1. Causal-comparative Research existing differences To determine the cause for, or consequences of, existing differences in groups of individuals ex post facto Also referred to as ‘ex post facto’ research (Latin for ‘after the fact’) – retrospection

2. Cause for / Consequence of Cause for – inquiry teaching method vs. lecture teaching method (difference in performance) Consequences of – vegetarian diet vs. non- vegetarian diet (difference in mental health) Any other examples?

3. Questions… 1. How is a causal-comparative study similar to a descriptive study? 2. How is a causal-comparative study similar to a correlational study? 3. How is a causal-comparative study different from an experimental study?

4. When to use a causal- comparative design? 1. When it is unethical to manipulate an independent variable (e.g. diet) 2. When the independent variable CANNOT be manipulated (e.g. sex, ethnicity, etc.) 3. When the independent variable not been changed due to ignorance or negligence (e.g. teaching methods)

5. Did you know? Causal-comparative studies are most common in the field of medicine and sociology Why?

6. Which of the following questions would lend themselves well to causal- comparative research? 1. How many students were enrolled in PSYC101 this semester? 2. Which subject do high school students like least? 3. How do elementary school teachers teach phonics? 4. Are two-year-old girls more aggressive than two-year-old boys? 5. How might Jimmy Thomas be helped to read? 6. Is teacher enthusiasm related to student success in academic classes? 7. What is the best way to teach arithmetic? 8. Do female students perform better in literature classes than male students? 9. Does sleep (amount of time) affect academic performance of students at college?

7. Weaknesses 1. Lack randomization…so what? 2. Inability to manipulate an independent variable…so what?

8. Threats to internal validity Location Instrumentation Loss of subjects

9. Data analysis 1. Construct frequency polygons (to compare data graphically) 2. Calculate means and standard deviations 3. Statistical testing – t-test or ANOVA do notprove 4. Results of causal-comparative studies should always be interpreted with caution…they do not prove cause and effect!