Causal Comparative Research Design Assoc. Prof. Dr. Şehnaz Şahinkarakaş

Introduction to Causal-Comparative Research A causal-comparative study is a study in which the researcher attempts to determine the cause, or reason, for pre-existing differences in groups of individuals called an “ex post facto” study because both the effect and the hypothetized cause have already occurred and must be studied in retrospect.

The aim of causal-comparative research is to determine the cause of existing differences among groups. Correlational research involves collecting data on TWO or more variables on ONE group; Causal comparative research involves the collection of data on ONE independent variable for TWO or more groups.

Can be an alternative to experiments. The basic causal-comparative research begins with a noted difference between two groups and the causes for or consequences of this difference is looked for Can be an alternative to experiments. E.g. If conducting the experiment is costly or time-consuming, you can find groups that are already using what you want to test and conduct a causal-comparative research.

Important points to know Exact knowledge of variables is required There are independent and dependent variables Pre-defined groups are compared No manipulation of variables No random assignment of individuals or groups Degree of cause-effect is uncertain.

Advantages Allows cause-effect relationship study when subject manipulation is impossible or very difficult Allows to study many relationships within a given population at one time.

Problems Degree at which a researcher can determine the causal-effect with any measure of certainty is not known In relationship study of X and Y, it may reveal X causes Y, Y causes X, or another variable Z cause both X and Y.

Similarities between causal-comparative and correlational research Both seek to explore relationships among variables Both try to identify variables that are worthy of exploration through experimental research Neither allow manipulation Both attempt to explore causation but causation must be argued: no causal statements.

Differences between causal-comparative and correlational research Correlational Resarch Causal-Comparative Research Requires a score on each variable for each subject Studies two or more groups of subjects Investigates two or more quantitative variables Involves at least one categorical variable (e.g. gender) Analyzes data using scatterplots and/or correlation coefficients Compares averages or uses crossbreak tables

Similarities between causal-comparative and experimental research Both require at least one categorical variable (e.g. gender) Both compare group performances to determine relationships Both compare separate groups of subjects .

Differences between causal-comparative and experimental research Causal-Comparative Research Independent variable is manipulated No manipulation Provides stronger evidence for causation Provides weaker evidence for causation Researcher has flexibility in forming the structure of design Groups are already formed

Designing Groups Defined Group: Group must be defined as the one that possesses the characteristic to be studied Definition must be precise for understanding and interpretation of findings E.g. Science literacy study defined one teacher population as being “high context-enriched” in their instruction and the other as “low context-enriched”

Comparison Groups Select group not possessing (or not to the same degree) the defined characteristic It usually has similar characteristics with exception of the variable that is the focus of the study.

A sample for causal-comparative research Group Independent Variable Dependent Variable (a) I C Participation (has the characteristic) O Level of motivation II -C Non-participation (does not have the characteristic) (b) C1 Counselors (one characteristic) Amount of job satisfaction C2 Teachers (another characteristic) 0 Amount of job satisfaction

Strengthening research design Because the participants are not selected randomly and there is no manipulation, internal validity is threatened. Thus, one (or two) of the following techniques must be used while forming the groups ( can be selected in the statistics program): Pair-wise Matching Extreme groups Analysis of covariance (ANCOVA)

Instrumentation / Data Collection Different types of instruments can be used in causal-comparative research: achievement tests, questionnaires, observational devices, attitudinal measures, etc.

Data Analysis A variety of descriptive and inferential statistics can be used Desctiptive statistics Mean, median, mode Standard deviation Inferential statistics T-test ANOVA Chi-square

Interpreting the Results The results must be interpreted with caution. As with correlational studies, they identify relationships but do not prove cause and effect. There are two main ways to strengthen the interpretability of such studies: Formulating alternative hypotheses and investigating them whenever possible Examining all of the variables if the dependent variable is categorical