McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Causal-Comparative Research Chapter Sixteen

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Causal-Comparative Research Chapter Sixteen

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. What is Causal-Comparative Research? Investigators attempt to determine the cause of differences that already exist between or among groups of individuals. Investigators attempt to determine the cause of differences that already exist between or among groups of individuals. This is viewed as a form of Associative Research since both describe conditions that already exist (a.k.a. ex post facto). This is viewed as a form of Associative Research since both describe conditions that already exist (a.k.a. ex post facto). The group difference variable is either a variable that cannot be manipulated or one that might have been manipulated but for one reason or another, has not been. The group difference variable is either a variable that cannot be manipulated or one that might have been manipulated but for one reason or another, has not been. Studies in medicine and sociology are causal-comparative in nature, as are studies of differences between men and women. Studies in medicine and sociology are causal-comparative in nature, as are studies of differences between men and women.

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Similarities and Differences Between Causal-Comparative and Correlational Research Similarities Similarities Associative research Associative research Attempt to explain phenomena of interest Attempt to explain phenomena of interest Seek to identify variables that are worthy of later exploration through experimental research Seek to identify variables that are worthy of later exploration through experimental research Neither permits the manipulation of variables Neither permits the manipulation of variables Attempt to explore causation Attempt to explore causation Differences Differences Causal studies compare two or more groups of subjects Causal studies compare two or more groups of subjects Causal studies involve at least one categorical variable Causal studies involve at least one categorical variable Causal studies often compare averages or use crossbreak tables instead of scatterplots and correlations coefficients Causal studies often compare averages or use crossbreak tables instead of scatterplots and correlations coefficients

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Similarities and Differences Between Causal-Comparative and Experimental Research Similarities Similarities Require at least one categorical variable Require at least one categorical variable Both compare group performances to determine relationships Both compare group performances to determine relationships Both compare separate groups of subjects Both compare separate groups of subjects Differences Differences In experimental research, the independent variable is manipulated In experimental research, the independent variable is manipulated Causal studies are likely to provide much weaker evidence for causation Causal studies are likely to provide much weaker evidence for causation In experimental studies, researchers can assign subjects to treatment groups In experimental studies, researchers can assign subjects to treatment groups The researcher has greater flexibility in formulating the structure of the design in experimental research The researcher has greater flexibility in formulating the structure of the design in experimental research

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Steps Involved in Causal- Comparative Research Problem Formulation Problem Formulation The first step is to identify and define the particular phenomena of interest and consider possible causes The first step is to identify and define the particular phenomena of interest and consider possible causes Sample Sample Selection of the sample of individuals to be studied by carefully identifying the characteristics of select groups Selection of the sample of individuals to be studied by carefully identifying the characteristics of select groups Instrumentation Instrumentation There are no limits on the types of instruments that are used in Causal-comparative studies There are no limits on the types of instruments that are used in Causal-comparative studies Design Design The basic design involves selecting two or more groups that differ on a particular variable of interest and comparing them on another variable(s) without manipulation (see Figure 16.1) The basic design involves selecting two or more groups that differ on a particular variable of interest and comparing them on another variable(s) without manipulation (see Figure 16.1)

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. The Basic Causal-Comparative Designs IndependentDependent Groupvariablevariable (a)ICO (Group possesses(Measurement) characteristic) II–CO (Group does(Measurement) not possess characteristic) (b)IC 1 O (Group possesses(Measurement) characteristic 1) IIC 2 O (Group possesses(Measurement) characteristic 2)

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Examples of the Basic Causal- Comparative Design (Figure 16.1)

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Threats to Internal Validity in Causal-Comparative Research Subject Characteristics Subject Characteristics The possibility exists that the groups are not equivalent on one or more important variables The possibility exists that the groups are not equivalent on one or more important variables One way to control for an extraneous variable is to match subjects from the comparison groups on that variable One way to control for an extraneous variable is to match subjects from the comparison groups on that variable Creating or finding homogeneous subgroups would be another way to control for an extraneous variable Creating or finding homogeneous subgroups would be another way to control for an extraneous variable The third way to control for an extraneous variable is to use the technique of statistical matching The third way to control for an extraneous variable is to use the technique of statistical matching

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. (Figure 16.2)

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Does a Threat to Internal Validity Exist? (Figure 16.3)

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Other Threats Loss of subjects Loss of subjects Location Location Instrumentation Instrumentation History History Maturation Maturation Data collector bias Data collector bias Instrument decay Instrument decay Attitude Attitude Regression Regression Pre-test/treatment interaction effect Pre-test/treatment interaction effect

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Evaluating Threats to Internal Validity in Causal-Comparative Studies Involves three sets of steps as shown below: Involves three sets of steps as shown below: Step 1: What specific factors are known to affect the variable on which groups are being compared or may be logically be expected to affect this variable? Step 1: What specific factors are known to affect the variable on which groups are being compared or may be logically be expected to affect this variable? Step 2: What is the likelihood of the comparison groups differing on each of these factors? Step 2: What is the likelihood of the comparison groups differing on each of these factors? Step 3: Evaluate the threats on the basis of how likely they are to have an effect and plan to control for them. Step 3: Evaluate the threats on the basis of how likely they are to have an effect and plan to control for them.

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Data Analysis In a Causal-Comparative Study, the first step is to construct frequency polygons. Means and SD are usually calculated if the variables involved are quantitative. The most commonly used inference test is a t-test for differences between means. ANCOVAs are useful for these types of studies. Results should always be interpreted with caution since they do not prove cause and effect.

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Associations Between Categorical Variables There are no techniques analogous to partial correlation or the other techniques that have evolved from correlational research that can be used with categorical variables. Prediction from crossbreak tables is much less precise than from scatterplots. There are relatively few questions of interest in education that involve two categorical variables. It is common to find researchers who treat quantitative variables conceptually as if they were categorical, but nothing is gained by this procedure and it should be avoided.