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

2. What kind of research is considered the “gold standard” by the Institute of Education Sciences? A.Descriptive B.Causal-Comparative C.Correlational D.Experimental Why?

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

4. 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

5. First… Some Thought Questions

6. Causal-Comparative Design A study in which the researcher attempts to determine the cause, or 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

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

8. 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.

9. 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.

10. Correlational Design Determines whether and to what degree a relationship exists between two or more quantifiable variables.

11. Example of Correlation

12. Correlational Design The degree of the relationship is expressed as a coefficient of correlation Examples –Relationship between math achievement and math attitude –Relationship between degree of a school’s racial diversity and student use of stereotypical language –Your topics?

13. Correlation coefficient… +1.00 strong negative strong positive 0.00 no relationship

14. Advantages of Correlational Design Analysis of relationships among a large number of variables in a single study Information about the degree of the relationship between the variables being studied

15. Cautions A relationship between two variables does not mean one causes the other (Think about the reading achievement and body weight correlations on p. 189) Possibility of low reliability of the instruments makes it difficult to identify relationships

16. 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)

17. Cautions Need to identify your sample to know what is actually being compared. If using predictor variables, time interval between collecting the predictor and criterion variable data is important.

18. 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

19. 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

20. Correlational Designs Criteria for evaluating correlational studies –Causation should not be inferred from correlational studies –Practical significance should not be confused with statistical significance

21. Correlational Designs Criteria for evaluating correlational studies –The size of the correlation should be sufficient for the use of the results (individuals vs groups) –Prediction studies should report the accuracy of predictions for new subjects –Procedures for collecting data should be clearly indicated

22. Think… If you were going to take your action research topic, and create a causal- comparative study, what would it look like? --OR-- If you were going to take your action research project, and create a correlational study, what would it look like?