1. Introduction to Research Design Basic Concepts

2. Bivariate Experimental Research

3. Light Switch Experiment Experimental Units / Subjects = classrooms Manipulated IV = position of light switch Randomly assign to groups DV = brightness of room IV effect on DV = signal to be detected EV cause noise in DV

4. Coin Size Experiment IV = size of coin tossed in pool DV = height of wave produced EV = rowdy youngsters in pool Noise may obscure the IV  DV signal Confound: EV entangled with IV

5. Tacker’s Educational Experiment IV = method of instruction, traditional or new DV = student performance on exams Two classes, no random assignment New method significantly > old method Confounding variable: Time of class

6. Nonexperimental Research Observational research “Correlational” is a confusing term best avoided. No variable is manipulated. Best not to use the terms “independent variable” and “dependent variable” Better to use “grouping variable” and “criterion variable.”

7. Alcohol and Reaction Time Observation Participants = folks randomly sampled in downtown Greenville in evening. Grouping variable = have been drinking or not. Criterion variable = score on reaction time task. Correlation (r,  ) is statistically significant. Can we make a causal inference?

8. Reanalyze the data with Independent Samples t or ANOVA F Groups are significantly different. Can we make a causal inference?

9. Alcohol and Reaction Time Experiment Randomly assign participants to groups. One group drinks alcohol, the other not. IV = alcohol consumption DV = score on reaction time task Correlation (r,  ) is statistically significant. Can we make a causal inference?

10. Reanalyze the data with Independent Samples t or ANOVA F Groups are significantly different. Ind. Samples t and ANOVA F can be shown to be special cases of corr/regression analysis. Causal inference and how the data were collected, not how they were analyzed.

11. Alcohol and Reaction Time Observation 2 Participants = persons downtown in evening. Predictor variable (IV) = blood alcohol level Criterion variable (DV) = reaction time Correlation/regression analysis. Can I make a causal inference?

12. Third Variable Explanation

13. Casual Inference To infer that X is a cause of Y Show that X precedes Y. Show that X and Y and correlated. Rule out noncausal explanations. –establish prior equivalence of treatment groups –treat groups differently (manipulate IV) –demonstrate that groups differ on DV

14. X Causes But Not Correlated With Y This sounds impossible, but a case can be made. X has a direct causal effect on Y with magnitude.25. X has a direct causal effect on M with magintude.5 M has a direct causal effect on Y with magnitude -.5

15. The indirect effect of X through M on Y is.5(-.5) = -.25. The total effect (correlation) of X on Y is the sum of its direct effect and its indirect effect..25 + (-.25) = 0.

16. Design Notation N X O 1,2 N O 1,2 One group per row. Time flows from left to right. N for nonrandom assignment, R for random. X is an experimental treatment. O is an observation. –subscripts represent different variables.

17. Internal Validity The degree to which the design allows you to determine whether or not the experimental treatment affected the dependent variable in this research: as the IV was manipulated here as the DV was measured here with the subjects employed here