1. Non-Experimental Design Where are the beakers??

2. Cautions A relationship between two variables does NOT mean one causes the other (Think about the correlation between reading achievement and body weight) Correlation ≠ Causation

3. Cautions Lack of variability in scores (e.g. everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships Large sample sizes and/or using many variables can identify significant relationships for statistical reasons and not because the relationships really exist (Avoid shotgun approach)

4. Correlational Designs Guidelines for interpreting the size of correlation coefficients –Much larger correlations are needed for predictions with individuals than with groups Crude group predictions can be made with correlations as low as.40 to.60 Predictions for individuals require correlations above.75

5. Correlational Designs Guidelines for interpreting the size of correlation coefficients –Exploratory studies Correlations of.25 to.40 indicate the need for further research Much higher correlations are needed to confirm or test hypotheses

6. How would you study… The effect of smoking on student achievement? Whether children from abusive parents have lower self-esteem than children of non-abusive parents? The differences in work ethic between students of high, middle, and low socio- economic status?

7. Why does most educational research use non-experimental designs? There are ethical and logistical considerations that often impede the use of experimental studies.

8. What is the purpose of non-experimental designs? Describe current existing characteristics such as achievement, attitudes, relationships, etc. There is no manipulation of an independent variable

9. Causal-Comparative Design A study in which the researcher attempts to determine the reason for pre-existing differences in groups of individuals At least two different groups are compared on a dependent variable or measure of performance (called the “effect”) because the independent variable (called the “cause”) has already occurred or cannot be manipulated

10. Causal-Comparative Design A “kissing cousin” to correlational research design. Causes studied after they have exerted their effect on another variable.

11. Causal-Comparative Design Drawbacks –Difficult to establish causality based on collected data. –Unmeasured variables (confounding variables) are always a source of potential alternative causal explanations.

12. Causal-Comparative Example Green & Jaquess (1987) –Interested in the effect of high school students’ part-time employment on their academic achievement. –Sample: 477 high school juniors who were unemployed or employed > 10 hours/wk.

13. To Summarize… Can non-experimental research claim causality? NO!Why?