## What is an Antecedent Variable? (Explanation & Example)

In statistics, researchers are often interested in understanding the relationship between some independent variable and a dependent variable.

However, sometimes an  antecedent variable  can be present.

An antecedent variable is a variable that occurs  before  the independent and dependent variables under study and can help explain the relationship between the two.

You can remember this definition by remembering that the word antecedent literally means “previous or preexisting.”

## Examples of Antecedent Variables

Antecedent variables can be present in a variety of research scenarios. Some examples include:

Example 1: Age & Income

Suppose researchers are interested in studying the relationship between age and annual income. However, an antecedent variable that could help explain (or partially explain) the relationship between the two variables that should be considered is education level , since this tends to have a correlation with both age and income.

Example 2: Meditation & Happiness

Suppose researchers are interested in studying the relationship between meditation and reported happiness levels. However, an antecedent variable that could help explain (or partially explain) the relationship between the two variables that should be considered is work stress , since this can have an effect on both free time available to meditate and reported happiness.

## How to Control for Antecedent Variables

In an experiment, researchers could potentially control for antecedent variables by using them as blocking factors . For example, they could divide participants into “blocks” based on their education level, then study the relationship between age and income with each block.

In regression analysis, researchers could include antecedent variables in a regression model to control for their effects. For example, researchers could include education level as a variable in the regression model so that the regression coefficient for age could be interpreted as the average change in income while holding education level constant.

In both of these scenarios, it’s assumed that data is readily available for these antecedent variables which isn’t always the case. For example, it could be hard to quantify “work stress” even though we know it might be an antecedent variable that could affect ability to meditate and reported happiness.

## Related Variables

Two variables that are similar to antecedent variables and that can also affect the relationship between an independent variable and dependent variable include:

1. Extraneous variables : Variables that are not of interest in a study, but can affect both the independent and dependent variables.

2. Intervening variables : Variables that come between independent and dependent variables and have a direct effect on the relationship between the two.

Be wary of each of these types of variables when conducting an experiment or a study.

## Standardized vs. Unstandardized Regression Coefficients

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## Political Science Research Methods

Student resources, chapter summary.

After choosing a research question, the initial steps of an empirical research project include the following:

• proposing a suitable explanation for the phenomena under study
• formulating testable hypotheses
• defining the concepts identified in the hypotheses

Variables are used to specify how two or more variables are related in an effort to explain the phenomena of interest.

• An independent variable is thought to influence, affect, or cause variation in another variable.
• A dependent variable is thought to depend upon or be caused by variation in an independent variable.
• A variable is a concept with variation, while a constant is a concept without variation.
• In general, more than one independent variable is needed to adequately explain political phenomena.
• A variable that occurs prior to all other variables is referred to as an antecedent variable, while a variable that occurs closer in time to the dependent variable is called an intervening variable .
• An arrow diagram can be used to present and keep track of variables and complicated explanations.

When we assert that variation in independent variable X causes variation in dependent variable Y, we are making three assertions:

• that X and Y covary
• that the change in X precedes the change in Y
• that covariation between X and Y is not spurious or a coincidence

A hypothesis is an explicit statement about the relationship between phenomena that formalizes the researcher’s informed guess. Data analysis is used to test the hypothesis as it may be correct or incorrect.

There are six characteristics of a good hypothesis. A good hypothesis should:

• be an empirical statement that formalizes educated guesses about phenomena that exist in the political world, not a statement about what the researcher wants to be true.
• explain general phenomena rather than one particular occurrence of the phenomena.
• be plausible—there should be a logical reason for thinking that the hypothesis might be confirmed by the data.
• be specific by stating the direction of the relationship between two phenomena, be it a positive or negative relationship .
• be consistent with the data by using terms that are consistent with the manner of testing.
• be testable through feasible to obtain data that will indicate if the hypothesis is defensible.

Hypotheses also specify a unit of analysis , or the level of political actor to which it applies (individuals, groups, states, organizations, etc.).

• Most research uses hypotheses with one unit of analysis.
• While a cross-level analysis specifying more than one unit of analysis is sometimes useful for making ecological inferences about individuals from aggregate data, in general, researchers should not mix units of analysis within a hypothesis.

Definitions of concepts should be clear, accurate, precise, and informative, so that others may fully understand the concept as it was tested and evaluate the measurement strategy for the concept.

Many of the concepts used in political science are fairly abstract and require careful and extensive thought to make definitions clear.

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## Antecedent - Definition & Meaning

What is antecedent .

In the field of statistics, an antecedent (or ‘antecedent variable’) is a variable that explains the behaviour of another (subsequent) variable. Usually the antecedent is a chronologically preceding variable, as seen in auto-regressive and time-series models. In the context of simple regression, the antecedent variable would be one that would explain the behaviour of both the independent and the dependent variables.

The primary intention of using an antecedent in statistical models when applied to the field of social science is to explain the cause-effect relationship between the variables in a phenomenon. This helps to get a clearer picture of why and how that phenomenon occurs when the latter’s mechanism is not fully clear.

However, it may so happen that the relationship so explained by use of an antecedent may not be realistic, even though statistically significant with high correlation. The well-known example of rhythmic increase of ‘ice-cream sales’ and ‘level of crime’ is one such situation. So use of only antecedents and regression models is not recommended for complete explanation of any phenomenon.

This article has been researched & authored by the Business Concepts Team . It has been reviewed & published by the MBA Skool Team. The content on MBA Skool has been created for educational & academic purpose only.

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Methodology

• Types of Variables in Research & Statistics | Examples

## Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts
• Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

## Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

## Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

## Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

## Here's why students love Scribbr's proofreading services

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

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## Rebecca Bevans

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Neag School of Education

## Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

Quantitative Data Analysis

## 7 Multivariate Analysis

Roger Clark

We saw, in our discussion of bivariate analysis , how crosstabulation can be used to examine bivariate relationships, like the one Kearney and Levine discovered between watching 16 and Pregnant and becoming pregnant for teenaged women. In this chapter, we’ll be investigating how researchers gain greater understanding of bivariate relationships by controlling for other variables. In other words, we’ll begin our exploration of multivariate analyses , or analyses that enable researchers to investigate the relationship between two variables while examining the role of other variables.

You may recall that Kearney and Levine claim to have investigated the relationship between watching 16 and Pregnant and becoming pregnant, and thought it might have been at least partly due to the fact that those who watched were more likely to seek out information about (and perhaps use) contraception. Researchers call a variable that they think might affect, or be implicated in, a bivariate relationship a control variable . In the case of Kearney and Levine’s study, the control variable they thought might be implicated in the relationship between watching 16 and Pregnant and becoming pregnant was seeking out information about (or using) contraception.

Before we go further we’d like to introduce you to three kinds of control variables: intervening , antecedent , and extraneous control variables. An intervening control variable is a variable a researcher believes is affected by an independent variable and in turn affects a dependent variable. The Latin root of “intervene” is intervener , meaning “to come between”—and that’s what intervening variables do. They come between, at least in the researcher’s mind, the independent and dependent variables.

For Kearney and Levine, seeking information about contraception was an intervening variable: it’s a variable they thought was affected by watching 16 and Pregnant (their independent variable) and in turn affected the likelihood that a young woman would be become pregnant (their dependent variable). More precisely, their three-variable hypothesis goes something like this: a young woman who watched 16 and Pregnant was more likely to seek information than a woman who did not watch it, and a woman who sought information about contraception was less likely to get pregnant than a woman who did not seek information about contraception. One quick way to map such a hypothesis is the following:

Importantly, researchers who believe they’ve found an intervening variable linking an independent variable and a dependent variable don’t believe they are challenging the possibility that the independent variable may be a cause of variation in the dependent variable. (More about “cause” in a second.) They are simply pointing to a possible way, or mechanism through which, the independent variable may cause or affect variation in the dependent variable.

A second kind of control variable is an antecedent variable. An antecedent variable is a variable that a researcher believes affects both the independent variable and the dependent variable. Antecedent has a Latin root that translates into “something that came before.” And that’s what researchers who think they’ve found an antecedent variable believe: that they’ve found a variable that not only comes before and affects both the independent variable and the dependent variable, but also, in some real sense, causes them to go together, or to be related.

For an example of a researcher/theorist who thinks he may have found an antecedent variable that explains a relationship, think about what Robert Sternberg is saying about the correlation between the attractiveness of children and the care their parents give them in this article on the research by W. Andrew Harrell .

Quiz at the end of the article: What two variables constituted the independent and dependent variables of the finding announced by researchers at the University of Alberta? How did they show these two variables were related? What variable did Robert Sternberg suspect might have been an antecedent variable for the independent and dependent variables found to be related by the U. of Alberta researchers? How did he think this variable might explain the relationship?

If you said that the basic relationship discovered by the University of Alberta researchers was that ugly children get poorer care from their parents than pretty children, you were right on the money. (It’s back-patting time!) Here the proposed independent variable was the attractiveness of children and the dependent variable was the parental care they received.

If you said that the socioeconomic status or wealth of the parents was what Sternberg thought might be an antecedent variable for these two variables (attractiveness and care), then you should glow with pride. Sternberg suggested that wealthier parents can both make their children look more attractive than poorer parents can and give their children better care than poorer parents can. One quick way to map such a hypothesis is like this:

Importantly, a researcher who thinks they have found an antecedent variable for a relationship implies that that have found a reason why the original relationship might be non-causal. Spurious is a word researchers use to describe non-causal relationships. Philosophers of science have told us that in order for a relationship between an independent variable and a dependent variable to be causal, three conditions must obtain:

1. The independent and dependent variables must be related. We demonstrated ways, using crosstabulation, that such relationships can be established with data. The Alberta researchers did show that the attractiveness of children was associated with how well they were treated (cared for or protected) in supermarkets. This condition is sometimes called association .

2. Instances of the independent variable occurring must come before, or at least not after, instances of the dependent variables. The attractiveness of the children in the Alberta study almost certainly preceded their treatment by their parents during the shopping expeditions observed by the researchers. This factor is often called temporal order .

3. There can be NO antecedent variable that creates the relationship between the independent variable and the dependent variable. This is the really tough condition for researchers to demonstrate, because, in principle, there could be an infinite numbers of antecedent variables that create such a relationship. This factor is often called elimination of alternatives . There is one research method—the controlled laboratory experiment—that theoretically eliminates this difficulty, but it is beyond the scope of this book to show you how. Yet it is not beyond our scope to show you how an antecedent variable might be shown, with data, to throw real doubt on the notion that an independent variable causes a dependent variable. And we’ll be doing that shortly.

## Back to Our Main Story

A third kind of control variable is an extraneous variable . An extraneous variable is a variable that has an effect on the dependent variable that is separate from the effect of the independent variable. One can easily imagine variables that would affect the chances of an adolescent woman’s getting pregnant (the dependent variable for Kearney and Levine) that have nothing to do with her having watched, or not watched, the TV show 16 and Pregnant . Whether or not friends are sexually active, and whether or not she defines herself as a lesbian, are two such variables. Sexual experience of her friendship group and sexual orientation, then, might be considered extraneous variables when considering the relationship between watching 16 and Pregnant and becoming pregnant. One might map the relationship among these four variables in the following way:

## What Happens When You Control for a Variable and What Does it Mean?

You may be wondering how one could confirm any three-variable hypothesis with data. Let’s look at an example using data from eight imaginary adolescent women, whether they watched 16 and Pregnant , got pregnant, and sought information about contraception:

Checking out a three-variable hypothesis requires, first, that you determine the relationship between the independent and dependent variables: in this case, between having watched 16 and Pregnant and pregnancy status. Do you recall how to do that? In any case, we’ve done it in Table 1.

Table 1. Crosstabulation of Watching 16 and Pregnant and Pregnancy Status

You’ll note that the direction of this relationship, as expected by Kearny and Levine, is that women who had watched the show were less likely to get pregnant than those who had not. And a Yule’s Q of 0.80 suggests the relationship is strong.

What controlling a relationship for another variable means is that one looks at the original relationship (in this case between watching the show and becoming pregnant) after eliminating variation in the control variable. We eliminate such variation by separating out the cases that fall into each category of the control variable, and examining the relationship between the independent and dependent variables in each category. In this case, what this means is that we first look at the relationship between watching and getting pregnant for those who have sought contraceptive information and then look at it for those who have not sought such information. To do this we create two more tables that have the same form as Table 1, one into which only those who fell into the “yes” category of having sought contraceptive information are put, the other into which only those cases that fell into the “no” category of having sought contraceptive information are put. Doing this, we’ve created two more tables, Tables 2 and 3. Table 2 looks at the relationship between having watched the show and having become pregnant only for the four cases that sought contraceptive information; Table 3 does this only for the four cases that didn’t seek contraceptive information.

Table 2 Crosstabulation of Watching 16 and Pregnant and Pregnancy Status For Those Who Sought Contracepti ve Information

Table 3 Crosstabulation of Watching 16 and Pregnant and Pregnancy Status For Those Who Did Not Seek Contraceptive Information

We call relationship between an independent and dependent variable for the part of a sample that falls into one category of a control variable a partial relationship or simply a partial . What is notable about the partial relationships in both Table 4.2 and 4.3 is that they are as weak as they could possibly be (both Yule’s Qs are equal to 0.00); both are much weaker than the original relationship between watching the show and becoming pregnant. In fact, in the context of controlling a relationship between two variables for a third, the relationship between the independent variable and the dependent variable, before the control, is often called an original relationship .

It may not surprise you to learn that controlling a relationship for a third variable does not always yield partials that are all weaker than the original. In fact, a famous methodologist, Paul Lazarsfeld (see Rosenberg, 1968), identified four distinct possibilities and others have called the resulting typology the elaboration model . Elaboration , in fact, is the term used by researchers for the process of controlling a relationship for a third variable. Table 4 outlines the basic characteristics of Lazarsfeld’s four types of elaboration, with one more thrown in because, as we’ll show, this fifth one is not only a logical, but also a practical, possibility.

Table 4. The Elaboration Model: Five Kinds of Elaboration

Quiz at the end of the table : What kind of elaboration is demonstrated, in your view, in Tables 4.1 to 4.3?

You may recall that Kearney and Levine saw seeking contraceptive information as an intervening variable between the watching of 16 and Pregnant and pregnancy status. Moreover, the partial relationships (shown in Tables 2 and 3) are both weaker than the original (shown in Table 1), so the elaboration shown in Tables 1 through 3 is an interpretation . If this kind of elaboration occurred in the real world, one could be pretty sure that seeking contraceptive information was indeed a mechanism through which watching the show affected a teenage woman’s pregnancy status. Note: while the quantitative result in cases of interpretation and explanation are the same, the explanations for the processes at work are different, and this means the researcher must rely on their own knowledge of the variables at hand to determine which is at work. In cases of interpretation, an intervening variable is at work, and thus the relationship between the independent and dependent variables is a real relationship—it’s just that the intervening variable is the mechanism through which this relationship occurs. In contrast, for cases of explanation, an antecedent variable is responsible for the apparent relationship between the independent and dependent variables, and thus this apparent relationship does not really exist. Rather, it is spurious.

In the real world, things don’t usually work out quite as neatly as they did in this example, where an original relationship completely “disappears” in the partials. If one finds evidence of an interpretation , it’s likely to be more subdued. Tables 5 and 6 demonstrate this point. Here, the researcher’s (Roger’s) hypothesis had been that people who are more satisfied with their finances are generally happier than people who are less satisfied. Table 5 uses General Social Survey (GSS) data to provide support for this hypothesis. Comparing percentages, about 47.5 percent of people who are satisfied with their finances claimed to be very happy, while only 17.3 percent who have claimed to be not at all satisfied with their finances said they were very happy. Moreover, a gamma of 0.42 suggests this relationship is in the hypothesized direction and that it is strong.

Table 5 Crosstabulation of Satisfaction with Finances and General Happiness, GSS Data from SDA

Roger also introduced a control variable, the happiness of respondents’ marriages, believing that this variable might be an intervening variable for the relationship between financial satisfaction and general happiness. In fact, he hypothesized that people who are more satisfied with their finances would be happier in their marriages than people who were not satisfied with their finances, and that happily married people would be more generally happy than people who are not happy in their marriages. In terms of the elaboration model, he was expecting that the relationships between financial satisfaction and general happiness for each part of the sample defined by a level of marital happiness (i.e., the partials) would be weaker than the original relationship between financial satisfaction and general happiness. And (hallelujah!) he was right. Table 6 shows that the relationship between financial satisfaction and general happiness for those with very happy marriages yielded a gamma of 0.35; for those with pretty happy marriages, 0.33; and for those with not too happy marriages, 0.31. All three of the partial relationships were weaker than the original, which we showed in Table 5 had a gamma of 0.43.

Table 6. Gammas for the Relationship Between Satisfaction with Finances and General Happiness for People with Different Degrees of Marital Happiness

Because the partials for each level of marital happiness are only somewhat weaker than the original relationship between financial satisfaction and general happiness, they don’t suggest that marital satisfaction is the only reason for the relationship between financial satisfaction and general happiness, but they do suggest it is probably part of the reason. A curious researcher might look for others. But you get the idea: data can be used to shed light on the meaning of basic, two-variable relationships.

Perhaps more interesting still is that data can be used to resolve disputes about basic relationships. To illustrate, let’s return to the “Ugly Children” study and Alberta, discussed in the chapter on bivariate analysis . One of the Alberta researchers, a Dr. Harrell, essentially said the fact that prettier children got better care than uglier children was causal: parents with prettier children are propelled by evolutionary forces, in his view, to protect their children (and, one assumes, parents of uglier children are not). A Dr. Sternberg, however, didn’t see this relationship as causal. Instead, he saw it as the spurious result of wealth: wealthier parents can feed and clothe their kids better than others and are more likely to be caught up on supermarket etiquette associated with child care than others. Who’s right?

One way one could check this out is by collecting and analyzing data. Suppose, for instance, that a researcher replicated the Alberta study (following parent/child dyads around supermarkets to determine the attractiveness of the children and how well they were cared for), but added observations about the cars the parent/child couples came in. Late-model cars might be used as an indicator of relative wealth; beat-up dentmobiles (like Roger’s), of relative poverty. Then one could see how much of the relationship between attractiveness and care “disappeared” in the parts of the sample that were defined by wealth and poverty. Suppose, in fact, the data so collected looked like this:

Table 7. Hypothetical Data to Test Dr. Sternberg’s Hypothesis

Quiz about these data: Can you figure out the direction and strength of the relationship between the attractiveness of children and their care in this sample? What is the strength of this relationship within each category (rich and poor) of the control variable? What kind of elaboration did you uncover?

If you found that the original relationship was that pretty children got better care than ugly children (75% of the former did so, while only 25% of the latter did), you should be glowing with pride. If you found that the strength of the relationship (Yule’s Q=0.80) was strong, your brilliance is even more evident. And if you found that this strength “disappeared” (Yule’s Qs = 0.00) within each category of the wealth, you’re a borderline genius. If you decided that the elaboration is an “explanation,” because the partials are both weaker than the original and you’ve got an antecedent variable (at least according to Sternberg), you’ve crossed the border into genius.

Now referring back to the criteria for demonstrating causation (above), you’ll note that the third criterion was that there must not be any antecedent variable that creates the relationship between the independent and dependent variables. What this means in terms of data analysis is that there can’t be any antecedent variables whose control makes the relationship “disappear” within each of the parts of the sample defined by its categories. But that’s exactly what has happened above. In other words, one can show that a relationship is non-causal (or spurious) by showing, through data, that there is an antecedent variable whose control, as in the example we’ve just been working with, makes the relationship “disappear.” Pretty cool, huh?

On the other hand, while it’s impossible to use data to show that a relationship is causal, [1] it is possible to show that any single third variable that others hypothesize is creating the relationship between the relevant independent and dependent variables isn’t really creating that relationship. Thus, for example, Harrell and his Alberta team might have heard Sternberg’s claim that the wealth of “families” is the real reason why the attractiveness and care of children are related. And if they’d collected data like the following, they could have shown this claim was false. Can you use the data to do so? See if you can analyze the data and figure out what kind of elaboration Harrell et al. would have discovered.

Table 8. Hypothetical Data to Test Dr. Harrell

If you found that these data yielded a “replication,” you’re clearly on the brink of mastering the elaboration model. The original relationship between attractiveness and care was that pretty kids got better care than ugly kids (100% of pretty kids got it, compared to 50% of ugly kids who did) and this relationship was strong (Yule’s Q= 1.00). But each of the partials was just as strong (Yule’s Qs = 1.00), and had the same direction, as the original. What a replication shows is that the variable that was conceived of as an antecedent variable (wealth) does not “explain” the original relationship at all. The relationship is just as strong in each part of the sample defined by categories of the antecedent variable as it was before variation in this variable was controlled.

## A Quick Word About Significance Levels

This chapter’s focus on the elaboration model and controlling relationships has been all about making comparisons: primarily about comparing the strength of partial relationships to the strength of original relationships (but sometimes, as you’ll soon see, comparing the strength of partials to one another). We haven’t said a thing about comparing inferential statistics and the resulting information about whether one dare generalize from a sample to the larger population from which the sample has been drawn. This has been intentional. You may recall (from the chapter on bivariate analyses ) that the magnitude of chi-square is directly related to the size of the sample: the larger the sample, given the same relationship, the greater the chi-square. When one controls a relationship between an independent and dependent variable, however, one is dividing the sample into at least two parts, and, depending on the number of categories of the control variable, potentially more. So comparing the chi-squares, and therefore the significance levels, of partials to that of an original is hardly a fair fight. The originals will always involve more cases than the partials. So we usually limit our comparisons to those of strength (and sometimes direction), though if a relationship loses its statistical significance when examining the partials this does mean that the relationship cannot necessarily be generalized in its partial form.

Having made this important point, however, we’ll let you loose on the two quizzes that will end this chapter, each of which will introduce you to a new kind of elaboration.

## Quiz #1 at the End of the Chapter

Show that the (hypothetical) sample data, below, conceivably collected to test Kearney and Levine’s three-variable hypothesis (that adolescents who watched the show were more likely to seek contraceptive information than others, and that those who sought information were less likely to get pregnant than others) are illustrative of a “ specification .” For which category of the control variable (sought contraceptive information) is the relationship between having watched 16 and Pregnant and having gotten pregnant stronger? For which is it weaker? Why would such data NOT support Kearney and Levine’s hypothesis? [2]

## Quiz #2 at the End of the Chapter

Suppose the data you collected to test Sternberg’s hypothesis (that the relationship between the attractiveness of children and their care is a result of family wealth or social class) really looked like this  ⇒

What kind of elaboration would you have uncovered? What makes you say so? (Doesn’t it seem odd that partial relationships can be stronger than original relationships? But they sure can. That what Roger calls a “revelation.”)

multivariate analysis

antecedent variable

intervening variable

control variable

extraneous variable

original relationship

partial relationship

elaboration

interpretation

replication

explanation

specification

Below are real data from the GSS. See what you can make of them.

Who do you think would be more fearful of walking in their neighborhoods at night: males or females? Recalling that gamma= Yule’s Q for 2 x 2 tables, what does the following table, and its accompanying statistics, tell you about the actual direction and strength of the relationship? Support your answer with details from the table.

Table 9. Crosstabulation of Gender (Sex) with Whether Respondent Reports Being Fearful of Walking in the Neighborhood at Night (Fear), GSS Data from SDA

*Row variable treated as the dependent variable.

We controlled this relationship for “race,” a variable that had three categories: Whites, Blacks, and others. Suppose you learned that the gamma for this relationship among Whites was -0.61, among Blacks was -0.53 and among those identifying as members of other racial groups was -0.44. What kind of elaboration, in your view, would you have uncovered? Justify your answer.

• Please read the article by Robert Bartsch et al ., entitled “ Gender Representation in Television Commercials: Updating an Update ” ( Sex Roles, Vol. 43, Nos. 9/10, 2000: 735-743). [3] What is the main point of the article, in your view? What is the significance, according to Bartsch et al., of the gender of the voice-over in a commercial? Please examine Table 1 on page 739. Describe the overall gender breakdown of the voice-overs in 1998. Which gender was more represented in the voice-overs? Now look at the gender breakdown for voice-overs for domestic products and nondomestic products separately. Which of these is the stronger relationship: the one for domestic or the one for nondomestic products? What kind of elaboration would you say Bartsch et al . uncovered when they controlled the gender of voice-over for type of product (domestic or nondomestic)? How might you account for this finding?

• Diagramming an Extraneous Variable Relationship © Mikaila Mariel Lemonik Arthur
• The reason you can never show, through data analysis, that a two-variable relationship is causal is that for every two-variable relationship there are an infinite number of possible antecedent variables, and we just don’t live long enough to test all the possibilities. ↵
• The original relationship, in this case, would be strong |Yule’s Q|= 0.80. The partial relationship for those who had sought contraception, however, would be stronger (|Yule’s Q|= 1.00, while that for those who had not sought contraception would be very weak (|Yule’s Q|= 0.00). You can specify, therefore, that the original relationship is particularly strong for those who’d sought contraception and particularly weak for those who had not. Kearney and Levine’s hypothesis had anticipated an “interpretation,” but this data yield a specification. So the data would prove their hypothesis wrong. ↵
• If the link below doesn’t work, perhaps you can hunt down an electronic copy of the article through your college’s library service. ↵

Quantitative analyses that explores relationships involving more than two variables or examines the impact of other variables on a relationship between two variables.

A variable that is neither the independent variable nor the dependent variable in a relationship, but which may impact that relationship.

A variable hypothesized to intervene in the relationship between an independent and a dependent variable; in other words, a variable that is affected by the independent variable and in turn affects the dependent variable.

A variable that is hypothesized to affect both the independent variable and the dependent variable.

The term used to refer to relationship where variables seem to vary in relation to one another, but where in fact no causal relationship exists.

The situation in which variables are able to be shown to be related to one another.

The order of events in time; in relation to causation, the fact that independent variables must occur prior to dependent variables.

In relation to causation, the requirement that for a causal relationship to exist, all possible explanations other than the hypothesized independent variable have been eliminated as the cause of the dependent variable.

A variable that impacts the dependent variable but is not related to the independent variable.

Examining a relationship between two variables while eliminating the effect of variation in an additional variable, the control variable.

A relationship between an independent and a dependent variable for only the portion of a sample that falls into a given category of a control variable.

Shorter term for a partial relationship.

The relationship between an independent variable and a dependent variable before controlling for an additional variable.

A typology developed by Paul Lazarsfeld for the possible analytical outcomes of controlling for a variable.

A term used to refer to the process of controlling for a variable.

## Independent and Dependent Variables

Before we can begin to choose our statistical test, we must determine which is the independent and which is the dependent variable in our hypothesis. Our dependent variable is always the phenomenon or behavior that we want to explain or predict . The independent variable represents a predictor or causal variable in the study. In any antecedent-consequent relationship , the antecedent is the independent variable and the consequent is the dependent variable.

It has been traditional for the man rather than the woman to receive the check when a couple dines out. A researcher wondered whether this would be true if the woman was clearly in charge, asking for the wine list, questioning the waiter about dishes on the menu, etc. A large random sample of restaurants was selected. One couple was used in all restaurants, but in half the man assumed the traditional in-charge role, and in the other half the woman was in charge. At each restaurant, the couple recorded whether the check was presented to the man or to the woman.

Test the research hypothesis that the check will be presented to the person showing in-charge behavior.

The behavior that we are trying to explain is the presentation of the check . Did the wait staff give the check to the man or the woman? This would be the dependent variable in the study. The independent variable is was manipulated as part of the experimental design. The independent variable was who was in charge during the dinner.

## 1. What is an antecedent variable?

Answer: An antecedent variable is a condition or event that occurs before and is thought to influence the outcome or occurrence of a dependent variable.

## 2. What is the difference between an antecedent variable and a dependent variable?

Answer: An antecedent variable is a condition or event that occurs before and is thought to influence the outcome or occurrence of a dependent variable. A dependent variable is the outcome or result of the antecedent variable and is affected by it.

## 3. What types of variables are classified as antecedent variables?

Answer: Antecedent variables can be any type of variable, such as demographic, psychological, or environmental variables.

## 4. What is the purpose of studying antecedent variables?

Answer: The purpose of studying antecedent variables is to identify any potential relationships between them and a dependent variable, so that these relationships can be better understood.

## 5. How are antecedent variables used in research?

Answer: Antecedent variables are used in research to identify any potential relationships between them and a dependent variable, so that these relationships can be better understood.

## 6. How are antecedent variables different from intervening variables?

Answer: Antecedent variables occur prior to the dependent variable, while intervening variables occur between the antecedent variable and the dependent variable.

## 7. What are some examples of antecedent variables?

Answer: Examples of antecedent variables include demographic characteristics, psychological states, environmental conditions, and other conditions that occur before the dependent variable.

## 8. How are antecedent variables measured?

Answer: Antecedent variables are typically measured using a combination of survey questions, interviews, observations, and other data collection methods.

## 9. Are antecedent variables always predictive of a dependent variable?

Answer: Not necessarily. It is possible for antecedent variables to have no relationship to a dependent variable, or for the relationship to be weak or indirect.

## 10. How do antecedent variables interact with other variables?

Answer: Antecedent variables can interact with other variables to influence the outcome of the dependent variable. For example, the presence of an antecedent variable can interact with other variables to increase or decrease the likelihood of a certain outcome.

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• v.38(3); Fall 2005

## Combined Antecedent Variables as Motivating operations within Functional Analyses

Nathan a call.

The University of Iowa

## David P Wacker

Joel e ringdahl, eric w boelter.

Functional analysis test conditions typically manipulate a single antecedent variable and an associated consequence to better isolate response–reinforcer relations. In some instances no problem behavior is observed, perhaps representing a false-negative finding. The present study evaluated one approach to assess potentially false-negative findings within functional analyses. Participants were exposed to single-antecedent functional analysis test conditions and combined-antecedent test conditions within a multielement design. Both participants engaged in problem behavior primarily during the combined-antecedent test conditions, and treatments matched to the results were effective in reducing problem behavior. Findings are discussed in terms of clinical implications of combining antecedent variables to further examine potentially false-negative functional analysis results.

One potential limitation of functional analyses is that a subset of individuals does not exhibit problem behavior during test conditions. These individuals may represent Type II errors or false negatives ( Wacker, Berg, Harding, & Cooper-Brown, 2004 ). That is, some individuals engage in problem behavior in the natural setting but do not display those behaviors during analogue conditions. One reason false-negative errors may occur is that antecedent variables manipulated in test conditions do not function as motivating operations (MOs; Laraway, Snycerski, Michael, & Poling, 2003 ) and, thus, do not occasion problem behavior. Previous research has demonstrated that combinations of antecedent variables might motivate problem behavior ( O'Reilly, Lacey, & Lancioni, 2000 ; Wacker et al., 1996 ). Manipulating multiple MOs within functional analyses might occasion problem behavior for some individuals for whom a false-negative finding would otherwise be obtained. The present study examined whether manipulating multiple antecedent variables within functional analysis test conditions would be one means of clarifying false-negative outcomes.

## Setting, Participants, and Response Definitions

Analyses were conducted while participants were patients in an inpatient psychology unit. Richard was a 17-year-old boy who had been diagnosed with a genetic disorder resulting in mental retardation and a seizure disorder. His problem behaviors included aggression and destruction in the form of throwing objects. Kevin was a 2-year 8-month-old boy who had been diagnosed with a disruptive behavior disorder. His problem behavior consisted of aggression. For both participants, aggression was defined as audible contact between an extremity and another person or displacement of an object that resulted in audible contact between that object and another person.

## Data Collection and Interobserver Agreement

All sessions were scored via closed-circuit video monitoring using laptop computers that collected real-time data. A second observer independently collected interobserver agreement data during 26% of sessions for Richard and 27% of sessions for Kevin. Agreement percentages were calculated by separating the data into 10-s bins, calculating agreement within each bin, averaging across bins, and multiplying the result by 100%. Agreement averaged 98% for Richard (range, 95% to 100%) and 99% for Kevin (range, 87% to 100%).

## Preference and Demand Assessments

High-preference (HP) and low-preference (LP) items were identified using a combination of assessment procedures described by Roane, Vollmer, Ringdahl, and Marcus (1998) and Fisher et al. (1992) . For both participants, a demand was defined as an instruction to interact with items (HP or LP) in an LP activity. To establish relative preferences for activities with the HP and LP items, a demand assessment was conducted with Kevin in a paired-choice format similar to the preference assessment (details available on request). For Richard, the preference and demand assessments identified performing academic tasks (LP activity) using spelling flashcards (LP items) as the demand; for Kevin, taking pieces apart (LP activity) from a marble game (HP item) and sorting (LP activity) counting bears (LP item) by color were identified as demands.

## Functional Analysis

Functional analysis procedures included free play, attention, escape, and tangible conditions ( Iwata, Dorsey, Slifer, Bauman, & Richman, 1982/1994 ). Modifications were made to compare results from single-antecedent conditions to those from combined-antecedent conditions. The combined-antecedent conditions for each participant were based on descriptive analyses and results of previous assessments.

## Single-Antecedent Conditions

Throughout the attention condition, HP and LP items remained available. In the tangible condition, access to the HP item was contingent on problem behavior while the LP item and attention remained available. During the escape condition with Kevin, the therapist instructed him to engage in the LP activity with the HP item. For Richard, demands consisted of the LP activity with the LP item. For both participants, therapists delivered attention during demands in the form of verbal prompting, praise, and encouragement on a variable-time 20-s schedule to control for a potential MO in the form of restricted attention. During breaks, the child was free to interact with the HP item in whatever manner he chose. In addition, one combined-antecedent test condition (described below) was included in each session block.

## Combined-Antecedent Conditions

Demand and diverted attention/contingent attention (richard).

The therapist delivered an instruction to engage in the LP activity and informed Richard that while he worked, she was going to engage in another activity (e.g., read a magazine). The therapist then engaged in the stated activity and did not attend to Richard. Twenty seconds of attention was delivered contingent on the occurrence of problem behavior during the demand.

## Demand and restricted tangible item/contingent escape (Kevin)

Kevin's HP item was placed out of reach at the beginning of the session. The therapist then delivered an instruction to engage in the LP activity with the LP item. The therapist provided a 20-s break from the demand contingent on the occurrence of problem behavior. During the break, Kevin was free to play with the LP item in whatever manner he chose, and the HP item remained unavailable.

Treatment for both participants consisted of functional communication training (FCT) with extinction, conducted in a nonconcurrent multiple baseline design. During FCT, the reinforcer that maintained problem behavior, as identified in the functional analysis, was made available contingent on appropriate requests. Problem behavior was neutrally blocked or ignored. FCT sessions were conducted in the context of the antecedent variables that evoked the target behavior during the functional analysis.

## RESULTS AND DISCUSSION

Results of Kevin's analysis are presented in Figure 1 (top). During the functional analysis, aggression was observed most often during the combined demand and restricted tangible item/contingent escape condition. No aggression was observed during the free-play or attention conditions, and it occurred infrequently during escape and tangible conditions. Aggression decreased to zero during the first three sessions of treatment. Following three consecutive treatment sessions without aggression, a work requirement was added in which a break was delivered only after Kevin completed one portion of the task after asking for a break. After a brief increase in aggression across four sessions, Kevin did not engage in aggression for six consecutive sessions. The work requirement was then increased to two portions of the task before breaks were delivered, and there was no increase in aggression. During the first session in which Kevin's care provider conducted the treatment, aggression increased to 0.2 responses per minute, after which it was not observed for the remainder of the analysis.

Appropriate requests during Phase 2 are depicted as open circles.

Kevin's use of appropriate requests also is shown in Figure 1 . Although requests initially occurred at relatively high levels, the rate of requests decreased across sessions. There are at least two plausible explanations for this decrease. First, the increasing work requirement necessitated his spending a greater proportion of the session in instructional time, resulting in fewer opportunities to ask for breaks. Second, repeated exposure to the demand may have resulted in mastery of the skills required to complete the demand, decreasing the value of escape from the task.

Results of Richard's analysis are presented in Figure 1 (bottom). No problem behavior occurred during the escape or tangible conditions. Richard engaged in relatively high rates of problem behavior during the combined demand and diverted attention/contingent attention condition ( M  =  0.5 responses per minute). Problem behavior was observed in the first two attention sessions; however, no problem behavior occurred subsequently in this condition. Following four sessions of treatment, Richard did not engage in problem behavior for three consecutive sessions. Appropriate requests also increased from zero in the first treatment session to an average of 0.7 per minute during the final three sessions.

The purpose of the current study was to evaluate the potential contributions of combining common functional analysis antecedent variables within test conditions. Only one of the consequences associated with the two antecedent variables was delivered contingent on problem behavior. It is not clear from the current data whether the consequence associated with the other antecedent variable from the combined-antecedent test condition would have also functioned as a reinforcer for problem behavior in the presence of a second antecedent variable. Also, although activity preference across the demand escape and demand and restricted tangible item/contingent escape conditions was held constant for Kevin (i.e., both were LP), the topography of the two activities differed. This procedure introduced an additional variable, so it is not possible to account for changes in his behavior solely by restricted access to the HP activity. These limitations may be areas for future research.

For both participants, elevated rates of problem behavior were observed within the combined-antecedent test conditions, whereas little or no problem behavior was observed in control or single-antecedent test conditions. Thus, failure to include the combined-antecedent variables would likely have resulted in false-negative findings for these participants. These results suggest that functional analyses that combine selected pairs of antecedent variables may clarify outcomes when standard test conditions do not result in problem behavior. It may be the case that the presence of combined antecedent variables was more analogous to the MOs in the natural environment that evoked problem behavior. Because the manner in which combinations of antecedent variables operate is likely to be idiosyncratic, developing preassessment strategies that aid the identification of combinations with elevated potential to precipitate problem behavior is an important topic for future research.

## Acknowledgments

We thank Todd Kopelman for his assistance with collecting data for this project as well as Agnes DeRaad for her input on a previous version of this manuscript.

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## Antecedent events as predictive variables for behavioral function

Affiliation.

• 1 Ramon Llull University, c/ Císter, 34, 08022 Barcelona, Spain. Electronic address: [email protected].
• PMID: 24210354
• DOI: 10.1016/j.ridd.2013.09.040

Challenging behavior is one of the largest barriers to ensuring that people with intellectual disabilities (ID) are able to participate in the community. These difficulties have become one of the main causes of social exclusion. The research into and treatment of challenging behavior has usually involved the identification of its function and the manipulation of the events or environmental conditions that influence its occurrence (antecedent variables). The present research explores the relationship between antecedents and behavioral function and the extent to which antecedent variables may act as predictors of behavioral function. This relationship is explored using two standardized instruments: Questions About Behavioral Function and Contextual Assessment Inventory. Data from the validation of these instruments for the Spanish population involved 300 participants with ID and 328 challenging behaviors. The results suggest that social/cultural variables are most related to challenging behavior, whereas biological variables seem to only be related to physically maintained behavior.

Keywords: Antecedent variables; Behavioral function; CAI; Challenging behavior; Functional assessment; Intellectual disabilities; QABF.

## Publication types

• Research Support, Non-U.S. Gov't
• Aggression / psychology*
• Attention Deficit and Disruptive Behavior Disorders / psychology
• Intellectual Disability / psychology*
• Mental Disorders / psychology*
• Middle Aged
• Risk Factors
• Self-Injurious Behavior / psychology
• Severity of Illness Index
• Social Environment*
• Stereotyped Behavior
• Surveys and Questionnaires

## Antecedent: Understanding the Importance of Identifying the Cause

Antecedent is a term that is commonly used in linguistics and grammar. It refers to a word, phrase, or clause that comes before another word or phrase, and is often replaced by a pronoun. In simpler terms, it is the word or phrase that a pronoun refers to.

Understanding antecedents is an important aspect of effective communication, both written and verbal. Pronouns are used frequently in everyday language, and it is crucial to ensure that they are used correctly. Using the wrong antecedent with a pronoun can lead to confusion and miscommunication.

## Defining Antecedent

Antecedent is a term that is used in various fields such as psychology, linguistics, and mathematics. In general, antecedent refers to something that comes before or precedes another thing. In this section, we will discuss the meaning of antecedent in different fields.

## Antecedent in Psychology

In psychology, antecedent refers to the events or circumstances that precede a behavior. Antecedents can be environmental, social, or internal factors that trigger a particular behavior. For example, a child may throw a tantrum when he is hungry, tired, or frustrated with a task. In this case, hunger, fatigue, and frustration are the antecedents that trigger the child’s behavior.

## Antecedent in Linguistics

In linguistics, antecedent refers to a word, phrase, or clause that is replaced by a pronoun in a sentence. The antecedent is usually the noun or noun phrase that comes before the pronoun. For example, in the sentence “Mary saw John and called to him,” John is the antecedent of the pronoun “him.” Antecedents can also be found in longer sentences with more complex structures.

## Antecedent in Mathematics

In mathematics, antecedent refers to the first part of an if-then statement. The antecedent is the condition that must be met in order for the consequent to be true. For example, in the statement “If x is greater than 5, then y is less than 10,” the antecedent is “x is greater than 5.” If x is not greater than 5, then the statement is false, regardless of the value of y.

In conclusion, antecedent is a term that is used in different fields with slightly different meanings. In psychology, antecedent refers to the events or circumstances that precede a behavior. In linguistics, antecedent refers to a word, phrase, or clause that is replaced by a pronoun in a sentence. In mathematics, antecedent refers to the first part of an if-then statement.

## Types of Antecedent

Antecedents can be classified into three main types: Simple Antecedent, Complex Antecedent, and Compound Antecedent.

## Simple Antecedent

A simple antecedent is a noun, pronoun, or phrase that directly precedes a pronoun and refers to a single person, place, or thing. For example, in the sentence “John went to the store, and he bought some milk,” the simple antecedent is “John.”

## Complex Antecedent

A complex antecedent is a noun, pronoun, or phrase that refers to a group of people, places, or things. It can be made up of multiple words and often requires the use of a relative pronoun to connect the antecedent and the pronoun. For example, in the sentence “The car that I bought last week broke down, and it needs to be towed,” the complex antecedent is “the car that I bought last week.”

## Compound Antecedent

A compound antecedent is made up of two or more nouns, pronouns, or phrases that are joined by a coordinating conjunction such as “and” or “or.” For example, in the sentence “John and Mary went to the store, and they bought some milk,” the compound antecedent is “John and Mary.”

It is important to match the pronoun to the correct antecedent to avoid confusion and ensure clarity in communication. When using complex or compound antecedents, it is especially important to use clear and concise language to avoid any misunderstandings.

## Antecedent Examples

Antecedents are used in various fields to clarify what or who a pronoun is referring to in a sentence. Here are some examples of antecedents in literature, law, and history.

## Antecedent in Literature

In literature, antecedents are used to avoid repetition and add variety to the language. For example, in William Shakespeare’s play “Romeo and Juliet,” the character Juliet says, “Romeo, Romeo, wherefore art thou Romeo?” The antecedent in this sentence is “Romeo,” which is repeated twice for emphasis. Without the use of an antecedent, the sentence would be unclear and confusing.

## Antecedent in Law

In law, antecedents are used to clarify the meaning of legal documents. For example, in a contract, the antecedent is used to refer to a specific term or condition. This helps to avoid ambiguity and ensures that both parties understand the terms of the agreement. In legal writing, antecedents are often used in conjunction with pronouns to avoid repetition and make the document easier to read.

## Antecedent in History

In history, antecedents are used to refer to events or people that influenced a particular event or person. For example, in the American Civil War, the antecedent to the conflict was the issue of slavery. This issue had been a source of tension between the North and the South for many years prior to the outbreak of war. Understanding the antecedents to historical events can help us to better understand the causes and consequences of those events.

In conclusion, antecedents are an important part of language and are used in various fields to clarify meaning and avoid repetition. By understanding the use of antecedents, we can improve our writing and communication skills.

## Antecedent in Research

Antecedent variables are essential in research, as they help in explaining the relationship between the independent and dependent variables under study. Antecedent variables are variables that occur before the independent and dependent variables, and they may affect the relationship between the two variables. This section will discuss antecedent variables in quantitative and qualitative research.

## Antecedent in Quantitative Research

In quantitative research, antecedent variables are often referred to as confounding variables. Confounding variables are variables that affect the relationship between the independent and dependent variables, and they may lead to spurious relationships. Researchers use statistical techniques such as regression analysis and ANOVA to control for confounding variables.

For example, in a study that investigates the relationship between physical activity and blood pressure, age and gender may be antecedent variables. Age and gender may affect the relationship between physical activity and blood pressure, and researchers may need to control for these variables to establish a causal relationship between physical activity and blood pressure.

## Antecedent in Qualitative Research

In qualitative research, antecedent variables may be referred to as contextual factors. Contextual factors are factors that affect the research setting and may affect the research outcomes. Researchers need to identify and control for these factors to ensure that the research outcomes are valid and reliable.

For example, in a study that investigates the experiences of cancer patients, the research outcomes may be affected by the type of cancer, the stage of cancer, and the treatment received. Researchers need to identify and control for these contextual factors to ensure that the research outcomes are not biased.

In conclusion, antecedent variables are important in research, as they help in explaining the relationship between the independent and dependent variables. In quantitative research, antecedent variables are often referred to as confounding variables, while in qualitative research, they may be referred to as contextual factors. Researchers need to identify and control for antecedent variables to ensure that the research outcomes are valid and reliable.

Antecedents can be a tricky concept to grasp, especially when it comes to pronoun-antecedent agreement. Here are some frequently asked questions about antecedents:

What is an antecedent?

An antecedent is a word, phrase, or clause that a pronoun refers to in a sentence. For example, in the sentence “John saw the dog and he petted it,” “John” is the antecedent of “he,” and “dog” is the antecedent of “it.”

Why is antecedent agreement important?

Antecedent agreement is important because it ensures that the meaning of a sentence is clear and unambiguous. If a pronoun does not agree with its antecedent in number, gender, or person, it can lead to confusion or misunderstanding.

How do you ensure antecedent agreement?

To ensure antecedent agreement, make sure that the pronoun and its antecedent agree in number, gender, and person. For example, if the antecedent is singular and masculine, the pronoun should also be singular and masculine.

Can an antecedent come after a pronoun?

While it is more common for an antecedent to come before a pronoun, it is possible for an antecedent to come after a pronoun. For example, in the sentence “She saw the dog and petted it,” “dog” is the antecedent of “it,” even though it comes after the pronoun.

What happens if there is no antecedent?

If there is no antecedent, a sentence may be incomplete or meaningless . For example, in the sentence “He needs it,” it is unclear what “it” refers to without an antecedent.

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1. What is an Antecedent Variable? (Explanation & Example)

However, sometimes an antecedent variable can be present. An antecedent variable is a variable that occurs before the independent and dependent variables under study and can help explain the relationship between the two. You can remember this definition by remembering that the word antecedent literally means "previous or preexisting.".

2. What is an Antecedent Variable? (Explanation & Example)

An antecedent variable is a variable that occurs before the independent and dependent variables under study and can help explain the relationship between the two. You can remember this definition by remembering that the word antecedent literally means "previous or preexisting." Examples of Antecedent Variables. Antecedent variables can be ...

3. Antecedent variable

Antecedent variable. In statistics and social sciences, an antecedent variable is a variable that cannot help to explain the apparent relationship (or part of the relationship) between other variables that are nominally in a cause and effect relationship. In a regression analysis, an antecedent variable would be one that influences both the ...

4. Antecedent Variable

In comparison with cross-cultural psychologists, their research designs rely much less on what the anthropologist Beatrice Whiting called "packaged" variables. A packaged variable is an index of culture as an antecedent or independent variable. An example of a packaged variable is the labels "Japanese" or "German."

5. PDF antecedent variable: A variable that occurs before, and may be a cause

quantitative research question: A question that asks about the empirical relationship between two or more variables. research design: The overall plan of a study for collecting data. spurious relationship: A noncausal statistical association between two variables produced by a common cause (i.e., an antecedent variable). statistical significance: The likelihood that the results of a study ...

6. Chapter Summary

Chapter Summary. After choosing a research question, the initial steps of an empirical research project include the following: Variables are used to specify how two or more variables are related in an effort to explain the phenomena of interest. An independent variable is thought to influence, affect, or cause variation in another variable.

7. Antecedent

In the field of statistics, an antecedent (or 'antecedent variable') is a variable that explains the behaviour of another (subsequent) variable. Usually the antecedent is a chronologically preceding variable, as seen in autoregressive and time-series models. In the context of simple regression, the antecedent variable would be one that would explain the behaviour of both the independent ...

8. Antecedent Variable

Antecedent Variable. Control-related cognitions thus refer to subjective appraisals of any type of cause-effect relations, functional relations between variables, or relations between antecedent variables and consequences. ... Following Klein and Kozlowski's (2000) conceptualization of multilevel research, we grouped these variables further ...

9. Types of Variables in Research & Statistics

Examples. Discrete variables (aka integer variables) Counts of individual items or values. Number of students in a class. Number of different tree species in a forest. Continuous variables (aka ratio variables) Measurements of continuous or non-finite values. Distance.

10. Antecedent Variable

In statistics and social sciences, an antecedent variable is a variable that can help to explain the apparent relationship (or part of the relationship) betw...

11. Variables

"In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the ...

12. Variables in Research

Categorical Variable. This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

13. Research Variables: Types, Uses and Definition of Terms

The purpose of research is to describe and explain variance in the world, that is, variance that occurs naturally in the world or change that we create due to manipulation. Variables are therefore ...

14. Multivariate Analysis

There can be NO antecedent variable that creates the relationship between the independent variable and the dependent variable. This is the really tough condition for researchers to demonstrate, because, in principle, there could be an infinite numbers of antecedent variables that create such a relationship. ... There is one research method ...

15. Variables

In any antecedent-consequent relationship, the antecedent is the independent variable and the consequent is the dependent variable. Study. It has been traditional for the man rather than the woman to receive the check when a couple dines out. ... Test the research hypothesis that the check will be presented to the person showing in-charge ...

16. What is an Antecedent Variable? (Explanation & Example)

Certain antecedent variable is a variable is occurs before the independent and dependent variables under study and ca help explaining the relationship between the two. Him can remember get definition by remembering that the word antecedent verbatim means "previous or preexisting." Examples of Antecedent Variables

17. Idiosyncratic Variables Affecting Functional Analysis Outcomes: A

Further research devoted to manipulating particular MOs and discriminative stimuli associated with the type of therapist and setting will shed light on the behavioral mechanisms responsible for their effects. ... Evaluation of combined-antecedent variables on functional analysis results and treatment of problem behavior in a school setting ...

18. ANTECEDENT VARIABLE

Answer: An antecedent variable is a condition or event that occurs before and is thought to influence the outcome or occurrence of a dependent variable. A dependent variable is the outcome or result of the antecedent variable and is affected by it. 3. What types of variables are classified as antecedent variables?

19. Effects of Antecedent Variables on Disruptive Behavior and Accurate

Future research using these procedures should alter antecedent variables individually to evaluate their effects on behavior. An additional limitation is that the current study focused on assessment rather than treatment. A logical extension to this line of research is to evaluate the effectiveness of treatments based on these types of analyses.

20. Combined Antecedent Variables as Motivating operations within

These limitations may be areas for future research. For both participants, elevated rates of problem behavior were observed within the combined-antecedent test conditions, whereas little or no problem behavior was observed in control or single-antecedent test conditions. ... Because the manner in which combinations of antecedent variables ...

21. Antecedents And Consequences Of Meaningful Work: A Systematic

The research findings are the first body of literature in this field, the second is the antecedent and consequences variables in previous research and the third is the perspectives harmonization ...

22. Antecedent events as predictive variables for behavioral function

The research into and treatment of challenging behavior has usually involved the identification of its function and the manipulation of the events or environmental conditions that influence its occurrence (antecedent variables). The present research explores the relationship between antecedents and behavioral function and the extent to which ...

23. Antecedent: Understanding the Importance of Identifying the Cause

In quantitative research, antecedent variables are often referred to as confounding variables. Confounding variables are variables that affect the relationship between the independent and dependent variables, and they may lead to spurious relationships. Researchers use statistical techniques such as regression analysis and ANOVA to control for ...