Topics Covered in this Session

• Definition and Characteristics of Causal Comparative Research
• Appropriateness/Limitations

Cause and Effect in Educational Research

Experimental, quasi-experimental, and causal-comparative research methods are frequently studied together because they all try to show cause and effect relationships among two or more variables. To conduct cause and effect research, one variable(s) is considered the causal or independent variable and one variable(s) is considered the effect or dependent variable.

Causal Comparative Research

• Cause and Effect in Educational Research
• Definition and Characteristics of Causal Comparative Research
• Appropriateness/Limitations

Cause and Effect in Educational Research

Experimental, quasi-experimental, and causal-comparative research methods are frequently studied together because they all try to show cause and effect relationships among two or more variables. To conduct cause and effect research, one variable(s) is considered the causal or independent variable and one variable(s) is considered the effect or dependent variable.

I. Comments on Causal Comparative Research

In Session 9, we saw how experimental research attempts to manipulate the causal (independent) variable(s) in order to change an effect (dependent) variable(s). Causal comparative research attempts to attribute a change in the effect variable(s) when the causal variable(s) cannot be manipulated.

For example: if you wanted to study the effect of socioeconomic variables such as sex, race, ethnicity, or income on academic achievement, you might identify two existing groups of students: one group - high achievers; second group - low achievers. You then would study the differences of the two groups as related to socioeconomic variables that already occurred or exist as the reason for the difference in the achievement between the two groups. To establish a cause effect relationship in this type of research you have to build a strongly persuasive logical argument. Because it deals with variables that have already occurred or exist, causal-comparative research is also referred to as ex post facto research.

The most common statistical techniques used in causal comparative research are analysis of variance and t-tests wherein significant differences in the means of some measure (i.e. achievement) are compared between or among two or more groups.

Data Sources

• Raw scores such as test scores
• Measures such as grade point averages
• Judgements, and other assessments made of the subjects involved

Research Tools

• Standardized tests
• Surveys
• Structured interviews

Procedural Considerations

• The most important procedural consideration in doing causal comparative research is to identify two or more groups which are demonstrably different in an educationally important way such as high academic achievement versus low academic achievement. An attempt is then made to identify the cause which resulted in the differences in the effect (i.e. academic achievement). The cause (i.e. race, sex, income, etc.) has already had its effect and cannot be manipulated, changed or altered. In selecting subjects for causal- comparative research, it is most important that they be identical as possible except for the difference (i.e. independent variable - race, sex, income) which may have caused the demonstrated effect (i.e. dependent variable - academic achievement)
• Hypotheses are generally used
• Statistics are extensively used in experimental research and include measures of spread or dispersion such as: t-tests; Chi-Square; analysis of variance as well as measures of relationship such as: Pearson Product-Moment Coefficient; Spearman Rank Order Coefficient; Phi Correlation Coefficient; regression

SPECIAL PROCEDURAL CONSIDERATIONS A. B. Statistics are extensively used in causal comparative research and include measures of relationship such as: Pearson Product-Moment Coefficient; Spearman Rank Order Coefficient; Phi Correlation Coefficient; Regression; as well as measures of spread or dispersion such as: t-tests; Chi-Square; Analysis of Variance. REPORT PRESENTATION Reports tend to rely on both quantitative and qualitative presentations. Statistical data is almost always provided and supports the overall argument which is used to establish the cause and effect relationship.

Report Presentation

• Reports tend to rely on quantitative presentations
• Statistical data is almost always provided and supports the overall cause-effect argument.