Causal-Comparative Design — Previous Year Questions

📊 Topic Overview

Metric	Value
Papers Appeared	9 of 28 (32.1%)
Priority	🟡 MEDIUM
Section Distribution	Sec A: 3 | Sec B: 5 | Sec C: 1
Most Repeated	Similarities and differences with correlational/experimental design (5 times)
Probability	Medium–High

🎯 Must-Know Concepts

1. Definition and nature of causal-comparative design
2. Similarities and differences with correlational research
3. Similarities and differences with experimental research
4. Strengths and limitations
5. Steps in causal-comparative research

---

🔥 Key Question: Causal-Comparative vs Correlational vs Experimental (Section B)

Answer (~300 words):

Definition: Causal-comparative research (also called ex-post facto in some frameworks) is a type of non-experimental research in which the researcher attempts to determine the cause or consequence of differences that already exist between groups. The researcher identifies a DV (effect) and looks back retrospectively to identify the probable IV (cause). No manipulation of variables is attempted.

Example: Comparing cognitive performance of children who attended preschool vs those who did not — the researcher did not assign children to these groups; the "treatment" has already occurred.

Causal-Comparative vs Correlational Research:

Dimension	Causal-Comparative	Correlational
Purpose	Determine cause-effect relationships	Determine degree of association
Groups	Two or more pre-formed groups	Single sample
Data type	Group differences on DV	Continuous scores on two or more variables
Analysis	t-test, ANOVA	Correlation coefficient, regression
Causal inference	Weak — retrospective	Not implied
Variable manipulation	None	None

Similarity: Both are non-experimental; neither involves manipulation of IV; neither can definitively establish causality.

Causal-Comparative vs Experimental Research:

Dimension	Causal-Comparative	Experimental
IV manipulation	No	Yes
Random assignment	No	Yes
Control over extraneous variables	Limited	High
Causal inference	Weak	Strong
Group formation	Pre-existing	Researcher-created
Generalisability	Moderate	Varies

Similarity: Both compare groups; both examine possible IV–DV relationships; both may use the same statistical analyses (t-test, ANOVA).

Strengths of Causal-Comparative Design:

• Studies variables that cannot be manipulated (e.g., gender, trauma history, SES).
• Economical — uses naturally occurring groups.
• Useful first step before experimental research.

Limitations:

• Cannot establish causality — direction of effect is ambiguous.
• Groups may differ on variables other than the IV of interest (confounding).
• Selection bias — groups were not randomly formed.

Steps:

1. Define the problem and identify groups.
2. Review literature and formulate hypothesis.
3. Select participants from existing groups.
4. Collect data on the DV.
5. Analyse for group differences.
6. Interpret cautiously — consider alternative explanations.

---

🧠 Memory Aids

Key Difference from Experimental: No MARE — no Manipulation, no Assignment (random), no Randomisation, no Experimental control

---

📚 Related Content

• Causal-Comparative Research
• Correlational Research Design
• Ex-Post Facto Research

✅ Self-Assessment

1. How does causal-comparative research differ from experimental research?
2. Why is causal-comparative research unable to definitively establish causality?
3. What is the key statistical difference between causal-comparative and correlational research?
4. Give an example of a research question suited to causal-comparative design.
5. What is meant by "selection bias" in causal-comparative studies?

💡 Exam Tips

• Appears mostly in Section B — focus on comparison tables.
• Examiners specifically ask for comparative analysis with correlational AND experimental design — prepare both comparisons.
• Note: some IGNOU papers use "causal comparative" and "ex-post facto" interchangeably; acknowledge this when relevant.