Causal-Comparative Causal-Comparative ResearchResearch

Causal-Comparative ResearchCausal-Comparative ResearchChapter SixteenChapter Sixteen

What is Causal-Comparative What is Causal-Comparative Research?Research?

Investigators attempt to determine the cause of Investigators attempt to determine the cause of differences that already exist between or among groups of differences that already exist between or among groups of individuals.individuals.

This is viewed as a form of Associative Research since both This is viewed as a form of Associative Research since both describe conditions that already exist (a.k.a. ex post facto).describe conditions that already exist (a.k.a. ex post facto).

The group difference variable is either a variable that The group difference variable is either a variable that cannot be manipulated or one that might have been cannot be manipulated or one that might have been manipulated but for one reason or another, has not been.manipulated but for one reason or another, has not been.

Studies in medicine and sociology are causal-comparative Studies in medicine and sociology are causal-comparative in nature, as are studies of differences between men and in nature, as are studies of differences between men and women.women.

Similarities and Differences Between Similarities and Differences Between Causal-Comparative and Causal-Comparative and

Correlational ResearchCorrelational Research SimilaritiesSimilarities

Associative researchAssociative research Attempt to explain Attempt to explain

phenomena of interestphenomena of interest Seek to identify Seek to identify

variables that are variables that are worthy of later worthy of later exploration through exploration through experimental researchexperimental research

Neither permits the Neither permits the manipulation of manipulation of variablesvariables

Attempt to explore Attempt to explore causationcausation

DifferencesDifferences Causal studies compare Causal studies compare

two or more groups of two or more groups of subjectssubjects

Causal studies involve Causal studies involve at least one categorical at least one categorical variablevariable

Causal studies often Causal studies often compare averages or compare averages or use crossbreak tables use crossbreak tables instead of scatterplots instead of scatterplots and correlations and correlations coefficientscoefficients

Similarities and Differences Between Similarities and Differences Between Causal-Comparative and Experimental Causal-Comparative and Experimental

ResearchResearch SimilaritiesSimilarities

Require at least one Require at least one categorical variablecategorical variable

Both compare group Both compare group performances to performances to determine relationshipsdetermine relationships

Both compare separate Both compare separate groups of subjectsgroups of subjects

DifferencesDifferences In experimental research, In experimental research,

the independent variable the independent variable is manipulatedis manipulated

Causal studies are likely Causal studies are likely to provide much weaker to provide much weaker evidence for causationevidence for causation

In experimental studies, In experimental studies, researchers can assign researchers can assign subjects to treatment subjects to treatment groupsgroups

The researcher has The researcher has greater flexibility in greater flexibility in formulating the structure formulating the structure of the design in of the design in experimental researchexperimental research

Steps Involved in Causal-Steps Involved in Causal-Comparative ResearchComparative Research

Problem FormulationProblem Formulation The first step is to identify and define the particular The first step is to identify and define the particular

phenomena of interest and consider possible causesphenomena of interest and consider possible causes SampleSample

Selection of the sample of individuals to be studied by Selection of the sample of individuals to be studied by carefully identifying the characteristics of select groupscarefully identifying the characteristics of select groups

InstrumentationInstrumentation There are no limits on the types of instruments that are used There are no limits on the types of instruments that are used

in Causal-comparative studiesin Causal-comparative studies DesignDesign

The basic design involves selecting two or more groups that The basic design involves selecting two or more groups that differ on a particular variable of interest and comparing them differ on a particular variable of interest and comparing them on another variable(s) without manipulation (see Figure on another variable(s) without manipulation (see Figure 16.1)16.1)

The Basic Causal-Comparative DesignsThe Basic Causal-Comparative Designs

Independent DependentGroup variable variable

(a) I C O(Group possesses (Measurement)

characteristic)

II –C O(Group does (Measurement)not possess

characteristic)

(b) I C1 O(Group possesses (Measurement)characteristic 1)

II C2 O(Group possesses (Measurement)characteristic 2)

Examples of the Basic Causal-Examples of the Basic Causal-Comparative Design Comparative Design (Figure 16.1)(Figure 16.1)

Threats to Internal Validity in Threats to Internal Validity in Causal-Comparative ResearchCausal-Comparative Research

Subject CharacteristicsSubject Characteristics The possibility exists that the groups are not The possibility exists that the groups are not

equivalent on one or more important variablesequivalent on one or more important variables One way to control for an extraneous variable is to One way to control for an extraneous variable is to

match subjectsmatch subjects from the comparison groups on that from the comparison groups on that variablevariable

Creating or finding homogeneous subgroups would Creating or finding homogeneous subgroups would be another way to control for an extraneous variablebe another way to control for an extraneous variable

The third way to control for an extraneous variable is The third way to control for an extraneous variable is to use the technique of to use the technique of statistical matchingstatistical matching

(Figure 16.2)(Figure 16.2)

Does a Threat to Internal Validity Exist? (Figure 16.3)Does a Threat to Internal Validity Exist? (Figure 16.3)

Other ThreatsOther Threats

Loss of subjectsLoss of subjects LocationLocation InstrumentationInstrumentation HistoryHistory MaturationMaturation

Data collector biasData collector bias Instrument decayInstrument decay AttitudeAttitude RegressionRegression Pre-test/treatment Pre-test/treatment

interaction effectinteraction effect

Evaluating Threats to Internal Validity in Evaluating Threats to Internal Validity in Causal-Comparative StudiesCausal-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 Step 1: What specific factors are known to affect the variable on which groups are being compared or may variable on which groups are being compared or may be logically be expected to affect this variable?be logically be expected to affect this variable?

Step 2: What is the likelihood of the comparison Step 2: What is the likelihood of the comparison groups differing on each of these factors?groups differing on each of these factors?

Step 3: Evaluate the threats on the basis of how likely Step 3: Evaluate the threats on the basis of how likely they are to have an effect and plan to control for they are to have an effect and plan to control for them.them.

Data AnalysisData 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.

Associations Between Categorical Associations Between Categorical VariablesVariables

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.