., 1998. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior, Psychological Bulletin 124 (1) (1998), pp. 54–74.   Web of Science ® Google Scholar
• Pearce, J.M., and Hall, J., 1992. "Stimulus significance, conditionability, and the orienting response in rats". In: Campbell, B.A., Hayne, H., and Richardson, R., eds. Attention and information processing in infants and adults: Perspectives from human and animal research . Hillsday, NJ: Lawrence Erlbaum Associates; 1992. pp. 137–160.   Google Scholar
• Petty, R.E., and Cacioppo, J.T., 1981. Attitude and persuasion: Classic and contemporary approaches . Dubuque, IA: William C. Brown; 1981.   Google Scholar
• Porter, B.E., Leeming, F.C., and Dwyer, W.O., 1995. Solid waste recovery: A review of behavioral programs to increase recycling, Environment and Behavior 27 (2) (1995), pp. 122–152.   Web of Science ® Google Scholar
• Prochaska, J.O., Redding, C.A., Harlow, L.L., Rossi, J.S., and Velicer, W.F., 1994. The transtheoretical model of change and HIV prevention: A review, Health Education Quarterly 21 (4) (1994), pp. 471–486.   PubMed Google Scholar
• Prochaska, J.O., 1979. Systems of psychotherapy: A transtheoretical analysis . Homewood, IL: Dorsey Press; 1979.   Google Scholar
• Ramsey, J.M., 1989. A study of the effects of issue investigation and action training on characteristics associated with environmental behavior in seventh grade students . Southern Illinois University; 1989, Unpublished Doctoral Dissertation.   Google Scholar
• Ramsey, C.E., and Rickson, R.E., 1977. Environmental knowledge and attitudes, Journal of Environmental Education 8 (1977), pp. 10–18.   Web of Science ® Google Scholar
• Reber, A.S., and Reber, E., 2001. The Penguin dictionary of psychology . London: Penguin; 2001.   Google Scholar
• Riechard, D.E., and Peterson, S.J., 1998. Perception of environmental risk related to gender, community socioeconomic setting, age, and locus of control, Journal of Environmental Education 30 (1) (1998), pp. 11–19.   Google Scholar
• Robottom, I., and Hart, P., 1995. Behaviorist EE research: Environmentalism as individualism, Journal of Environmental Education 26 (2) (1995), pp. 5–9.   Google Scholar
• Robottom, I., and Hart, P., 1993. Towards a meta‐research agenda in science and environmental education, International Journal of Science Education 15 (5) (1993), pp. 591–605.   Web of Science ® Google Scholar
• Roeckelein, J.E., 1998. Dictionary of theories, laws, and concepts in psychology . Westport, CT: Greenwood; 1998.   Google Scholar
• Rogers, E.M., 2003. Diffusion of Innovations (Fifth edition) . New York: Free Press; 2003.   Google Scholar
• Rogers, E.M., 1962. Diffusion of innovations . New York: Free Press of Glencoe; 1962.   Google Scholar
• Rogers, E.M., 1968. Supplement to bibliography on the diffusion of innovations: Diffusion of innovations research report 6a . East Lansing, MI: Michigan State University Department of Communication; 1968.   Google Scholar
• Rogers, E.M., 1972. Taboo communication and social change: Family planning in Asia, and some suggested modifications in the classical diffusion model . Presented at Paper presented to the Department of Human Communication, May 5, in Michigan State University. East Lansing, MI, .   Google Scholar
• Rogers, E.M., and Shoemaker, F.F., 1971. Communication of innovations; A cross‐cultural approach . New York: Free Press of Glencoe; 1971, (Revised edition of Diffusion of Innovations).   Google Scholar
• Rosenstock, I.M., 1990. "The Health Belief Model: Explaining health behavior through expectancies". In: Glanz, K.G., Lewis, F.M., and Rimmer, B.K., eds. Health behavior and health education . San Francisco: Jossey‐Bass; 1990. pp. 39–62.   Google Scholar
• Rosenstock, I.M., 1966. Why people use health services, Milbank Memorial Fund Quarterly 44 (1966), pp. 94–124.   PubMed Google Scholar
• Rotter, J.B., 1954. Social learning and clinical psychology . Englewood Cliffs, NJ: Prentice‐Hall; 1954.   Google Scholar
• Rotter, J.B., 1966. Generalized expectancies for internal versus external control of reinforcement, Psychological Monographs 80 (1) (1966), p. 609.   Google Scholar
• Rotter, J.B., 1990. Internal versus external control of reinforcement: A case history of a variable, American Psychologist 45 (4) (1990), pp. 489–493.   Web of Science ® Google Scholar
• Santrock, J.W., 2000. Psychology: Brief edition . Boston: McGraw‐Hill; 2000.   Google Scholar
• Scanell, D.J., and Wlazelek, B., 2002. Validation study of the AIDS Health Belief Scale, Canadian Journal of Human Sexuality 11 (1) (2002), pp. 41–49.   Google Scholar
• Sensiper, S., 1999. Generating family‐school partnerships through social marketing . Cambridge, MA: Harvard Family Research Project; 1999, Retrieved 2 June 2008 from http://www.hfrp.org/publications‐resources/browse‐our‐publications/generating‐family‐school‐partnerships‐through‐social‐marketing .   Google Scholar
• Sheeran, P., and Abraham, C., 1996. "The Health Belief Model". In: Conner, M., and Norman, P., eds. Predicting health behavior: Research and practice with social cognition model . Buckingham, UK: Open University Press; 1996. pp. 23–61.   Google Scholar
• Sia, A.P., Hungerford, H., and Tomera, A.N., 1985. Selected predictors of responsible environmental behavior: An analysis, Journal of Environmental Education 17 (2) (1985), pp. 31–40.   Google Scholar
• Sia, A.P., Hungerford, H., and Tomera, A.N., 1986. A study of predictors of environmental behaviour using US samples, Southern African Journal of Environmental Education 2 (1986), pp. 3–7.   Google Scholar
• Sisson, G.R., 2001. Hands‐on training: A simple and effective method for on‐the‐job training . San Francisco: Berrett‐Kowhler; 2001.   Google Scholar
• Sivek, D., 1989. An analysis of selected predictors of environmental behavior of three conservation organizations . Southern Illinois University; 1989, Unpublished Doctoral Dissertation.   Google Scholar
• Smith, W.A., 2006. "Social marketing: an overview of approach and effects". In: Injury Prevention . 2006. pp. 38–43, 12 Supplement 1.   Google Scholar
• Smith‐Sebasto, N.J., and Fortner, R.W., 1994. The environmental action internal control index, Journal of Environmental Education 25 (4) (1994), pp. 23–30.   Google Scholar
• Smith‐Sebasto, N.J., 1992. The revised perceived environmental control measure: a review and analysis, Journal of Environmental Education 23 (2) (1992), pp. 24–33.   Google Scholar
• Spiegler, M.D., and Guevremont, D.C., 2003. Contemporary Behavior Therapy . Belmont, CA: Wadsworth Publishing Company; 2003.   Google Scholar
• 1992. Squire, L.R., ed. Encyclopedia of learning and memory . New York: Macmillan; 1992.   Google Scholar
• Staddon, J., 2004. The future, Behavior and Philosophy 32 (2004), pp. 243–245.   Google Scholar
• Staats, H., 2003. "Understanding proenvironmental attitudes and behavior: An analysis and review of research based on the Theory of Planned Behavior". In: Bonnes, M., Lee, T., and Bonaiuto, M., eds. Psychological theories for environmental issues . Burlington, VT: Ashgate; 2003. pp. 171–202.   Google Scholar
• Stern, P.C., and Oskamp, S., 1987. "Managing scarce environmental resources". In: Stokols, D., and Altman, I., eds. Handbook of environmental psychology . Vol. 2. New York: Wiley; 1987. pp. 1043–1088.   Google Scholar
• Suppes, P., 1975. From behaviorism to neo‐behaviorism, Theory and Decision 6 (3) (1975), pp. 269–285.   Web of Science ® Google Scholar
• Sutherland, S., 1996. The international dictionary of psychology . New York: Crossroad; 1996.   Google Scholar
• Thombs, D.L., Dimintroff, J., Wolcott, B.J., Nickel, M., and Austin, D.M., 1996. The effects of drinking motivations and behavior on ratings of alcohol intervention effectiveness: A social marketing analysis, Journal of Alcohol and Drug Education 41 (2) (1996), pp. 63–79.   Google Scholar
• Tomporowski, P.D., 2003. The psychology of skill: A life‐span approach . Westport, CT: Praeger; 2003.   Google Scholar
• Triandis, H.C., 1977. Interpersonal Behavior . Monterey, CA: Brooks/Cole; 1977.   Google Scholar
• UNESCO‐UNEP, 1976. The Belgrade charter, Connect: UNESCO‐UNEP Environmental Education Newsletter 1 (1) (1976), pp. 1–2.   Google Scholar
• UNESCO, 1978. Final report: Intergovernmental conference on environmental education . Paris: UNESCO ED/MD/49; 1978, Organized by UNESCO in Cooperation with UNEP, Tbilisi, USSR, 14–26 October, 1977.   Google Scholar
• Volk, T.L., and McBeth, W., 1998. "Environmental literacy in the United States". In: Hungerford, H.R., Bluhm, W.J., Volk, T.L., and Ramsy, J.M., eds. Essential readings in environmental education . Champaign, IL: Stipes; 1998. pp. 76–88.   Google Scholar
• Wallace, L.S., 2002. Osteoporosis prevention in college women: Application of the expanded Health Belief Model, American Journal of Health Behavior 26 (3) (2002), pp. 163–172.   PubMed Web of Science ® Google Scholar
• Wexley, K.N., and Latham, G.P., 1991. Developing and training human resources in organizations . New York: HarperCollins; 1991.   Google Scholar
• 1999. Wilson, R.A., and Keil, F.C., eds. The MIT encyclopedia of the cognitive sciences . London: MIT Press; 1999.   Google Scholar
• Winfield, E.B., and Whaley, A.L., 2002. A comprehensive test of the Health Belief Model in the prediction of condom use among African American college students, Journal of Black Psychology 28 (4) (2002), pp. 330–346.   Google Scholar
• Wittig, A.F., and Belkin, G., 1990. Introduction to psychology . New York: McGraw‐Hill; 1990.   Google Scholar
• Woodell, J., and Garofoli, E., 2002. "Faculty development and the diffusion of innovations". 2002, Campus Technology, December 30. Available online: www.campustechnology.com/articles/39321 .   Google Scholar
• Yerkes, R., and Biederman, K., 2003. Outdoor education and the development of environmental responsibility revisited, Taproot 13 (4) (2003), pp. 3–5.   Google Scholar
• Zak‐Pace, J., and Stern, M., 2004. Health belief factors and dispositional optimism as predictors of STD and HIV preventive behavior, Journal of American College Health 52 (5) (2004), pp. 229–236.   PubMed Web of Science ® Google Scholar
• Zepke, N., 2005. Diversity, adult education and the future: A tentative exploration, International Journal of Lifelong Education 24 (2) (2005), pp. 165–178.   Google Scholar
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Resilience is the process and outcome of successfully adapting to difficult or challenging life experiences, especially through mental, emotional, and behavioral flexibility and adjustment to external and internal demands.

A number of factors contribute to how well people adapt to adversities, including the ways in which individuals view and engage with the world, the availability and quality of social resources, and specific coping strategies.

Psychological research demonstrates that the resources and skills associated with resilience can be cultivated and practiced.

Adapted from the APA Dictionary of Psychology

Resources from APA

Building your resilience

Resilience for teens: 10 tips to build skills on bouncing back from rough times

Resilience guide for parents and teachers

Building Psychological Resilience in Military Personnel

The Pain Survival Guide, Rev. Ed.

Motivation Myth Busters

Nature Meets Nurture

Magination Press children’s books

Out of the Fires

You Are Your Strong

Abracadabra!

The Hugging Tree

What to Do When Mistakes Make You Quake, Revised

Journal special issues

Psychiatric Rehabilitation for Veterans

Somos Latinxs

Collaborative and Participatory Research to Promote Engagement, Empowerment, and Resilience for Immigrant and Refugee Youth, Families, and Communities

Risk and Resilience in Sexual and Gender Minority Relationships