1. Research Methods and Data Analysis in Psychology Spring 2015 Kyle Stephenson

2. Overview – Day 11 Review Experimental Design ▫One-way designs ▫Factorial designs ▫Main effects & interaction effects ▫Mixed designs

3. Review What do you remember from last class?

4. Research Methods Basic Concepts Scientific Method Variance Effect Size Measurement Selecting Participants Research Design Descriptive Research Correlational Research Cross-sectional Designs Longitudinal Designs Experimental Studies Simple Experiments Advanced Experimental Design Statistics Descriptive Central Tendency Variation Distributions Outliers Graphing Inferential Correlation Pearson’s R Regression Means Differences T-tests ANOVA Interactions Presentation of Findings Scientific Writing Oral Presentation Ethics

5. Research Methods Basic Concepts Scientific Method Variance Effect Size Measurement Selecting Participants Research Design Descriptive Research Correlational Research Cross-sectional Designs Longitudinal Designs Experimental Studies Simple Experiments Advanced Experimental Design Statistics Descriptive Central Tendency Variation Distributions Outliers Graphing Inferential Correlation Pearson’s R Regression Means Differences T-tests ANOVA Interactions Presentation of Findings Scientific Writing Oral Presentation Ethics

6. How Do We Confirm Causation? Experimental research is the only way to confidently establish a causal relationship between factors

7. A Tall Order: Control ALL extraneous variables except the one you’re interested in (IV) If everything else is held constant and you manipulate only the IV, then any change in the DV must be attributable to the change in IV

8. Partitioning of Variance Error Variance Systematic Variance Experimental Variance Confound Variance

9. Take-home Goal of an experiment is to control for confounding variables (necessary to infer causation) Internal validity is the degree to which you are successful in controlling confounds There are many threats to internal validity that must be accounted for

10. One-Way Designs An experiment needs (at least) ▫One IV with at least two levels (need to compare something) So, simplest experiment has one IV (“one-way”) with two levels One-way designs can be ▫Randomized groups ▫Matched subjects ▫Repeated measures

11. Pre-test? Measure DV both before and after experiment ▫Pros:  Make sure groups didn’t differ pre-experiment  More statistically powerful  See how much DV changed ▫Con:  Potential pre-test sensitization

12. Why Study More Than One IV? Saves time & money Can look at interactions Experiments with 2+ IVs are called “factorial designs” – each IV is a “factor.”

13. How Complex Is Your Study? # of IVs # of levels for each IV A “2 x 2 factorial” (read “2-by-2”) is a design with two independent variables, each with two levels. A “3 x 3 factorial” has two independent variables, each with three levels. A “2 x 2 x 4 factorial” has three independent variables, two with two levels, and one with four levels.

14. Main Effects & Interaction Effects The main effect of an independent variable is the effect of that independent variable while ignoring the effects of all other independent variables in the design. Pretend you only have one IV in your experiment How many main effects?

15. Main Effects & Interaction Effects An interaction occurs when the effect of one independent variable differs across the levels of another independent variable. The effect of one IV depends on the level of the other How many interaction effects?

16. Graph of an Interaction Variable A had a different effect on participants in Condition B1 than on those in Condition B2.

17. Graph of an Interaction #2

18. Mixed Designs Between-Within Designs ▫participants are randomly assigned to only one level of some independent variable(s) but receive every level of other independent variable(s) “Expericorr” Designs ▫designs that include both independent variables (that are manipulated) and participant variables (that are measured)

19. Take-Home Experiments can range from very simple to impossibly complex Interaction effects are often most interesting, but require ▫2+ IVs ▫More complicated statistics ▫Good conceptual understanding