how to do comparative study in research

How to Do Comparative Analysis in Research ( Examples )

Comparative analysis is a method that is widely used in social science . It is a method of comparing two or more items with an idea of uncovering and discovering new ideas about them. It often compares and contrasts social structures and processes around the world to grasp general patterns. Comparative analysis tries to understand the study and explain every element of data that comparing. 

Most social scientists are involved in comparative analysis. Macfarlane has thought that “On account of history, the examinations are typically on schedule, in that of other sociologies, transcendently in space. The historian always takes their society and compares it with the past society, and analyzes how far they differ from each other.

The comparative method of social research is a product of 19 th -century sociology and social anthropology. Sociologists like Emile Durkheim, Herbert Spencer Max Weber used comparative analysis in their works. For example, Max Weber compares the protestant of Europe with Catholics and also compared it with other religions like Islam, Hinduism, and Confucianism.

To do a systematic comparison we need to follow different elements of the method.

In social science, we can do comparisons in different ways. It is merely different based on the topic, the field of study. Like Emile Durkheim compare societies as organic solidarity and mechanical solidarity. The famous sociologist Emile Durkheim provides us with three different approaches to the comparative method. Which are;

2 . The unit of comparison

3. The motive of comparison

As another method of study, a comparative analysis is one among them for the social scientist. The researcher or the person who does the comparative method must know for what grounds they taking the comparative method. They have to consider the strength, limitations, weaknesses, etc. He must have to know how to do the analysis.

Steps of the comparative method

1. Setting up of a unit of comparison

As mentioned earlier, the first step is to consider and determine the unit of comparison for your study. You must consider all the dimensions of your unit. This is where you put the two things you need to compare and to properly analyze and compare it. It is not an easy step, we have to systematically and scientifically do this with proper methods and techniques. You have to build your objectives, variables and make some assumptions or ask yourself about what you need to study or make a hypothesis for your analysis.

The grounds of comparison should be understandable for the reader. You must acknowledge why you selected these units for your comparison. For example, it is quite natural that a person who asks why you choose this what about another one? What is the reason behind choosing this particular society? If a social scientist chooses primitive Asian society and primitive Australian society for comparison, he must acknowledge the grounds of comparison to the readers. The comparison of your work must be self-explanatory without any complications.

The main element of the comparative analysis is the thesis or the report. The report is the most important one that it must contain all your frame of reference. It must include all your research questions, objectives of your topic, the characteristics of your two units of comparison, variables in your study, and last but not least the finding and conclusion must be written down. The findings must be self-explanatory because the reader must understand to what extent did they connect and what are their differences. For example, in Emile Durkheim’s Theory of Division of Labour, he classified organic solidarity and Mechanical solidarity . In which he means primitive society as Mechanical solidarity and modern society as Organic Solidarity. Like that you have to mention what are your findings in the thesis.

Your paper must link each point in the argument. Without that the reader does not understand the logical and rational advance in your analysis. In a comparative analysis, you need to compare the ‘x’ and ‘y’ in your paper. (x and y mean the two-unit or things in your comparison). To do that you can use likewise, similarly, on the contrary, etc. For example, if we do a comparison between primitive society and modern society we can say that; ‘in the primitive society the division of labour is based on gender and age on the contrary (or the other hand), in modern society, the division of labour is based on skill and knowledge of a person.

Demerits of comparison

Comparative analysis is not always successful. It has some limitations. The broad utilization of comparative analysis can undoubtedly cause the feeling that this technique is a solidly settled, smooth, and unproblematic method of investigation, which because of its undeniable intelligent status can produce dependable information once some specialized preconditions are met acceptably.

One more basic issue with broad ramifications concerns the decision of the units being analyzed. The primary concern is that a long way from being a guiltless as well as basic assignment, the decision of comparison units is a basic and precarious issue. The issue with this sort of comparison is that in such investigations the depictions of the cases picked for examination with the principle one will in general turn out to be unreasonably streamlined, shallow, and stylised with contorted contentions and ends as entailment.

However, a comparative analysis is as yet a strategy with exceptional benefits, essentially due to its capacity to cause us to perceive the restriction of our psyche and check against the weaknesses and hurtful results of localism and provincialism. We may anyway have something to gain from history specialists’ faltering in utilizing comparison and from their regard for the uniqueness of settings and accounts of people groups. All of the above, by doing the comparison we discover the truths the underlying and undiscovered connection, differences that exist in society.

What is comparative analysis? A complete guide

Last updated

18 April 2023

Reviewed by

Jean Kaluza

Short on time? Get an AI generated summary of this article instead

Comparative analysis is a valuable tool for acquiring deep insights into your organization’s processes, products, and services so you can continuously improve them. 

Similarly, if you want to streamline, price appropriately, and ultimately be a market leader, you’ll likely need to draw on comparative analyses quite often.

When faced with multiple options or solutions to a given problem, a thorough comparative analysis can help you compare and contrast your options and make a clear, informed decision.

If you want to get up to speed on conducting a comparative analysis or need a refresher, here’s your guide.

Make comparative analysis less tedious

Dovetail streamlines comparative analysis to help you uncover and share actionable insights

• What exactly is comparative analysis?

A comparative analysis is a side-by-side comparison that systematically compares two or more things to pinpoint their similarities and differences. The focus of the investigation might be conceptual—a particular problem, idea, or theory—or perhaps something more tangible, like two different data sets.

For instance, you could use comparative analysis to investigate how your product features measure up to the competition.

After a successful comparative analysis, you should be able to identify strengths and weaknesses and clearly understand which product is more effective.

You could also use comparative analysis to examine different methods of producing that product and determine which way is most efficient and profitable.

The potential applications for using comparative analysis in everyday business are almost unlimited. That said, a comparative analysis is most commonly used to examine

Emerging trends and opportunities (new technologies, marketing)

Competitor strategies

Financial health

Effects of trends on a target audience

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Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

• Why is comparative analysis so important? 

Comparative analysis can help narrow your focus so your business pursues the most meaningful opportunities rather than attempting dozens of improvements simultaneously.

A comparative approach also helps frame up data to illuminate interrelationships. For example, comparative research might reveal nuanced relationships or critical contexts behind specific processes or dependencies that wouldn’t be well-understood without the research.

For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.

• Comparative vs. competitive analysis—what’s the difference?

Comparative analysis is generally divided into three subtypes, using quantitative or qualitative data and then extending the findings to a larger group. These include

Pattern analysis —identifying patterns or recurrences of trends and behavior across large data sets.

Data filtering —analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria. 

Decision tree —flowcharting to visually map and assess potential outcomes, costs, and consequences.

In contrast, competitive analysis is a type of comparative analysis in which you deeply research one or more of your industry competitors. In this case, you’re using qualitative research to explore what the competition is up to across one or more dimensions.

For example

Service delivery —metrics like the Net Promoter Scores indicate customer satisfaction levels.

Market position — the share of the market that the competition has captured.

Brand reputation —how well-known or recognized your competitors are within their target market.

• Tips for optimizing your comparative analysis

Conduct original research

Thorough, independent research is a significant asset when doing comparative analysis. It provides evidence to support your findings and may present a perspective or angle not considered previously. 

Make analysis routine

To get the maximum benefit from comparative research, make it a regular practice, and establish a cadence you can realistically stick to. Some business areas you could plan to analyze regularly include:

Profitability

Competition

Experiment with controlled and uncontrolled variables

In addition to simply comparing and contrasting, explore how different variables might affect your outcomes.

For example, a controllable variable would be offering a seasonal feature like a shopping bot to assist in holiday shopping or raising or lowering the selling price of a product.

Uncontrollable variables include weather, changing regulations, the current political climate, or global pandemics.

Put equal effort into each point of comparison

Most people enter into comparative research with a particular idea or hypothesis already in mind to validate. For instance, you might try to prove the worthwhileness of launching a new service. So, you may be disappointed if your analysis results don’t support your plan.

However, in any comparative analysis, try to maintain an unbiased approach by spending equal time debating the merits and drawbacks of any decision. Ultimately, this will be a practical, more long-term sustainable approach for your business than focusing only on the evidence that favors pursuing your argument or strategy.

Writing a comparative analysis in five steps

To put together a coherent, insightful analysis that goes beyond a list of pros and cons or similarities and differences, try organizing the information into these five components:

1. Frame of reference

Here is where you provide context. First, what driving idea or problem is your research anchored in? Then, for added substance, cite existing research or insights from a subject matter expert, such as a thought leader in marketing, startup growth, or investment

2. Grounds for comparison Why have you chosen to examine the two things you’re analyzing instead of focusing on two entirely different things? What are you hoping to accomplish?

3. Thesis What argument or choice are you advocating for? What will be the before and after effects of going with either decision? What do you anticipate happening with and without this approach?

For example, “If we release an AI feature for our shopping cart, we will have an edge over the rest of the market before the holiday season.” The finished comparative analysis will weigh all the pros and cons of choosing to build the new expensive AI feature including variables like how “intelligent” it will be, what it “pushes” customers to use, how much it takes off the plates of customer service etc.

Ultimately, you will gauge whether building an AI feature is the right plan for your e-commerce shop.

4. Organize the scheme Typically, there are two ways to organize a comparative analysis report. First, you can discuss everything about comparison point “A” and then go into everything about aspect “B.” Or, you alternate back and forth between points “A” and “B,” sometimes referred to as point-by-point analysis.

Using the AI feature as an example again, you could cover all the pros and cons of building the AI feature, then discuss the benefits and drawbacks of building and maintaining the feature. Or you could compare and contrast each aspect of the AI feature, one at a time. For example, a side-by-side comparison of the AI feature to shopping without it, then proceeding to another point of differentiation.

5. Connect the dots Tie it all together in a way that either confirms or disproves your hypothesis.

For instance, “Building the AI bot would allow our customer service team to save 12% on returns in Q3 while offering optimizations and savings in future strategies. However, it would also increase the product development budget by 43% in both Q1 and Q2. Our budget for product development won’t increase again until series 3 of funding is reached, so despite its potential, we will hold off building the bot until funding is secured and more opportunities and benefits can be proved effective.”

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• Comparative Analysis

What It Is and Why It's Useful

Comparative analysis asks writers to make an argument about the relationship between two or more texts. Beyond that, there's a lot of variation, but three overarching kinds of comparative analysis stand out:

• Coordinate (A ↔ B): In this kind of analysis, two (or more) texts are being read against each other in terms of a shared element, e.g., a memoir and a novel, both by Jesmyn Ward; two sets of data for the same experiment; a few op-ed responses to the same event; two YA books written in Chicago in the 2000s; a film adaption of a play; etc. 
• Subordinate (A  → B) or (B → A ): Using a theoretical text (as a "lens") to explain a case study or work of art (e.g., how Anthony Jack's The Privileged Poor can help explain divergent experiences among students at elite four-year private colleges who are coming from similar socio-economic backgrounds) or using a work of art or case study (i.e., as a "test" of) a theory's usefulness or limitations (e.g., using coverage of recent incidents of gun violence or legislation un the U.S. to confirm or question the currency of Carol Anderson's The Second ).
• Hybrid [A  → (B ↔ C)] or [(B ↔ C) → A] , i.e., using coordinate and subordinate analysis together. For example, using Jack to compare or contrast the experiences of students at elite four-year institutions with students at state universities and/or community colleges; or looking at gun culture in other countries and/or other timeframes to contextualize or generalize Anderson's main points about the role of the Second Amendment in U.S. history.

"In the wild," these three kinds of comparative analysis represent increasingly complex—and scholarly—modes of comparison. Students can of course compare two poems in terms of imagery or two data sets in terms of methods, but in each case the analysis will eventually be richer if the students have had a chance to encounter other people's ideas about how imagery or methods work. At that point, we're getting into a hybrid kind of reading (or even into research essays), especially if we start introducing different approaches to imagery or methods that are themselves being compared along with a couple (or few) poems or data sets.

Why It's Useful

In the context of a particular course, each kind of comparative analysis has its place and can be a useful step up from single-source analysis. Intellectually, comparative analysis helps overcome the "n of 1" problem that can face single-source analysis. That is, a writer drawing broad conclusions about the influence of the Iranian New Wave based on one film is relying entirely—and almost certainly too much—on that film to support those findings. In the context of even just one more film, though, the analysis is suddenly more likely to arrive at one of the best features of any comparative approach: both films will be more richly experienced than they would have been in isolation, and the themes or questions in terms of which they're being explored (here the general question of the influence of the Iranian New Wave) will arrive at conclusions that are less at-risk of oversimplification.

For scholars working in comparative fields or through comparative approaches, these features of comparative analysis animate their work. To borrow from a stock example in Western epistemology, our concept of "green" isn't based on a single encounter with something we intuit or are told is "green." Not at all. Our concept of "green" is derived from a complex set of experiences of what others say is green or what's labeled green or what seems to be something that's neither blue nor yellow but kind of both, etc. Comparative analysis essays offer us the chance to engage with that process—even if only enough to help us see where a more in-depth exploration with a higher and/or more diverse "n" might lead—and in that sense, from the standpoint of the subject matter students are exploring through writing as well the complexity of the genre of writing they're using to explore it—comparative analysis forms a bridge of sorts between single-source analysis and research essays.

Typical learning objectives for single-sources essays: formulate analytical questions and an arguable thesis, establish stakes of an argument, summarize sources accurately, choose evidence effectively, analyze evidence effectively, define key terms, organize argument logically, acknowledge and respond to counterargument, cite sources properly, and present ideas in clear prose.

Common types of comparative analysis essays and related types: two works in the same genre, two works from the same period (but in different places or in different cultures), a work adapted into a different genre or medium, two theories treating the same topic; a theory and a case study or other object, etc.

How to Teach It: Framing + Practice

Framing multi-source writing assignments (comparative analysis, research essays, multi-modal projects) is likely to overlap a great deal with "Why It's Useful" (see above), because the range of reasons why we might use these kinds of writing in academic or non-academic settings is itself the reason why they so often appear later in courses. In many courses, they're the best vehicles for exploring the complex questions that arise once we've been introduced to the course's main themes, core content, leading protagonists, and central debates.

For comparative analysis in particular, it's helpful to frame assignment's process and how it will help students successfully navigate the challenges and pitfalls presented by the genre. Ideally, this will mean students have time to identify what each text seems to be doing, take note of apparent points of connection between different texts, and start to imagine how those points of connection (or the absence thereof)

• complicates or upends their own expectations or assumptions about the texts
• complicates or refutes the expectations or assumptions about the texts presented by a scholar
• confirms and/or nuances expectations and assumptions they themselves hold or scholars have presented
• presents entirely unforeseen ways of understanding the texts

—and all with implications for the texts themselves or for the axes along which the comparative analysis took place. If students know that this is where their ideas will be heading, they'll be ready to develop those ideas and engage with the challenges that comparative analysis presents in terms of structure (See "Tips" and "Common Pitfalls" below for more on these elements of framing).

Like single-source analyses, comparative essays have several moving parts, and giving students practice here means adapting the sample sequence laid out at the " Formative Writing Assignments " page. Three areas that have already been mentioned above are worth noting:

• Gathering evidence : Depending on what your assignment is asking students to compare (or in terms of what), students will benefit greatly from structured opportunities to create inventories or data sets of the motifs, examples, trajectories, etc., shared (or not shared) by the texts they'll be comparing. See the sample exercises below for a basic example of what this might look like.
• Why it Matters: Moving beyond "x is like y but also different" or even "x is more like y than we might think at first" is what moves an essay from being "compare/contrast" to being a comparative analysis . It's also a move that can be hard to make and that will often evolve over the course of an assignment. A great way to get feedback from students about where they're at on this front? Ask them to start considering early on why their argument "matters" to different kinds of imagined audiences (while they're just gathering evidence) and again as they develop their thesis and again as they're drafting their essays. ( Cover letters , for example, are a great place to ask writers to imagine how a reader might be affected by reading an their argument.)
• Structure: Having two texts on stage at the same time can suddenly feel a lot more complicated for any writer who's used to having just one at a time. Giving students a sense of what the most common patterns (AAA / BBB, ABABAB, etc.) are likely to be can help them imagine, even if provisionally, how their argument might unfold over a series of pages. See "Tips" and "Common Pitfalls" below for more information on this front.

Sample Exercises and Links to Other Resources

• Common Pitfalls
• Advice on Timing
• Try to keep students from thinking of a proposed thesis as a commitment. Instead, help them see it as more of a hypothesis that has emerged out of readings and discussion and analytical questions and that they'll now test through an experiment, namely, writing their essay. When students see writing as part of the process of inquiry—rather than just the result—and when that process is committed to acknowledging and adapting itself to evidence, it makes writing assignments more scientific, more ethical, and more authentic. 
• Have students create an inventory of touch points between the two texts early in the process.
• Ask students to make the case—early on and at points throughout the process—for the significance of the claim they're making about the relationship between the texts they're comparing.
• For coordinate kinds of comparative analysis, a common pitfall is tied to thesis and evidence. Basically, it's a thesis that tells the reader that there are "similarities and differences" between two texts, without telling the reader why it matters that these two texts have or don't have these particular features in common. This kind of thesis is stuck at the level of description or positivism, and it's not uncommon when a writer is grappling with the complexity that can in fact accompany the "taking inventory" stage of comparative analysis. The solution is to make the "taking inventory" stage part of the process of the assignment. When this stage comes before students have formulated a thesis, that formulation is then able to emerge out of a comparative data set, rather than the data set emerging in terms of their thesis (which can lead to confirmation bias, or frequency illusion, or—just for the sake of streamlining the process of gathering evidence—cherry picking). 
• For subordinate kinds of comparative analysis , a common pitfall is tied to how much weight is given to each source. Having students apply a theory (in a "lens" essay) or weigh the pros and cons of a theory against case studies (in a "test a theory") essay can be a great way to help them explore the assumptions, implications, and real-world usefulness of theoretical approaches. The pitfall of these approaches is that they can quickly lead to the same biases we saw here above. Making sure that students know they should engage with counterevidence and counterargument, and that "lens" / "test a theory" approaches often balance each other out in any real-world application of theory is a good way to get out in front of this pitfall.
• For any kind of comparative analysis, a common pitfall is structure. Every comparative analysis asks writers to move back and forth between texts, and that can pose a number of challenges, including: what pattern the back and forth should follow and how to use transitions and other signposting to make sure readers can follow the overarching argument as the back and forth is taking place. Here's some advice from an experienced writing instructor to students about how to think about these considerations:

a quick note on STRUCTURE

     Most of us have encountered the question of whether to adopt what we might term the “A→A→A→B→B→B” structure or the “A→B→A→B→A→B” structure.  Do we make all of our points about text A before moving on to text B?  Or do we go back and forth between A and B as the essay proceeds?  As always, the answers to our questions about structure depend on our goals in the essay as a whole.  In a “similarities in spite of differences” essay, for instance, readers will need to encounter the differences between A and B before we offer them the similarities (A d →B d →A s →B s ).  If, rather than subordinating differences to similarities you are subordinating text A to text B (using A as a point of comparison that reveals B’s originality, say), you may be well served by the “A→A→A→B→B→B” structure.  

     Ultimately, you need to ask yourself how many “A→B” moves you have in you.  Is each one identical?  If so, you may wish to make the transition from A to B only once (“A→A→A→B→B→B”), because if each “A→B” move is identical, the “A→B→A→B→A→B” structure will appear to involve nothing more than directionless oscillation and repetition.  If each is increasingly complex, however—if each AB pair yields a new and progressively more complex idea about your subject—you may be well served by the “A→B→A→B→A→B” structure, because in this case it will be visible to readers as a progressively developing argument.

As we discussed in "Advice on Timing" at the page on single-source analysis, that timeline itself roughly follows the "Sample Sequence of Formative Assignments for a 'Typical' Essay" outlined under " Formative Writing Assignments, " and it spans about 5–6 steps or 2–4 weeks. 

Comparative analysis assignments have a lot of the same DNA as single-source essays, but they potentially bring more reading into play and ask students to engage in more complicated acts of analysis and synthesis during the drafting stages. With that in mind, closer to 4 weeks is probably a good baseline for many single-source analysis assignments. For sections that meet once per week, the timeline will either probably need to expand—ideally—a little past the 4-week side of things, or some of the steps will need to be combined or done asynchronously.

What It Can Build Up To

Comparative analyses can build up to other kinds of writing in a number of ways. For example:

• They can build toward other kinds of comparative analysis, e.g., student can be asked to choose an additional source to complicate their conclusions from a previous analysis, or they can be asked to revisit an analysis using a different axis of comparison, such as race instead of class. (These approaches are akin to moving from a coordinate or subordinate analysis to more of a hybrid approach.)
• They can scaffold up to research essays, which in many instances are an extension of a "hybrid comparative analysis."
• Like single-source analysis, in a course where students will take a "deep dive" into a source or topic for their capstone, they can allow students to "try on" a theoretical approach or genre or time period to see if it's indeed something they want to research more fully.
• DIY Guides for Analytical Writing Assignments

• Types of Assignments
• Unpacking the Elements of Writing Prompts
• Formative Writing Assignments
• Single-Source Analysis
• Research Essays
• Multi-Modal or Creative Projects
• Giving Feedback to Students

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Methods in Comparative Effectiveness Research

Katrina armstrong.

From the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Comparative effectiveness research (CER) seeks to assist consumers, clinicians, purchasers, and policy makers to make informed decisions to improve health care at both the individual and population levels. CER includes evidence generation and evidence synthesis. Randomized controlled trials are central to CER because of the lack of selection bias, with the recent development of adaptive and pragmatic trials increasing their relevance to real-world decision making. Observational studies comprise a growing proportion of CER because of their efficiency, generalizability to clinical practice, and ability to examine differences in effectiveness across patient subgroups. Concerns about selection bias in observational studies can be mitigated by measuring potential confounders and analytic approaches, including multivariable regression, propensity score analysis, and instrumental variable analysis. Evidence synthesis methods include systematic reviews and decision models. Systematic reviews are a major component of evidence-based medicine and can be adapted to CER by broadening the types of studies included and examining the full range of benefits and harms of alternative interventions. Decision models are particularly suited to CER, because they make quantitative estimates of expected outcomes based on data from a range of sources. These estimates can be tailored to patient characteristics and can include economic outcomes to assess cost effectiveness. The choice of method for CER is driven by the relative weight placed on concerns about selection bias and generalizability, as well as pragmatic concerns related to data availability and timing. Value of information methods can identify priority areas for investigation and inform research methods.

INTRODUCTION

The desire to determine the best treatment for a patient is as old as the medical field itself. However, the methods used to make this determination have changed substantially over time, progressing from the humoral model of disease through the Oslerian application of clinical observation to the paradigm of experimental, evidence-based medicine of the last 40 years. Most recently, the field of comparative effectiveness research (CER) has taken center stage 1 in this arena, driven, at least in part, by the belief that better information about which treatment a patient should receive is part of the answer to addressing the unsustainable growth in health care costs in the United States. 2 , 3

The emergence of CER has galvanized a re-examination of clinical effectiveness research methods, both among researchers and policy organizations. New definitions have been created that emphasize the necessity of answering real-world questions, where patients and their clinicians have to pick from a range of possible options, recognizing that the best choice may vary across patients, settings, and even time periods. 4 The long-standing emphasis on double-blinded, randomized controlled trials (RCTs) is increasingly seen as impractical and irrelevant to many of the questions facing clinicians and policy makers today. The importance of generating information that will “assist consumers, clinicians, purchasers, and policy makers to make informed decisions” 1 (p29) is certainly not a new tenet of clinical effectiveness research, but its primacy in CER definitions has important implications for research methods in this area.

CER encompasses both evidence generation and evidence synthesis. 5 Generation of comparative effectiveness evidence uses experimental and observational methods. Synthesis of evidence uses systematic reviews and decision and cost-effectiveness modeling. Across these methods, CER examines a broad range of interventions to “prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care.” 1 (p29)

EXPERIMENTAL METHODS

RCTs became the gold standard for clinical effectiveness research soon after publication of the first RCT in 1948. 6 An RCT compares outcomes across groups of participants who are randomly assigned to different interventions, often including a placebo or control arm ( Fig 1 ). RCTs are widely revered for their ability to address selection bias, the correlation between the type of intervention received and other factors associated with the outcome of interest. RCTs are fundamental to the evaluation of new therapeutic agents that are not available outside of a trial setting, and phase III RCT evidence is required for US Food and Drug Administration approval. RCTs are also important for evaluating new technology, including imaging and devices. Increasingly, RCTs are also used to shed light on biology through correlative mechanistic studies, particularly in oncology.

Experimental and observational study designs. In a randomized controlled trial, a population of interest is screened for eligibility, randomly assigned to alternative interventions, and observed for outcomes of interest. In an observational study, the population of interest is assigned to alternative interventions based on patient, provider, and system factors and observed for outcomes of interest.

However, traditional approaches to RCTs are increasingly seen as impractical and irrelevant to many of the questions facing clinicians and policy makers today. RCTs have long been recognized as having important limitations in real-world decision making, 7 including: one, RCTs often have restrictive enrollment criteria so that the participants do not resemble patients in practice, particularly in clinical characteristics such as comorbidity, age, and medications or in sociodemographic characteristics such as race, ethnicity, and socioeconomic status; two, RCTs are often not feasible, either because of expense, ethical concerns, or patient acceptance; and three, given their expense and enrollment restrictions, RCTs are rarely able to answer questions about how the effect of the intervention may vary across patients or settings.

Despite these limitations, there is little doubt that RCTs will be a major component of CER. 8 Furthermore, their role is likely to grow with new approaches that increase their relevance in clinical practice. 9 Adaptive trials use accumulating evidence from the trials to modify trial design of the trial to increase efficiency and the probability that trial participants benefit from participation. 10 These adaptations can include changing the end of the trial, changing the interventions or intervention doses, changing the accrual rate, or changing the probability of being randomly assigned to the different arms. One example of an adaptive clinical trial in oncology is the multiarm I-Spy2 trial, which is evaluating multiple agents for neoadjuvant breast cancer treatment. 11 The I-Spy2 trial uses an adaptive approach to assigning patients to treatment arms (where patients with a tumor profile are more likely to be assigned to the arm with the best outcomes for that profile), and data safety monitoring board decisions are guided by Bayesian predicted probabilities of pathologic complete response. 12 , 13 Other examples of adaptive clinical trials in oncology include a randomized trial of four regiments in metastatic prostate cancer, where patients who did not respond to their initial regimen (selected based on randomization) were then randomly assigned to the remaining three regimens, 14 and the CALGB (Cancer and Leukemia Group B) 49907 trial, which used Bayesian predictive probabilities of inferiority to determine the final sample size needed for the comparison of capecitabine and standard chemotherapy in elderly women with early-stage breast cancer. 15 Pragmatic trials relax some of the traditional rules of RCTs to maximize the relevance of the results for clinicians and policy makers. These changes may include expansion of eligibility criteria, flexibility in the application of the intervention and in the management of the control group, and reduction in the intensity of follow-up or procedures for assessing outcomes. 16

OBSERVATIONAL METHODS

The emergence of comparative effectiveness has led to a renewed interest in the role of observational studies for assessing the benefits and harms of alternative interventions. Observational studies compare outcomes between patients who receive different interventions through some process other than investigator randomization. Most commonly, this process is the natural variation in clinical care, although observational studies also can take advantage of natural experiments, where higher-level changes in care delivery (eg, changes in state policy or changes in hospital unit structure) lead to changes in intervention exposure between groups. Observational studies can enroll patients by exposure (eg, type of intervention) using a cohort design or outcome using a case-control design. Cohort studies can be performed prospectively, where participants are recruited at the time of exposure, or retrospectively, where the exposure occurred before participants are identified.

The strengths and limitations of observational studies for clinical effectiveness research have been debated for decades. 7 , 17 Because the incremental cost of including an additional participant is generally low, observational studies often have relatively large numbers of participants who are more representative of the general population. Large, diverse study populations make the results more generalizable to real-world practice and enable the examination of variation in effect across patient subgroups. This advantage is particularly important for understanding effectiveness among vulnerable populations, such as racial minorities, who are often underrepresented in RCT participants. Observational studies that take advantage of existing data sets are able to provide results quickly and efficiently, a critical need for most CER. Currently, observational data already play an important role in influencing guidelines in many areas of oncology, particularly around prevention (eg, nutritional guidelines, management of BRCA1/2 mutation carriers) 18 , 19 and the use of diagnostic tests (eg, use of gene expression profiling in women with node-negative, estrogen receptor–positive breast cancer). 20 However, observational studies also have important limitations. Observational studies are only feasible if the intervention of interest is already being used in clinical practice; they are not possible for evaluation of new drugs or devices. Observational studies are subject to bias, including performance bias, detection bias, and selection bias. 17 , 21 Performance bias occurs when the delivery of one type of intervention is associated with generally higher levels of performance by the health care unit (ie, health care quality) than the delivery of a different type of intervention, making it difficult to determine if better outcomes are the result of the intervention or the accompanying higher-quality health care. Detection bias occurs when the outcomes of interest are more easily detected in one group than another, generally because of differential contact with the health care system between groups. Selection bias is the most important concern in the validity of observational studies and occurs when intervention groups differ in characteristics that are associated with the outcome of interest. These differences can occur because a characteristic is part of the decision about which treatment to recommend (ie, disease severity), which is often termed confounding by indication, or because it is correlated with both intervention and outcome for another reason. A particular concern for CER of therapies is that some new agents may be more likely to be used in patients for whom established therapies have failed and who are less likely to be responsive to any therapy.

There are two main approaches for addressing bias in observational studies. First, important potential confounders must be identified and included in the data collection. Measured confounders can be addressed through multivariate and propensity score analysis. A telling example of the importance of adequate assessment of potential confounders was found through examination of the observational studies of hormone replacement therapy (HRT) and coronary heart disease (CHD). Meta-analyses of observational studies had long estimated a substantial reduction in CHD risk with the use of postmenopausal HRT. However, the WHI (Women's Health Initiative) trial, a large, double-blind RCT of postmenopausal HRT, found no difference in CHD risk between women assigned to HRT or placebo. Although this apparent contradiction is often used as general evidence against the validity of observational studies, a re-examination of the observational studies demonstrated that studies that adjusted for measures of socioeconomic status (a clear confounder between HRT use and better health outcomes) had results similar to those of the WHI, whereas studies that did not adjust for socioeconomic status found a protective effect with HRT 22 ( Fig 2 ). The use of administrative data sets for observational studies of comparative effectiveness is likely to become increasingly common as health information technology spreads, and data become more accessible; however, these data sets may be particularly limiting in their ability to include data on potential confounders. In some cases, the characteristics that influence the treatment decision may not be available in the data (eg, performance status, tumor gene expression), making concerns about confounding by indication too high to proceed without adjusting data collection or considering a different question.

Meta-analysis of observational studies of hormone replacement therapy (HRT) and coronary artery disease incidence comparing studies that did and did not adjust for socioeconomic status (SES). Data adapted. 22

Second, several analytic approaches can be used to address differences between groups in observational studies. The standard analytic approach involves the use of multivariable adjustment through regression models. Regression allows the estimation of the change in the outcome of interest from the difference in intervention, holding the other variables in the model (covariates) constant. Although regression remains the standard approach to analysis of observational data, regression can be misleading if there is insufficient overlap in the covariates between groups or if the functional forms of the variables are incorrectly specified. 23 Furthermore, the number of covariates that can be included is limited by the number of participants with the outcome of interest in the data set.

Propensity score analysis is another approach to the estimation of an intervention effect in observational data that enables the inclusion of a large number of covariates and a transparent assessment of the balance of covariates after adjustment. 23 – 26 Propensity score analysis uses a two-step process, first estimating the probability of receiving a particular intervention based on the observed covariates (the propensity score) and estimating the effect of the intervention within groups of patients who had a similar probability of receiving the intervention (often grouped as quintiles of propensity score). The degree to which the propensity score is able to represent the differences in covariates between intervention groups is assessed by examining the balance in covariates across propensity score categories. In an ideal situation, after participants are grouped by their propensity for being treated, those who receive different interventions have similar clinical and sociodemographic characteristics—at least for the characteristics that are measured ( Table 1 ). Rates of the outcomes of interest are then compared between intervention groups within each propensity score category, paying attention to whether the intervention effect differs across patients with a different propensity for receiving the intervention. In addition, the propensity score itself can be included in a regression model estimating the effect of the intervention on the outcome, a method that also allows for additional adjustment for covariates that were not sufficiently balanced across intervention groups within propensity score categories.

Hypothetic Example of Propensity Score Analysis Comparing Two Intervention Groups, A and B

The use of propensity scores for oncology clinical effectiveness research has become increasingly popular over the last decade, with six articles published in Journal of Clinical Oncology in 2011 alone. 27 – 32 However, propensity score analysis has limitations, the most important of which is that it can only include the variables that are in the available data. If a factor that influences the intervention assignment is not included or measured accurately in the data, it cannot be adequately addressed by a propensity score. For example, in a prior propensity score analysis of the association between active treatment and prostate cancer mortality among elderly men, we were able to include only the variables available in Surveillance, Epidemiology, and End Results–Medicare linked data in our propensity score. 33 The data included some of the factors that influence treatment decisions (eg, age, comorbidities, tumor grade, and size) but not others (eg, functional status, prostate-specific antigen score). Furthermore, the measurement of some of the available factors was imperfect—for example, assessment of comorbidities was based on billing codes, which can underestimate actual comorbidity burden and provide no information about the severity of the comorbidity. Thus, although the final result demonstrating a fairly strong association between active treatment and reduced mortality was quite robust based on the data that were available, it is still possible that the association represents unaddressed selection factors where healthier men underwent active treatment. 34

Instrumental variable methods are a third analytic approach that estimate the effect of an intervention in observational data without requiring the factors that differ between the intervention groups to be available in the data, thereby addressing both measured and unmeasured confounders. 35 The goal underlying instrumental variable analysis is to identify a characteristic (called the instrument) that strongly influences the assignment of patients to intervention but is not associated with the outcomes of interest (except through the intervention). In essence, an instrumental variable approach is an attempt to replicate an RCT, where the instrument is randomization. 36 Common instruments include the patterns of treatment across geographic areas or health care providers, the distance to a health care facility able to provide the intervention of interest, or structural characteristics of the health care system that influence what interventions are used, such as the density of certain types of providers or facilities. The analysis involves two stages: first, the probability of receiving the intervention of interest is estimated as a function of the instrument variable and other covariates; second, a model is built predicting the outcome of interest based on the instrument-based intervention probability and the residual from the first model.

Instrumental variable analysis is commonly used in economics 37 and has increasingly been applied to health and health care. In oncology, instrumental variable approaches have been used to examine the effectiveness of treatments for lung, prostate, bladder, and breast cancers, with the most common instruments being area-level treatment patterns. 38 – 42 One recent analysis of prostate cancer treatment found that multivariable regression and propensity score methods resulted in essentially the same estimate of effect for radical prostatectomy, but an instrumental variable based on the treatment pattern of the previous year found no benefit from radical prostatectomy, similar to the estimate from a recently published trial. 41 , 43 However, concerns also exist about the validity of instrumental variable results, particularly if the instrument is not strongly associated with the intervention, or if there are other potential pathways by which the instrument may influence the outcome. Although the strength of the association between the instrument and the intervention assignment can be tested in the analysis, alternative pathways by which the instrument may be associated with the outcome are often not identified until after publication. A recent instrumental variable analysis used annual rainfall as the instrument to demonstrate an association between television watching and autism, arguing that annual rainfall is associated with the amount of time children watch television but is not otherwise associated with the risk of autism. 44 The findings generated considerable controversy after publication, with the identification of several other potential links between rainfall and autism. 45 Instrumental variable methods have traditionally been unable to examine differences in effect between patient subgroups, but new approaches may improve their utility in this important component of CER. 46 , 47

SYSTEMATIC REVIEWS

For some decisions faced by clinicians and policy makers, there is insufficient evidence to inform decision making, and new studies to generate evidence are needed. However, for other decisions, evidence exists but is sufficiently complex or controversial that it must be synthesized to inform decision making. Systematic reviews are an important form of evidence synthesis that brings together the available evidence using an organized and evaluative approach. 48 Systematic reviews are frequently used for guideline development and generally include four major steps. 49 First, the clinical decision is identified, and the analytic framework and key questions are determined. Sometimes the decision may be straightforward and involve a single key question (eg, Does drug A reduce the incidence of disease B?), but other times the question may be more complicated (eg, Should gene expression profiling be used in early-stage breast cancer?) and involve multiple key questions. 50 Second, the literature is searched to identify the relevant studies using inclusion and exclusion criteria that may include the timing of the study, the study design, and the location of the study. Third, the identified studies are graded on quality using established criteria such as the CONSORT criteria for RCTs 51 and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria for observational studies. 52 Studies that do not meet a minimum quality threshold may be excluded because of concern about the validity of the results. Fourth, the results of all the studies are collated in evidence tables, often including key characteristics of the study design or population that might influence the results. Meta-analytic techniques may be used to combine results across studies when there is sufficient homogeneity to make a single-point estimate statistically valid. Alternatively, models may be used to identify the study or population factors that are associated with different results.

Although systematic reviews are a key component of evidence-based medicine, their role in CER is still uncertain. The traditional approach to systematic reviews has often excluded observational studies because of concerns about internal validity, but such exclusions may greatly limit the evidence available for many important comparative effectiveness questions. CER is designed to inform real-world decisions between available alternatives, which may include multiple tradeoffs. Inclusion of information about harms in comparative effectiveness systematic reviews is desirable but often challenging because of limited data. Finally, systematic reviews are rarely able to examine differences in intervention effects across patient characteristics, another important step for achieving the goals of CER.

DECISION AND COST-EFFECTIVENESS ANALYSIS

Another evidence synthesis method that is gaining increasing traction in CER is decision modeling. Decision modeling is a quantitative approach to evidence synthesis that brings together data from a range of sources to estimate expected outcomes of different interventions. 53 The first step in a decision model is to lay out the structure of the decision, including the alternative choices and the clinical and economic outcomes of those alternatives. 54 Ensuring that the structure of the model holds true to the clinical scenario of interest without becoming overwhelmed by minor possible variations is critical for the eventual impact of the model. 55 Once the decision structure is determined, a decision tree or simulation model is created that incorporates the probabilities of different outcomes over time and the change in those probabilities from the use of different interventions. 56 , 57 To calculate the expected outcomes, a hypothetic cohort of patients is run through each of the decision alternatives in the model. Estimated outcomes are generally assessed as a count of events in the cohort (eg, deaths, cancers) or as the mean or median life expectancy among the cohort. 58

Decision models can also include information about the value placed on each of the outcomes (often referred to as utility) as well as the health care costs incurred by the interventions and the health outcomes. A decision model that includes cost and utility is often referred to as a cost-benefit or cost-effectiveness model and is used in some settings to compare value across interventions. The types of costs that are included depend on the perspective of the model, with a model from the societal perspective including both direct and indirect medical costs (eg, loss of productivity), a model from a payer (ie, insurer) perspective including only direct medical costs, and a model from a patient perspective including the costs experienced by the patient. Future costs are discounted to address the change in monetary value over time. 59 Sensitivity analyses are used to explore the impact of different assumptions on the model results, a critical step for understanding how the results should be used in clinical and policy decisions and for the development of future evidence-generation research. These sensitivity analyses often use a probabilistic approach, where a distribution is entered for each of the inputs and the computer samples from those distributions across a large number of simulations, thereby creating a confidence interval around the estimated outcomes of the alternative choices.

Decision models have several strengths in CER. They can link multiple sources of information to estimate the effect of different interventions on health outcomes, even when there are no studies that directly assess the effect of interest. Because they can examine the effect of variation in different probability estimates, they are particularly useful for understanding how patient characteristics will affect the expected outcomes of different interventions. Decision models can also estimate the impact of an intervention across a population, including the effect on economic outcomes. Decision and cost-effectiveness analyses have been used frequently in oncology, particularly for decisions with options that include the use of a diagnostic or screening test (eg, bone mineral density testing for management of osteoporosis risk), 60 involve significant tradeoffs (eg, adjuvant chemotherapy), 61 or have only limited empirical evidence (eg, management strategies in BRCA mutation carriers). 62

However, decision models also have several limitations that have limited their impact on clinical and policy decision making in the United States to date and are likely to constrain their role in future CER. Often, model results are highly sensitive to the assumptions of the model, and removing bias from these assumptions is difficult. The potential impact of conflicts of interest is high. Decision models require data inputs. For many decisions, data are insufficient for key inputs, requiring the use of educated guesses (ie, expert opinion). The measurement of utility has proven particularly challenging and can lead to counterintuitive results. In the end, decision analysis is similar to other comparative effectiveness methods—useful for the right question as long as results are interpreted with an understanding of the methodologic limitations.

SELECTION OF CER METHODS

The choice of method for a comparative effectiveness study involves the consideration of multiple factors. The Patient-Centered Outcomes Research Institute Methods Committee has identified five intrinsic and three extrinsic factors ( Table 2 ), including internal validity, generalizability, and variation across patient subgroups as well as the feasibility and time urgency. 63 The importance of these factors will vary across the questions being considered. For some questions, the concern about selection bias will be too great for observational studies, particularly if a strong instrument cannot be identified. Many questions about aggressive versus less aggressive treatments may fall into this category, because the decision is often correlated with patient characteristics that predict survival but are rarely found in observational data sets (eg, functional status, social support). For other questions, concern about selection bias will be less pressing than the need for rapid and efficient results. This scenario may be particularly relevant for the comparison of existing therapies that differ in cost or adverse outcomes, where the use of the therapy is largely driven by practice style. In many cases, the choice will be pragmatic based on what data are available and the feasibility of conducting an RCT. These choices will increasingly be informed by the value of information methods 64 – 66 that use economic modeling to provide guidance about where and how investment in CER should be made.

Factors That Influence Selection of Study Design for Patient-Centered Outcome Research

In reality, the questions of CER are not new but are simply more important than ever. Nearly 50 years ago, Sir Austin Bradford Hill spoke about the importance of a broad portfolio of methods in clinical research, saying “To-day … there are many drugs that work and work potently. We want to know whether this one is more potent than that, what dose is right and proper, for what kind of patient.” 7 (p109) This call has expanded beyond drugs to become the charge for CER. To fulfill this charge, investigators will need to use a range of methods, extending the experience in effectiveness research of the last decades “to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.” 1 (p29)

Supported by Award No. UC2CA148310 from the National Cancer Institute.

The content is solely the responsibility of the author and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Author's disclosures of potential conflicts of interest and author contributions are found at the end of this article.

AUTHOR'S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The author(s) indicated no potential conflicts of interest.

Comparative Case Studies: Methodological Discussion

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• First Online: 25 May 2022

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• Marcelo Parreira do Amaral 7  

Part of the book series: Palgrave Studies in Adult Education and Lifelong Learning ((PSAELL))

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Case Study Research has a long tradition and it has been used in different areas of social sciences to approach research questions that command context sensitiveness and attention to complexity while tapping on multiple sources. Comparative Case Studies have been suggested as providing effective tools to understanding policy and practice along three different axes of social scientific research, namely horizontal (spaces), vertical (scales), and transversal (time). The chapter, first, sketches the methodological basis of case-based research in comparative studies as a point of departure, also highlighting the requirements for comparative research. Second, the chapter focuses on presenting and discussing recent developments in scholarship to provide insights on how comparative researchers, especially those investigating educational policy and practice in the context of globalization and internationalization, have suggested some critical rethinking of case study research to account more effectively for recent conceptual shifts in the social sciences related to culture, context, space and comparison. In a third section, it presents the approach to comparative case studies adopted in the European research project YOUNG_ADULLLT that has set out to research lifelong learning policies in their embeddedness in regional economies, labour markets and individual life projects of young adults. The chapter is rounded out with some summarizing and concluding remarks.

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Introduction to the Book and the Comparative Study

Theoretical and Methodological Considerations

Main findings and discussion.

• Case-based research
• Comparative case studies

1 Introduction

Exploring landscapes of lifelong learning in Europe is a daunting task as it involves a great deal of differences across places and spaces; it entails attending to different levels and dimensions of the phenomena at hand, but not least it commands substantial sensibility to cultural and contextual idiosyncrasies. As such, case-based methodologies come to mind as tested methodological approaches to capturing and examining singular configurations such as the local settings in focus in this volume, in which lifelong learning policies for young people are explored in their multidimensional reality. The ensuing question, then, is how to ensure comparability across cases when departing from the assumption that cases are unique. Recent debates in Comparative and International Education (CIE) research are drawn from that offer important insights into the issues involved and provide a heuristic approach to comparative cases studies. Since the cases focused on in the chapters of this book all stem from a common European research project, the comparative case study methodology allows us to at once dive into the specifics and uniqueness of each case while at the same time pay attention to common treads at the national and international (European) levels.

The chapter, first, sketches the methodological basis of case-based research in comparative studies as a point of departure, also highlighting the requirements in comparative research. In what follows, second, the chapter focuses on presenting and discussing recent developments in scholarship to provide insights on how comparative researchers, especially those investigating educational policy and practice in the context of globalization and internationalization, have suggested some critical rethinking of case study research to account more effectively for recent conceptual shifts in the social sciences related to culture, context, space and comparison. In a third section, it presents the approach to comparative case studies adopted in the European research project YOUNG_ADULLLT that has set out to research lifelong learning policies in their embeddedness in regional economies, labour markets and individual life projects of young adults. The chapter is rounded out with some summarizing and concluding remarks.

2 Case-Based Research in Comparative Studies

In the past, comparativists have oftentimes regarded case study research as an alternative to comparative studies proper. At the risk of oversimplification: methodological choices in comparative and international education (CIE) research, from the 1960s onwards, have fallen primarily on either single country (small n) contextualized comparison, or on cross-national (usually large n, variable) decontextualized comparison (see Steiner-Khamsi, 2006a , 2006b , 2009). These two strands of research—notably characterized by Development and Area Studies on the one side and large-scale performance surveys of the International Association for the Evaluation of Educational Achievement (IEA) type, on the other—demarcated their fields by resorting to how context and culture were accounted for and dealt with in the studies they produced. Since the turn of the century, though, comparativists are more comfortable with case study methodology (see Little, 2000 ; Vavrus and Bartlett 2006 , 2009 ; Bartlett & Vavrus, 2017 ) and diagnoses of an “identity crisis” of the field due to a mass of single-country studies lacking comparison proper (see Schriewer, 1990 ; Wiseman & Anderson, 2013 ) started dying away. Greater acceptance of and reliance on case-based methodology has been related with research on policy and practice in the context of globalization and coupled with the intention to better account for culture and context, generating scholarship that is critical of power structures, sensitive to alterity and of other ways of knowing.

The phenomena that have been coined as constituting “globalization” and “internationalization” have played, as mentioned, a central role in the critical rethinking of case study research. In researching education under conditions of globalization, scholars placed increasing attention on case-based approaches as opportunities for investigating the contemporary complexity of policy and practice. Further, scholarly debates in the social sciences and the humanities surrounding key concepts such as culture, context, space, and place but also comparison have also contributed to a reconceptualization of case study methodology in CIE. In terms of the requirements for such an investigation, scholarship commands an adequate conceptualization that problematizes the objects of study and that does not take them as “unproblematic”, “assum[ing] a constant shared meaning”; in short, objects of study that are “fixed, abstract and absolute” (Fine, quoted in Dale & Robertson, 2009 , p. 1114). Case study research is thus required to overcome methodological “isms” in their research conceptualization (see Dale & Robertson, 2009 ; Robertson & Dale, 2017 ; see also Lange & Parreira do Amaral, 2018 ). In response to these requirements, the approaches to case study discussed in CIE depart from a conceptualization of the social world as always dynamic, emergent, somewhat in motion, and always contested. This view considers the fact that the social world is culturally produced and is never complete or at a standstill, which goes against an understanding of case as something fixed or natural. Indeed, in the past cases have often been understood almost in naturalistic ways, as if they existed out there, waiting for researchers to “discover” them. Usually, definitions of case study also referred to inquiry that aims at elucidating features of a phenomenon to yield an understanding of why, how and with what consequences something happens. One can easily find examples of cases understood simply as sites to observe/measure variables—in a nomothetic cast—or examples, where cases are viewed as specific and unique instances that can be examined in the idiographic paradigm. In contrast, rather than taking cases as pre-existing entities that are defined and selected as cases, recent case-oriented research has argued for a more emergent approach which recognizes that boundaries between phenomenon and context are often difficult to establish or overlap. For this reason, researchers are incited to see this as an exercise of “casing”, that is, of case construction. In this sense, cases here are seen as complex systems (Ragin & Becker, 1992 ) and attention is devoted to the relationships between the parts and the whole, pointing to the relevance of configurations and constellations within as well as across cases in the explanation of complex and contingent phenomena. This is particularly relevant for multi-case, comparative research since the constitution of the phenomena that will be defined, as cases will differ. Setting boundaries will thus also require researchers to account for spatial, scalar (i.e., level or levels with which a case is related) and temporal aspects.

Further, case-based research is also required to account for multiple contexts while not taking them for granted. One of the key theoretical and methodological consequences of globalization for CIE is that it required us to recognize that it alters the nature and significance of what counts as contexts (see Parreira do Amaral, 2014 ). According to Dale ( 2015 ), designating a process, or a type of event, or a particular organization, as a context, entails bestowing a particular significance on them, as processes, events, and so on that are capable of affecting other processes and events. The key point is that rather than being so intrinsically, or naturally, contexts are constructed as “contexts”. In comparative research, contexts have been typically seen as the place (or the variables) that enable us to explain why what happens in one case is different from what happens another case; what counts as context then is seen as having the same effect everywhere, although the forms it takes vary substantially (see Dale, 2015 ). In more general terms, recent case study approaches aim at accounting for the increasing complexity of the contexts in which they are embedded, which, in turn, is related to the increasing impact of globalization as the “context of contexts” (Dale, 2015 , p. 181f; see also Carter & Sealey, 2013 ; Mjoset, 2013 ). It also aims at accounting for overlapping contexts. Here it is important to note that contexts are not only to be seen in spatio-geographical terms (i.e., local, regional, national, international), but contexts may also be provided by different institutional and/or discursive contexts that create varying opportunity structures (Dale & Parreira do Amaral, 2015 ; see also Chap. 2 in this volume). What one can call temporal contexts also plays an important role, for what happens in the case unfolds as embedded not only in historical time, but may be related to different temporalities (see the concept of “timespace” as discussed by Lingard & Thompson, 2016 ) and thus are influenced by path dependence or by specific moments of crisis (Rhinard, 2019 ; see also McLeod, 2016 ). Moreover, in CIE research, the social-cultural production of the world is influenced by developments throughout the globe that take place at various places and on several scales, which in turn influence each other, but in the end, become locally relevant in different facets. As Bartlett and Vavrus write, “context is not a primordial or autonomous place; it is constituted by social interactions, political processes, and economic developments across scales and times.” ( Bartlett & Vavrus, 2017 , p. 14). Indeed, in this sense, “context is not a container for activity, it is the activity” (Bartlett & Vavrus, 2017 , p. 12, emphasis in orig.).

Also, dealing with the complexity of education policy and practice requires us to transcend the dichotomy of idiographic versus nomothetic approaches to causation. Here, it can be argued that case studies allow us to grasp and research the complexity of the world, thus offering conceptual and methodological tools to explore how phenomena viewed as cases “depend on all of the whole, the parts, the interactions among parts and whole, and the interactions of any system with other complex systems among which it is nested and with which it intersects” (Byrne, 2013 , p. 2). The understanding of causation that undergirds recent developments in case-based research aims at generalization, yet it resists ambitions to establishing universal laws in social scientific research. Focus is placed on processes while tracking the relevant factors, actors and features that help explain the “how” and the “why” questions (Bartlett and Vavrus 2017 , p. 38ff), and on “causal mechanisms”, as varying explanations of outcomes within and across cases, always contingent on interaction with other variables and dependent contexts (see Byrne, 2013 ; Ragin, 2000 ). In short, the nature of causation underlying the recent case study approaches in CIE is configurational and not foundational.

This is also in line with how CIE research regards education practice, research, and policy as a socio-cultural practice. And it refers to the production of social and cultural worlds through “social actors, with diverse motives, intentions, and levels of influence, [who] work in tandem with and/or in response to social forces” (Bartlett and Vavrus 2017 , p. 1). From this perspective, educational phenomena, such as in policymaking, are seen as a “deeply political process of cultural production engaged in and shaped by social actors in disparate locations who exert incongruent amounts of influence over the design, implementation, and evaluation of policy” ( Bartlett & Vavrus, 2017 , p. 1f). Culture here is understood in non-static and complex ways that reinforce the “importance of examining processes of sense-making as they develop over time, in distinct settings, in relation to systems of power and inequality, and in increasingly interconnected conversation with actors who do not sit physically within the circle drawn around the traditional case” (Bartlett & Vavrus, 2017 , p. 11, emphasis in orig.).

In sum, the approaches to case study put forward in CIE provide conceptual and methodological tools that allow for an analysis of education in the global context throughout scale, space, and time, which is always regarded as complexly integrated and never as isolated or independent. The following subsection discusses Comparative Case Studies (CCS) as suggested in recent comparative scholarship, which aims at attending to the methodological requirements discussed above by integrating horizontal, vertical, and transversal dimensions of comparison.

2.1 Comparative Case Studies: Horizontal, Vertical and Transversal Dimensions

Building up on their previous work on vertical case studies (Bartlett and Vavrus 2017 ; Vavrus & Bartlett, 2006 , 2009 ), Frances Vavrus and Lesley Bartlett have proposed a comparative approach to case study research that aims at meeting the requirements of culture and context sensitive research as discussed in this special issue.

As a research approach, CCS offers two theoretical-methodological lenses to research education as a socio-cultural practice. These lenses represent different views on the research object and account for the complexity of education practice, policy, and research in globalized contexts. The first lens is “context-sensitive”, which focuses on how social practices and interactions constitute and produce social contexts. As quoted above, from the perspective of a socio-cultural practice, “context is not a container for activity, it is the activity” (Vavrus and Bartlett 2017: 12, emphasis in orig.). The settings that influence and condition educational phenomena are culturally produced in different and sometimes overlapping (spatial, institutional, discursive, temporal) contexts as just mentioned. The second CCS lens is “culture-sensitive” and focuses on how socio-cultural practices produce social structures. As such, culture is a process that is emergent, dynamic, and constitutive of meaning-making as well as social structuration.

The CCS approach aims at studying educational phenomena throughout scale, time, and space by providing three axes for a “studying through” of the phenomena in question. As stated by Lesley Bartlett and Frances Vavrus with reference to comparative analyses of global education policy:

the horizontal axis compares how similar policies unfold in distinct locations that are socially produced […] and ‘complexly connected’ […]. The vertical axis insists on simultaneous attention to and across scales […]. The transversal comparison historically situates the processes or relations under consideration (Bartlett and Vavrus 2017 : 3, emphasis in orig.).

These three axes allow for a methodological conceptualization of “policy formation and appropriation across micro-, meso-, and macro levels” by not theorizing them as distinct or unrelated (Bartlett and Vavrus 2017 , p. 4). In following Latour, they state:

the macro is neither “above” nor “below” the intersections but added to them as another of their connections’ […]. In CCS research, one would pay close attention to how actions at different scales mutually influence one another (Bartlett and Vavrus 2017 , p. 13f, emphasis in orig.)

Thus, these three axes contain

processes across space and time; and [the CCS as a research design] constantly compares what is happening in one locale with what has happened in other places and historical moments. These forms of comparison are what we call horizontal, vertical, and transversal comparisons (Bartlett and Vavrus 2017 , p. 11, emphasis in orig.)

In terms of the three axes along with comparison is organized, the authors state that horizontal comparison commands attention to how historical and contemporary processes have variously influenced the “cases”, which might be constructed by focusing “people, groups of people, sites, institutions, social movements, partnerships, etc.” (Bartlett and Vavrus 2017 , p. 53) Horizontal comparisons eschew pressing categories resultant from one case others, which implies including multiple cases at the same scale in a comparative case study, while at the same time attending to “valuable contextual information” about each of them. Horizontal comparisons use units of analysis that are homologous, that is, equivalent in terms of shape, function, or institutional/organizational nature (for instance, schools, ministries, countries, etc.) ( Bartlett & Vavrus, 2017 , p. 53f). Similarly, comparative case studies may also entail tracing a phenomenon across sites, as in multi-sited ethnography (see Coleman & von Hellermann, 2012 ; Marcus, 1995 ).

Vertical comparison, in turn, does not simply imply the comparison of levels; rather it involves analysing networks and their interrelationships at different scales. For instance, in the study of policymaking in a specific case, vertical comparison would consider how actors at different scales variably respond to a policy issued at another level—be it inter−/supranational or at the subnational level. CCS assumes that their different appropriation of policy as discourse and as practice is often due to different histories of racial, ethnic, or gender politics in their communities that appropriately complicate the notion of a single cultural group (Bartlett and Vavrus 2017 , p. 73f). Establishing what counts as context in such a study would be done “by tracing the formation and appropriation of a policy” at different scales; and “by tracing the processes by which actors and actants come into relationship with one another and form non-permanent assemblages aimed at producing, implementing, resisting, and appropriating policy to achieve particular aims” ( Bartlett & Vavrus, 2017 , p. 76). A further element here is that, in this way, one may counter the common problem that comparison of cases (oftentimes countries) usually overemphasizes boundaries and treats them as separated or as self-sustaining containers, when, in reality, actors and institutions at other levels/scales significantly impact policymaking (Bartlett & Vavrus, 2017 ).

In terms of the transversal axis of comparison, Bartlett and Vavrus argue that the social phenomena of interest in a case study have to be seen in light of their historical development (Bartlett & Vavrus, 2017 , p. 93), since these “historical roots” impacted on them and “continues to reverberate into the present, affecting economic relations and social issues such as migration and educational opportunities.” As such, understanding what goes on in a case requires to “understand how it came to be in the first place.” ( Bartlett & Vavrus, 2017 , p. 93) argue:

history offers an extensive fount of evidence regarding how social institutions function and how social relations are similar and different around the world. Historical analysis provides an essential opportunity to contrast how things have changed over time and to consider what has remained the same in one locale or across much broader scales. Such historical comparison reveals important insights about the flexible cultural, social, political, and economic systems humans have developed and sustained over time (Bartlett & Vavrus, 2017 , p. 94).

Further, time and space are intimately related and studying the historical development of the social phenomena of interest in a case study “allows us to assess evidence and conflicting interpretations of a phenomenon,” but also to interrogate our own assumptions about them in contemporary times (Bartlett and Vavrus 2017 ), thus analytically sharpening our historical analyses.

As argued by the authors, researching the global dimension of education practice, research or policy aims at a “studying through” of phenomena horizontally, vertically, and transversally. That is, comparative case study builds on an emergent research design and on a strong process orientation that aims at tracing not only “what”, but also “why” and “how” phenomena emerge and evolve. This approach entails “an open-ended, inductive approach to discover what […] meanings and influences are and how they are involved in these events and activities—an inherently processual orientation” (Bartlett and Vavrus 2017 , p. 7, emphasis in orig.).

The emergent research design and process orientation of the CCS relativizes a priori, somewhat static notions of case construction in CIE and emphasizes the idea of a processual “casing”. The process of casing put forward by CCS has to be understood as a dynamic and open-ended embedding of “cased” research phenomena within moments of scale, space, and time that produce varying sets of conditions or configurations.

In terms of comparison, the primary logic is well in line with more sophisticated approaches to comparison that not simply establish relationships between observable facts or pre-existing cases; rather, the comparative logic aims at establishing “relations between sets of relationships”, as argued by Jürgen Schriewer:

[the] specific method of science dissociates comparison from its quasi-natural union with resemblances; the interest in identifying similarities shifts from the level of factual contents to the level of generalizable relationships. […] One of the primary ways of extending their scope, or examining their explanatory power, is the controlled introduction of varying sets of conditions. The logic of relating relationships, which distinguishes the scientific method of comparison, comes close to meeting these requirements by systematically exploring and analysing sociocultural differences with respect to scrutinizing the credibility of theories, models or constructs (Schriewer, 1990 , p. 36).

The notion of establishing relations between sets of relationships allows to treat cases not as homogeneous (thus avoiding a universalizing notion of comparison); it establishes comparability not along similarity but based on conceptual, functional and/or theoretical equivalences and focuses on reconstructing ‘varying sets of conditions’ that are seen as relevant in social scientific explanation and theorizing, and to which then comparative case studies may contribute.

The following section aims presents the adaptation and application of a comparative case study approach in the YOUNG_ADULLLT research project.

3 Exploring Landscapes of Lifelong Learning through Case Studies

This section illustrates the usage of comparative case studies by drawing from research conducted in a European research project upon which the chapters in this volume are based. The project departed from the observation that most current European lifelong learning (LLL) policies have been designed to create economic growth and, at the same time, guarantee social inclusion and argued that, while these objectives are complementary, they are, however, not linearly nor causally related and, due to distinct orientations, different objectives, and temporal horizons, conflicts and ambiguities may arise. The project was designed as a mixed-method comparative study and aimed at results at the national, regional, and local levels, focusing in particular on policies targeting young adults in situations of near social exclusion. Using a multi-level approach with qualitative and quantitative methods, the project conducted, amongst others, local/regional 18 case studies of lifelong learning policies through a multi-method and multi-level design (see Parreira do Amaral et al., 2020 for more information). The localisation of the cases in their contexts was carried out by identifying relevant areas in terms of spatial differentiation and organisation of social and economic relations. The so defined “functional regions” allowed focus on territorial units which played a central role within their areas, not necessarily overlapping with geographical and/or administrative borders. Footnote 1

Two main objectives guided the research: first, to analyse policies and programmes at the regional and local level by identifying policymaking networks that included all social actors involved in shaping, formulating, and implementing LLL policies for young adults; second, to recognize strengths and weaknesses (overlapping, fragmented or unfocused policies and projects), thus identifying different patterns of LLL policymaking at regional level, and investigating their integration with the labour market, education and other social policies. The European research project focused predominantly on the differences between the existing lifelong learning policies in terms of their objectives and orientations and questioned their impact on young adults’ life courses, especially those young adults who find themselves in vulnerable positions. What concerned the researchers primarily was the interaction between local institutional settings, education, labour markets, policymaking landscapes, and informal initiatives that together nurture the processes of lifelong learning. They argued that it is by inquiring into the interplay of these components that the regional and local contexts of lifelong learning policymaking can be better assessed and understood. In this regard, the multi-layered approach covered a wide range of actors and levels and aimed at securing compatibility throughout the different phases and parts of the research.

The multi-level approach adopted aimed at incorporating the different levels from transnational to regional/local to individual, that is, the different places, spaces, and levels with which policies are related. The multi-method design was used to bring together the results from the quantitative, qualitative and policy/document analysis (for a discussion: Parreira do Amaral, 2020 ).

Studying the complex relationships between lifelong learning (LLL) policymaking on the one hand, and young adults’ life courses on the other, requires a carefully established research approach. This task becomes even more challenging in the light of the diverse European countries and their still more complex local and regional structures and institutions. One possible way of designing a research framework able to deal with these circumstances clearly and coherently is to adopt a multi-level or multi-layered approach. This approach recognises multiple levels and patterns of analysis and enables researchers to structure the workflow according to various perspectives. It was this multi-layered approach that the research consortium of YOUNG_ADULLLT adopted and applied in its attempts to better understand policies supporting young people in their life course.

3.1 Constructing Case Studies

In constructing case studies, the project did not apply an instrumental approach focused on the assessment of “what worked (or not)?” Rather, consistently with Bartlett and Vavrus’s proposal (Bartlett & Vavrus, 2017 ), the project decided to “understand policy as a deeply political process of cultural production engaged in and shaped by social actors in disparate locations who exert incongruent amounts of influence over the design, implementation, and evaluation of policy” ( Bartlett & Vavrus, 2017 , p. 1f). This was done in order to enhance the interactive and relational dimension among actors and levels, as well as their embeddedness in local infra-structures (education, labour, social/youth policies) according to project’s three theoretical perspectives. The analyses of the information and data integrated by our case study approach aimed at a cross-reading of the relations among the macro socio-economic dimensions, structural arrangements, governance patterns, addressee biographies and mainstream discourses that underlie the process of design and implementation of the LLL policies selected as case study. The subjective dimensions of agency and sense-making animated these analyses, and the multi-level approach contextualized them from the local to the transnational levels. Figure 3.1 below represents the analytical approach to the research material gathered in constructing the case studies. Specifically, it shows the different levels, from the transnational level down to the addressees.

Multi-level and multi-method approach to case studies in YOUNG_ADULLLT. Source: Palumbo et al., 2019

The project partners aimed at a cross-dimensional construction of the case studies, and this implied the possibility of different entry points, for instance by moving the analytical perspective top-down or bottom-up, as well as shifting from left to right of the matrix and vice versa. Considering the “horizontal movement”, the multidimensional approach has enabled taking into consideration the mutual influence and relations among the institutional, individual, and structural dimensions (which in the project corresponded to the theoretical frames of CPE, LCR, and GOV). In addition, the “vertical movement” from the transnational to the individual level and vice versa was meant to carefully carry out a “study of flows of influence, ideas, and actions through these levels” (Bartlett and Vavrus 2017 , p. 11), emphasizing the correspondences/divergences among the perspectives of different actors at different levels. The transversal dimension, that is, the historical process, focused on the period after the financial crisis of 2007/2008 as it has impacted differently on the social and economic situations of young people, often resulting in stern conditions and higher competition in education and labour markets, which also called for a reassessment of existing policies targeting young adults in the countries studied.

Concerning the analyses, a further step included the translation of the conceptual model illustrated in Fig. 3.1 above into a heuristic table used to systematically organize the empirical data collected and guide the analyses cases constructed as multi-level and multidimensional phenomena, allowing for the establishment of interlinkages and relationships. By this approach, the analysis had the possibility of grasping the various levels at which LLL policies are negotiated and displaying the interplay of macro-structures, regional environments and institutions/organizations as well as individual expectations. Table 3.1 illustrates the operationalization of the data matrix that guided the work.

In order to ensure the presentability and intelligibility of the results, Footnote 2 a narrative approach to case studies analysis was chosen whose main task was one of “storytelling” aimed at highlighting what made each case unique and what difference it makes for LLL policymaking and to young people’s life courses. A crucial element of this entails establishing relations “between sets of relationships”, as argued above.

LLL policies were selected as starting points from which the cases themselves could be constructed and of which different stories could be developed. That stories can be told differently does not mean that they are arbitrary, rather this refers to different ways of accounting for the embedding of the specific case to its context, namely the “diverging policy frameworks, patterns of policymaking, networks of implementation, political discourses and macro-structural conditions at local level” (see Palumbo et al., 2020 , p. 220). Moreover, developing different narratives aimed at representing the various voices of the actors involved in the process—from policy-design and appropriation through to implementation—and making the different stakeholders’ and addressees’ opinions visible, creating thus intelligible narratives for the cases (see Palumbo et al., 2020 ). Analysing each case started from an entry point selected, from which a story was told. Mainly, two entry points were used: on the one hand, departing from the transversal dimension of the case and which focused on the evolution of a policy in terms of its main objectives, target groups, governance patterns and so on in order to highlight the intended and unintended effects of the “current version” of the policy within its context and according to the opinions of the actors interviewed. On the other hand, biographies were selected as starting points in an attempt to contextualize the life stories within the biographical constellations in which the young people came across the measure, the access procedures, and how their life trajectories continued in and possibly after their participation in the policy (see Palumbo et al., 2020 for examples of these narrative strategies).

4 Concluding Remarks

This chapter presented and discussed the methodological basis and requirements of conducting case studies in comparative research, such as those presented in the subsequent chapters of this volume. The Comparative Case Study approach suggested in the previous discussion offers productive and innovative ways to account sensitively to culture and contexts; it provides a useful heuristic that deals effectively with issues related to case construction, namely an emergent and dynamic approach to casing, instead of simply assuming “bounded”, pre-defined cases as the object of research; they also offer a helpful procedural, configurational approach to “causality”; and, not least, a resourceful approach to comparison that allows researchers to respect the uniqueness and integrity of each case while at the same time yielding insights and results that transcend the idiosyncrasy of the single case. In sum, CCS offers a sound approach to CIE research that is culture and context sensitive.

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do Amaral, M.P. (2022). Comparative Case Studies: Methodological Discussion. In: Benasso, S., Bouillet, D., Neves, T., Parreira do Amaral, M. (eds) Landscapes of Lifelong Learning Policies across Europe. Palgrave Studies in Adult Education and Lifelong Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96454-2_3

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Characteristics of a Comparative Research Design

Hannah richardson, 28 jun 2018.

Comparative research essentially compares two groups in an attempt to draw a conclusion about them. Researchers attempt to identify and analyze similarities and differences between groups, and these studies are most often cross-national, comparing two separate people groups. Comparative studies can be used to increase understanding between cultures and societies and create a foundation for compromise and collaboration. These studies contain both quantitative and qualitative research methods.

Explore this article

• Comparative Quantitative
• Comparative Qualitative
• When to Use It
• When Not to Use It

1 Comparative Quantitative

Quantitative, or experimental, research is characterized by the manipulation of an independent variable to measure and explain its influence on a dependent variable. Because comparative research studies analyze two different groups -- which may have very different social contexts -- it is difficult to establish the parameters of research. Such studies might seek to compare, for example, large amounts of demographic or employment data from different nations that define or measure relevant research elements differently.

However, the methods for statistical analysis of data inherent in quantitative research are still helpful in establishing correlations in comparative studies. Also, the need for a specific research question in quantitative research helps comparative researchers narrow down and establish a more specific comparative research question.

2 Comparative Qualitative

Qualitative, or nonexperimental, is characterized by observation and recording outcomes without manipulation. In comparative research, data are collected primarily by observation, and the goal is to determine similarities and differences that are related to the particular situation or environment of the two groups. These similarities and differences are identified through qualitative observation methods. Additionally, some researchers have favored designing comparative studies around a variety of case studies in which individuals are observed and behaviors are recorded. The results of each case are then compared across people groups.

3 When to Use It

Comparative research studies should be used when comparing two people groups, often cross-nationally. These studies analyze the similarities and differences between these two groups in an attempt to better understand both groups. Comparisons lead to new insights and better understanding of all participants involved. These studies also require collaboration, strong teams, advanced technologies and access to international databases, making them more expensive. Use comparative research design when the necessary funding and resources are available.

4 When Not to Use It

Do not use comparative research design with little funding, limited access to necessary technology and few team members. Because of the larger scale of these studies, they should be conducted only if adequate population samples are available. Additionally, data within these studies require extensive measurement analysis; if the necessary organizational and technological resources are not available, a comparative study should not be used. Do not use a comparative design if data are not able to be measured accurately and analyzed with fidelity and validity.

• 1 San Jose State University: Selected Issues in Study Design
• 2 University of Surrey: Social Research Update 13: Comparative Research Methods

About the Author

Hannah Richardson has a Master's degree in Special Education from Vanderbilt University and a Bacheor of Arts in English. She has been a writer since 2004 and wrote regularly for the sports and features sections of "The Technician" newspaper, as well as "Coastwach" magazine. Richardson also served as the co-editor-in-chief of "Windhover," an award-winning literary and arts magazine. She is currently teaching at a middle school.

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Comparative Research

Ai generator.

Although not everyone would agree, comparing is not always bad. Comparing things can also give you a handful of benefits. For instance, there are times in our life where we feel lost. You may not be getting the job that you want or have the sexy body that you have been aiming for a long time now. Then, you happen to cross path with an old friend of yours, who happened to get the job that you always wanted. This scenario may put your self-esteem down, knowing that this friend got what you want, while you didn’t. Or you can choose to look at your friend as an example that your desire is actually attainable. Come up with a plan to achieve your  personal development goal . Perhaps, ask for tips from this person or from the people who inspire you. According to the article posted in  brit.co , licensed master social worker and therapist Kimberly Hershenson said that comparing yourself to someone successful can be an excellent self-motivation to work on your goals.

Aside from self-improvement, as a researcher, you should know that comparison is an essential method in scientific studies, such as experimental research and descriptive research . Through this method, you can uncover the relationship between two or more variables of your project in the form of comparative analysis .

What is Comparative Research?

Aiming to compare two or more variables of an experiment project, experts usually apply comparative research examples in social sciences to compare countries and cultures across a particular area or the entire world. Despite its proven effectiveness, you should keep it in mind that some states have different disciplines in sharing data. Thus, it would help if you consider the affecting factors in gathering specific information.

Quantitative and Qualitative Research Methods in Comparative Studies

In comparing variables, the statistical and mathematical data collection, and analysis that quantitative research methodology naturally uses to uncover the correlational connection of the variables, can be essential. Additionally, since quantitative research requires a specific research question, this method can help you can quickly come up with one particular comparative research question.

The goal of comparative research is drawing a solution out of the similarities and differences between the focused variables. Through non-experimental or qualitative research , you can include this type of research method in your comparative research design.

13+ Comparative Research Examples

Know more about comparative research by going over the following examples. You can download these zipped documents in PDF and MS Word formats.

1. Comparative Research Report Template

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2. Business Comparative Research Template

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10. Comparative Research in Medical Treatments Example

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14. Casual Comparative Research in DOC

Best Practices in Writing an Essay for Comparative Research in Visual Arts

If you are going to write an essay for a comparative research examples paper, this section is for you. You must know that there are inevitable mistakes that students do in essay writing . To avoid those mistakes, follow the following pointers.

1. Compare the Artworks Not the Artists

One of the mistakes that students do when writing a comparative essay is comparing the artists instead of artworks. Unless your instructor asked you to write a biographical essay, focus your writing on the works of the artists that you choose.

2. Consult to Your Instructor

There is broad coverage of information that you can find on the internet for your project. Some students, however, prefer choosing the images randomly. In doing so, you may not create a successful comparative study. Therefore, we recommend you to discuss your selections with your teacher.

3. Avoid Redundancy

It is common for the students to repeat the ideas that they have listed in the comparison part. Keep it in mind that the spaces for this activity have limitations. Thus, it is crucial to reserve each space for more thoroughly debated ideas.

4. Be Minimal

Unless instructed, it would be practical if you only include a few items(artworks). In this way, you can focus on developing well-argued information for your study.

5. Master the Assessment Method and the Goals of the Project

We get it. You are doing this project because your instructor told you so. However, you can make your study more valuable by understanding the goals of doing the project. Know how you can apply this new learning. You should also know the criteria that your teachers use to assess your output. It will give you a chance to maximize the grade that you can get from this project.

Comparing things is one way to know what to improve in various aspects. Whether you are aiming to attain a personal goal or attempting to find a solution to a certain task, you can accomplish it by knowing how to conduct a comparative study. Use this content as a tool to expand your knowledge about this research methodology .

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In 2019 UCLA and MIT received a $453,000 Science, Technology, and Society program grant to support a collaborative research project entitled, Network Sovereignty: A Comparative Study of Local Network Initiatives in Rural, Low-income Communities. Using community-based research, the project investigated local telecom network initiatives in the Blackfeet Nation in Montana, Oaxaca, Mexico, and Bunda, Tanzania with the aim of assessing whether and how rural, low-income communities are empowered by network infrastructures. The project delivered four main insights and outcomes described below.

First, our comparative research revealed that communities which assert ownership over local infrastructure, embed network initiatives within local cultures, and prioritize digital education are much more likely to build and sustain local networks that support economic, political, and cultural lives. To support future network development in local communities, we formulated a Local Network Sovereignty Assessment Matrix to help evaluate local network initiatives across several categories, including community engagement, local cultures/ontologies economic ownership, digital education/technological knowledge, economic ownership, and community empowerment. This matrix, available below and in one of our published peer-reviewed articles, can be used in the future by community organizations, advocacy groups, and/or researchers to assess local network initiatives.

Second, our project expands understandings of network sovereignty--a term that refers to varying degrees of ownership, knowledge, and empowerment that local organizations and users experience in relation to Internet services and information and communication technologies (ICTs) in their communities.  Our research revealed that pactices and perceptions of local governance are crucial to mounting and sustaining meaningful and empowering network sovereignty initiatives. Historical experiences and variable understandings of sovereignty also determine whether people engage with, trust in, and support local network sovereignty initiatives. 

Third, our research emphasizes the technological knowledge, resourcefulness, and creativity that can emerge in rural, low-income communities that are far removed from centers of technological capital and power. It reveals how diverse peoples make sense of, appropriate, subvert, and design network technologies in efforts to imagine and shape their own sovereignty practices.

Finally, our research emerged from collaborations with Indigenous peoples (Blackfeet/Pikuni, Mije, Mixtec, Zapotec, Ikizu) and students from underrepresented minority backgrounds whose perspectives deepened and diversified understandings of network infrastructure and its relation to community empowerment. We published articles in four peer-reviewed journals and two chapters in scholarly books and wrote a series of press articles and policy recommendations. We also created and maintain the publicly available Network Sovereignty Blog , which features 24 short essays by researchers, graduate students, and community IT specialists. The blog interlinks early career researchers who are studying network infrastructures and raises awareness about their research projects. 

Last Modified: 11/30/2023 Modified by: Lisa Parks

Please report errors in award information by writing to: [email protected] .

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A Comparative Study of DC Beads, Callispheres and Multimodal Imaging Nano-Assembled Microspheres Loaded with Irinotecan in Vitro

Affiliations.

• 1 Wenzhou Medical University, Wenzhou, China.
• 2 Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
• 3 Zhejiang University of Technology, Hangzhou, China.
• 4 School of Automation, Hangzhou Dianzi University, Hangzhou, China.
• PMID: 39149935
• DOI: 10.1177/15330338241274289

Introduction: In recent years, the development of drug-eluting embolization beads that can be imaged has become a hot research topic in regard to meeting clinical needs. In our previous study, we successfully developed nano-assembled microspheres (NAMs) for multimodal imaging purposes. NAMs can not only be visualized under CT/MR/Raman imaging but can also load clinically required doses of doxorubicin. It is important to systematically compare the pharmacokinetics of NAMs with those of commercially available DC Beads and CalliSpheres to evaluate the clinical application potential of NAMs. Methods: In our study, we compared NAMs with two types of drug-eluting beads (DEBs) in terms of irinotecan, drug-loading capacity, release profiles, microsphere diameter variation, and morphological characteristics. Results: Our results indicate that NAMs had an irinotecan loading capacity similar to those of DC Beads and CalliSpheres but exhibited better sustained release in vitro. Conclusion: NAMs have great potential for application in transcatheter arterial chemoembolization for the treatment of colorectal cancer liver metastases.

Keywords: comparative study; drug loading; drug release; drug-eluting beads; irinotecan.

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Conflict of interest statement

Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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• In vitro comparative study of multimodal imaging nano-assembled microspheres with two clinical drug-eluting beads loaded with doxorubicin. He Y, Xu T, Ding T, Gong Y, Zeng H, Xi Z, Ye Y, Song Z, Pan T, Zhang Z, Ma Q, Li L, Zhang Y, Shao G. He Y, et al. Drug Deliv. 2023 Dec;30(1):2197177. doi: 10.1080/10717544.2023.2197177. Drug Deliv. 2023. PMID: 37078789 Free PMC article.
• Stability of irinotecan-loaded drug eluting beads (DC Bead) used for transarterial chemoembolization. Kaiser J, Thiesen J, Krämer I. Kaiser J, et al. J Oncol Pharm Pract. 2010 Mar;16(1):53-61. doi: 10.1177/1078155209337650. Epub 2009 Jul 17. J Oncol Pharm Pract. 2010. PMID: 19617306
• Characterization of a novel intrinsically radiopaque Drug-eluting Bead for image-guided therapy: DC Bead LUMI™. Ashrafi K, Tang Y, Britton H, Domenge O, Blino D, Bushby AJ, Shuturminska K, den Hartog M, Radaelli A, Negussie AH, Mikhail AS, Woods DL, Krishnasamy V, Levy EB, Wood BJ, Willis SL, Dreher MR, Lewis AL. Ashrafi K, et al. J Control Release. 2017 Mar 28;250:36-47. doi: 10.1016/j.jconrel.2017.02.001. Epub 2017 Feb 8. J Control Release. 2017. PMID: 28188808 Free PMC article.
• Toward a better understanding of the mechanism of action for intra-arterial delivery of irinotecan from DC Bead (TM) (DEBIRI). Lewis AL, Hall B. Lewis AL, et al. Future Oncol. 2019 Jun;15(17):2053-2068. doi: 10.2217/fon-2019-0071. Epub 2019 Apr 3. Future Oncol. 2019. PMID: 30942614 Review.
• Transarterial chemoembolization with irinotecan beads in the treatment of colorectal liver metastases: systematic review. Richardson AJ, Laurence JM, Lam VW. Richardson AJ, et al. J Vasc Interv Radiol. 2013 Aug;24(8):1209-17. doi: 10.1016/j.jvir.2013.05.055. J Vasc Interv Radiol. 2013. PMID: 23885916 Review.

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Kennesaw State physics major pursues life-changing research

KENNESAW, Ga. | Aug 19, 2024

The rising junior from Smyrna said classes in astronomy and physics taught her how physical forces effect everything around her, and she wanted to immerse herself in them. So, she came to Kennesaw State University for the opportunity to conduct research right away as a freshman.

“ Honestly, the research opportunities drew me to KSU,” said Manqueros, who is pursuing a bachelor’s degree in physics in Kennesaw State’s College of Science and Mathematics . “Other colleges mainly take graduate students for their research, and I knew that at KSU I could do meaningful research even if I was an undergraduate student.”

That desire for meaningful research drew her to the lab of associate professor Kisa Ranasinghe, who creates bioactive glass that can transport nanoparticles that treat various ailments. Manqueros approached Ranasinghe at an early-semester meeting for physics majors after hearing the professor discuss her work; Manqueros was hooked, and Ranasinghe was impressed.

“When someone stops me to say they’re interested in my research and want to learn more, that’s an indicator, right?” Ranasinghe said. “For a freshman to take that initiative and show that amount of enthusiasm is truly impressive. Very quickly I found out she has great potential.”

From that day forward, Manqueros poured herself into the life-changing research into bioglass, which isn’t really glass but a conduit that acts like glass to bring therapeutic nanoparticles into the body. Manqueros said cerium oxide nanoparticles within the bioglass can interact to treat Alzheimer’s disease, cancer, diabetes, and other physical and neurological conditions. The first part of her explanation, though, involves demystifying the idea of glass in the body.

“A lot of times when we say we're doing research on glass that we can put into your body, people freak out because they imagine the glass breaking—it’s not like that,” she explained. “The simple fact that we work with glass to better people's health—that's something that I really want to get across to people. What we do from the physics point of view is study those nanoparticles and how they interact within the glass.”

Manqueros will investigate these problems as a Birla Carbon Scholar this summer. She has also been the lead author on an abstract for a poster presentation that published earlier this year in the Georgia Journal of Science, and she presented findings at the Georgia Academy of Science conference in March, where she won first prize for undergraduate oral presentations in the division that covers physics, mathematics, computer science, and engineering.

Ranasinghe said Manqueros’ future is wide-open, though Manqueros said the future will involve more physics, either a master’s degree or a doctorate while continuing the research into bioglass. Life-changing research with societal impact will keep her engaged for a long time to come, she said.

“I actually enjoy what I do,” she said. “Oftentimes when you're doing work as a physicist, people don't see the meaning in what you do because they wonder why we need to study this. This research is impacting anyone who has some sort of disease or wants to improve their health or their body. I like that I have a direct impact on people's lives through the research that I do here at KSU.”

– Story by Dave Shelles

Photos by Matt Yung

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A leader in innovative teaching and learning, Kennesaw State University offers undergraduate, graduate and doctoral degrees to its more than 45,000 students. Kennesaw State is a member of the University System of Georgia with 11 academic colleges. The university’s vibrant campus culture, diverse population, strong global ties and entrepreneurial spirit draw students from throughout the country and the world. Kennesaw State is a Carnegie-designated doctoral research institution (R2), placing it among an elite group of only 7 percent of U.S. colleges and universities with an R1 or R2 status. For more information, visit kennesaw.edu .

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Massive biomolecular shifts occur in our 40s and 60s, Stanford Medicine researchers find

Time marches on predictably, but biological aging is anything but constant, according to a new Stanford Medicine study.

August 14, 2024 - By Rachel Tompa

We undergo two periods of rapid change, averaging around age 44 and age 60, according to a Stanford Medicine study. Ratana21 /Shutterstock.com

If it’s ever felt like everything in your body is breaking down at once, that might not be your imagination. A new Stanford Medicine study shows that many of our molecules and microorganisms dramatically rise or fall in number during our 40s and 60s.

Researchers assessed many thousands of different molecules in people from age 25 to 75, as well as their microbiomes — the bacteria, viruses and fungi that live inside us and on our skin — and found that the abundance of most molecules and microbes do not shift in a gradual, chronological fashion. Rather, we undergo two periods of rapid change during our life span, averaging around age 44 and age 60. A paper describing these findings was published in the journal Nature Aging Aug. 14.

“We’re not just changing gradually over time; there are some really dramatic changes,” said Michael Snyder , PhD, professor of genetics and the study’s senior author. “It turns out the mid-40s is a time of dramatic change, as is the early 60s. And that’s true no matter what class of molecules you look at.”

Xiaotao Shen, PhD, a former Stanford Medicine postdoctoral scholar, was the first author of the study. Shen is now an assistant professor at Nanyang Technological University Singapore.

These big changes likely impact our health — the number of molecules related to cardiovascular disease showed significant changes at both time points, and those related to immune function changed in people in their early 60s.

Abrupt changes in number

Snyder, the Stanford W. Ascherman, MD, FACS Professor in Genetics, and his colleagues were inspired to look at the rate of molecular and microbial shifts by the observation that the risk of developing many age-linked diseases does not rise incrementally along with years. For example, risks for Alzheimer’s disease and cardiovascular disease rise sharply in older age, compared with a gradual increase in risk for those under 60.

The researchers used data from 108 people they’ve been following to better understand the biology of aging. Past insights from this same group of study volunteers include the discovery of four distinct “ ageotypes ,” showing that people’s kidneys, livers, metabolism and immune system age at different rates in different people.

Michael Snyder

The new study analyzed participants who donated blood and other biological samples every few months over the span of several years; the scientists tracked many different kinds of molecules in these samples, including RNA, proteins and metabolites, as well as shifts in the participants’ microbiomes. The researchers tracked age-related changes in more than 135,000 different molecules and microbes, for a total of nearly 250 billion distinct data points.

They found that thousands of molecules and microbes undergo shifts in their abundance, either increasing or decreasing — around 81% of all the molecules they studied showed non-linear fluctuations in number, meaning that they changed more at certain ages than other times. When they looked for clusters of molecules with the largest changes in amount, they found these transformations occurred the most in two time periods: when people were in their mid-40s, and when they were in their early 60s.

Although much research has focused on how different molecules increase or decrease as we age and how biological age may differ from chronological age, very few have looked at the rate of biological aging. That so many dramatic changes happen in the early 60s is perhaps not surprising, Snyder said, as many age-related disease risks and other age-related phenomena are known to increase at that point in life.

The large cluster of changes in the mid-40s was somewhat surprising to the scientists. At first, they assumed that menopause or perimenopause was driving large changes in the women in their study, skewing the whole group. But when they broke out the study group by sex, they found the shift was happening in men in their mid-40s, too.

“This suggests that while menopause or perimenopause may contribute to the changes observed in women in their mid-40s, there are likely other, more significant factors influencing these changes in both men and women. Identifying and studying these factors should be a priority for future research,” Shen said.

Changes may influence health and disease risk

In people in their 40s, significant changes were seen in the number of molecules related to alcohol, caffeine and lipid metabolism; cardiovascular disease; and skin and muscle. In those in their 60s, changes were related to carbohydrate and caffeine metabolism, immune regulation, kidney function, cardiovascular disease, and skin and muscle.

It’s possible some of these changes could be tied to lifestyle or behavioral factors that cluster at these age groups, rather than being driven by biological factors, Snyder said. For example, dysfunction in alcohol metabolism could result from an uptick in alcohol consumption in people’s mid-40s, often a stressful period of life.

The team plans to explore the drivers of these clusters of change. But whatever their causes, the existence of these clusters points to the need for people to pay attention to their health, especially in their 40s and 60s, the researchers said. That could look like increasing exercise to protect your heart and maintain muscle mass at both ages or decreasing alcohol consumption in your 40s as your ability to metabolize alcohol slows.

“I’m a big believer that we should try to adjust our lifestyles while we’re still healthy,” Snyder said.

The study was funded by the National Institutes of Health (grants U54DK102556, R01 DK110186-03, R01HG008164, NIH S10OD020141, UL1 TR001085 and P30DK116074) and the Stanford Data Science Initiative.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Our bodies age in rapid bursts when we hit our mid-40s and early 60s, new study shows

We may not age in a linear fashion, but instead in bursts, including in our mid-40s and early 60s, suggests new research analyzing molecules in the body from stanford university..

So much for the idea of aging gracefully – or gradually. New research suggests the body ages in bursts, in particular, rapid changes about age 44 and another when we hit 60.

We consider aging as a constant process, but our bodies may actually age in a non-linear fashion, with certain types of molecules in our body increasing or decreasing dramatically when people hit the mid-40s, and again in the early 60s, according to research published Aug. 14 in the journal Nature Aging .

“We’re not just changing gradually over time; there are some really dramatic changes,” said the study's senior author Michael Snyder, a professor of genetics at the Stanford University School of Medicine, in a description of the research on the university site . “It turns out the mid-40s is a time of dramatic change, as is the early 60s. And that’s true no matter what class of molecules you look at.”

Rocket fuel: Are you consuming it? Consumer Reports study finds chemical in food, water

What did new aging research find?

Researchers studied data from 108 study participants who donated blood and other samples every few months over several years. They analyzed age-related changes in more than 135,000 different molecules, proteins and microbes.

The findings: thousands of these molecules and microorganisms – about 81% of those analyzed – increased or decreased more at certain ages. Researchers found molecules with the largest changes happened the most when people were in their mid-40s and their early 60s.

Finding the changes in the early 60s was not that surprising, because increased risks of diseases occur at that time, Snyder said. But the changes in the mid-40s happening equally in women and men was surprising.

“This suggests that while menopause or perimenopause may contribute to the changes observed in women in their mid-40s, there are likely other, more significant factors influencing these changes in both men and women," said Xiaotao Shen, first author on the study and a former Stanford Medicine postdoctoral scholar, now an assistant professor at Nanyang Technological University in Singapore, in the research summation.

"Identifying and studying these factors should be a priority for future research,” Shen said.

Specific findings by age group:

• Mid-40s: "Significant changes" were found in the amount of molecules related to cardiovascular disease, skin and muscle, and alcohol, caffeine and lipid metabolism.
• The 60s: Changes found at this age were related to carbohydrate and caffeine metabolism, immune regulation, kidney function, cardiovascular disease, and skin and muscle.

What advice do experts have based on this new aging research?

For those reaching the age when the aging "bursts" occur, those in their mid-40s and early 60s should "keep your exercise up to improve your muscle," Snyder told USA TODAY in an email conversation.

"In the 40s, watch and control your lipids to prevent atherosclerosis and maybe improve your fat deposition," he said.

As for increased alcohol metabolism, researchers haven't identified a cause and effect link to the changes and health implications, but an uptick in alcohol consumption in people’s mid-40s could be the culprit, Snyder said.

As folks approach their 60s, exercise remains important for muscle improvement and "is likely to improve the cardiovascular disease markers that we see change at both times." he said.

Also those hitting their 60s should watch carbohydrate intake and drink lots of water "to keep your kidney function up," Snyder said.

The research team plans to continue its work on the biology of aging, with hopes of improving guidance for people. “I’m a big believer that we should try to adjust our lifestyles while we’re still healthy,” Snyder said.

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Older Adults Do Not Benefit From Moderate Drinking, Large Study Finds

Virtually any amount increased the risk for cancer, and there were no heart benefits, the researchers reported.

By Roni Caryn Rabin

Even light drinking was associated with an increase in cancer deaths among older adults in Britain, researchers reported on Monday in a large study. But the risk was accentuated primarily in those who had existing health problems or who lived in low-income areas.

The study, which tracked 135,103 adults aged 60 and older for 12 years, also punctures the long-held belief that light or moderate alcohol consumption is good for the heart.

The researchers found no reduction in heart disease deaths among light or moderate drinkers, regardless of this health or socioeconomic status, when compared with occasional drinkers.

The study defined light drinking as a mean alcohol intake of up to 20 grams a day for men and up to 10 grams daily for women. (In the United States, a standard drink is 14 grams of alcohol .)

“We did not find evidence of a beneficial association between low drinking and mortality,” said Dr. Rosario Ortolá, an assistant professor of preventive medicine and public health at Universidad Autónoma de Madrid and the lead author of the paper, which was published in JAMA Network Open.

On the other hand, she added, alcohol probably raises the risk of cancer “from the first drop.”

The findings add to a mounting body of evidence that is shifting the paradigm in alcohol research. Scientists are turning to new methodologies to analyze the risks and benefits of alcohol consumption in an attempt to correct what some believe were serious flaws in earlier research, which appeared to show that there were benefits to drinking.

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Research shows our bodies go through rapid changes in our 40s and our 60s

For many people, reaching their mid-40s may bring unpleasant signs the body isn’t working as well as it once did. Injuries seem to happen more frequently. Muscles may feel weaker.

A new study, published Wednesday in Nature Aging , shows what may be causing the physical decline. Researchers have found that molecules and microorganisms both inside and outside our bodies are going through dramatic changes, first at about age 44 and then again when we hit 60. Those alterations may be causing significant differences in cardiovascular health and immune function.

The findings come from Stanford scientists who analyzed blood and other biological samples of 108 volunteers ages 25 to 75, who continued to donate samples for several years. 

“While it’s obvious that you’re aging throughout your entire life, there are two big periods where things really shift,” said the study’s senior author, Michael Snyder, a professor of genetics and director of the Center for Genomics and Personalized Medicine at Stanford Medicine. For example, “there’s a big shift in the metabolism of lipids when people are in their 40s and in the metabolism of carbohydrates when people are in their 60s.”

Lipids are fatty substances, including LDL, HDL and triglycerides, that perform a host of functions in the body, but they can be harmful if they build up in the blood.

The scientists tracked many kinds of molecules in the samples, including RNA and proteins, as well as the participants’ microbiomes.

The metabolic changes the researchers discovered indicate not that people in their 40s are burning calories more slowly but rather that the body is breaking food down differently. The scientists aren’t sure exactly what impact those changes have on health.

Previous research showed that resting energy use, or metabolic rate , didn’t change from ages 20 to 60. The new study’s findings don't contradict that.

The changes in metabolism affect how the body reacts to alcohol or caffeine, although the health consequences aren’t yet clear. In the case of caffeine, it may result in higher sensitivity. 

It’s also not known yet whether the shifts could be linked to lifestyle or behavioral factors. For example, the changes in alcohol metabolism might be because people are drinking more in their mid-40s, Snyder said.

For now, Snyder suggests people in their 40s keep a close eye on their lipids, especially LDL cholesterol.

“If they start going up, people might want to think about taking statins if that’s what their doctor recommends,” he said. Moreover, “knowing there’s a shift in the molecules that affect muscles and skin, you might want to warm up more before exercising so you don’t hurt yourself.”

Until we know better what those changes mean, the best way to deal with them would be to eat healthy foods and to exercise regularly, Snyder said.Dr. Josef Coresh, founding director of the Optimal Aging Institute at the NYU Grossman School of Medicine, compared the new findings to the invention of the microscope.

“The beauty of this type of paper is the level of detail we can see in molecular changes,” said Coresh, a professor of medicine at the school. “But it will take time to sort out what individual changes mean and how we can tailor medications to those changes. We do know that the origins of many diseases happen in midlife when people are in their 40s, though the disease may occur decades later.”

The new study “is an important step forward,” said Dr. Lori Zeltser, a professor of pathology and cell biology at the Columbia University Vagelos College of Physicians and Surgeons. While we don’t know what the consequences of those metabolic changes are yet, “right now, we have to acknowledge that we metabolize food differently in our 40s, and that is something really new.”

The shifts the researchers found might help explain numerous age-related health changes, such as muscle loss, because “your body is breaking down food differently,” Zeltser said.

Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings."