1. Experiment Basics: Designs Psych 231: Research Methods in Psychology

2. Announcements Exam 2 a week from today First draft of Class Experiment APA paper due in Labs this week

3. Experimental designs So far we’ve covered a lot of the about details experiments generally Now let’s consider some specific experimental designs. Some bad (but common) designs Some good designs 1 Factor, two levels 1 Factor, multi-levels Factorial (more than 1 factor) Between & within factors

4. 1 factor - 2 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable How does anxiety level affect test performance? lowmoderate test performance anxiety

5. 1 factor - 3 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable High Test lowmod test performance anxiety high

6. Factorial experiments Two or more factors Some vocabulary Factors - independent variables Levels - the levels of your independent variables 2 x 4 design means two independent variables, one with 2 levels and one with 4 levels “Conditions” or “groups” is calculated by multiplying the levels, so a 2x4 design has 8 different conditions A1 A2 B1B2B3B4

7. Factorial experiments Two or more factors Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables Interaction effects - how your independent variables affect each other Example: 2x2 design, factors A and B Interaction: At A1, B1 is bigger than B2 At A2, B1 and B2 don’t differ Everyday interaction = “it depends on …” A A1 A2 Dependent Variable B1 B2

8. Interaction effects Rate how much you would want to see a new movie (1 no interest, 5 high interest): Hail, Caesar! – new Cohen Brothers movie in 2016 Ask men and women – looking for an effect of gender Not much of a difference: no effect of gender

9. Interaction effects Maybe the gender effect depends on whether you know who is in the movie. So you add another factor: Effect of gender depends on whether George or Scarlett stars in the movie or not This is an interaction A video lectureA video lecture from ThePsychFiles.com podcast Suppose that George Clooney or Scarlett Johansson might star. You rate the preference if he were to star and if he were not to star.

10. Results of a 2x2 factorial design The complexity & number of outcomes increases: A = main effect of factor A B = main effect of factor B AB = interaction of A and B With 2 factors there are 8 basic possible patterns of results: 1) No effects at all 2) A only 3) B only 4) AB only 5) A & B 6) A & AB 7) B & AB 8) A & B & AB

11. 2 x 2 factorial design Condition mean A1B1 Condition mean A2B1 Condition mean A1B2 Condition mean A2B2 A1A2 B2 B1 Marginal means B1 mean B2 mean A1 meanA2 mean Main effect of B Main effect of A Interaction of AB What’s the effect of A at B1? What’s the effect of A at B2?

12. Main effect of A Main effect of B Interaction of A x B A B A1 A2 B1 B2 Main Effect of A Main Effect of B 60 45 30 60 30 60 30 A A1 A2 Dependent Variable B1 B2 ✓ X X Examples of outcomes At A1: B1 = B2 At A2: B1 = B2 The effect of A doesn’t depend on level of B

13. Main effect of A Main effect of B Interaction of A x B A B A1 A2 B1 B2 Main Effect of A Main Effect of B 45 60 30 45 30 60 A A1 A2 Dependent Variable B1 B2 ✓ X X Examples of outcomes At A1: B1 - B2 = 30 At A2: B1 - B2 = 30 The effect of A doesn’t depend on level of B

14. Main effect of A Main effect of B Interaction of A x B A B A1 A2 B1 B2 Main Effect of A Main Effect of B 45 60 30 60 A A1 A2 Dependent Variable B1 B2 ✓ X X Examples of outcomes At A1: B1 - B2 = +30 At A2: B1 - B2 = -30 The effect of A does depend on level of B

15. Main effect of A Main effect of B Interaction of A x B A B A1 A2 B1 B2 Main Effect of A Main Effect of B 45 30 60 30 A A1 A2 Dependent Variable B1 B2 ✓ ✓ ✓ Examples of outcomes At A1: B1 - B2 = 0 At A2: B1 - B2 = 30 The effect of A doesn’t depend on level of B

16. Anxiety and Test Performance test performance highlowmod anxiety easy medium hard 80 65 anxiety lowmodhigh 8035 50 70 80 main effect of difficulty 8060 main effect of anxiety Let’s add another variable: test difficulty. easy medium hard Test difficulty Interaction ? Yes: effect of anxiety depends on level of test difficulty

17. Factorial designs Consider the results of our class experiment An Interaction between cell phone and site type Resource: Dr. Kahn's reporting stats page Main effect of site type Main effect of cell phone ✓ -0.36 0.25 ✓ X

18. Factorial Designs Advantages Interaction effects –Can’t “see” interaction effects without factorial design –Consider the interaction effects before trying to interpret the main effects – Adding factors decreases the variability –Because you are controlling more of the variables that influence the dependent variable –This increases the statistical Power (your ability to detect an effect) of the statistical tests – Increases generalizability of the results –Because you have a situation closer to the real world (where all sorts of variables are interacting)

19. Factorial Designs Disadvantages Experiments become very large, and unwieldy The statistical analyses get much more complex Interpretation of the results can get hard In particular for higher-order interactions Higher-order interactions (when you have more than two interactions, e.g., ABC). Action MovieRomantic Comedy Here there is a three way interaction: gender X who stars X type of movie

20. Experimental designs So far we’ve covered a lot of the about details experiments generally Now let’s consider some specific experimental designs. Some bad (but common) designs Some good designs 1 Factor, two levels 1 Factor, multi-levels Factorial (more than 1 factor) Between & within factors

21. Example What is the effect of presenting words in color on memory for those words? Two different designs to examine this question Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab So you present lists of words for recall either in color or in black-and-white.

22. participants Colored words BW words Test  2-levels  Each of the participants is in only one level of the IV Between-Groups Factor Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab levels

23. participants Colored words BW words Test  2-levels, All of the participants are in both levels of the IV Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab levels  Sometimes called “repeated measures” design Within-Groups Factor

24. Between vs. Within Subjects Designs Within-subjects designs All participants participate in all of the conditions of the experiment. participants Colored words BW words Test participants Colored words BW words Test Between-subjects designs Each participant participates in one and only one condition of the experiment.

25. Within-subjects designs All participants participate in all of the conditions of the experiment. participants Colored words BW words Test participants Colored words BW words Test Between-subjects designs Each participant participates in one and only one condition of the experiment. Between vs. Within Subjects Designs

26. Between subjects designs Advantages: Independence of groups (levels of the IV) Harder to guess what the experiment is about without experiencing the other levels of IV Exposure to different levels of the independent variable(s) cannot “contaminate” the dependent variable Sometimes this is a ‘must,’ because you can’t reverse the effects of prior exposure to other levels of the IV No order effects to worry about Counterbalancing is not required participants Colored words BW words Test Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab

27. Between subjects designs Disadvantages Individual differences between the people in the groups Excessive variability Non-Equivalent groups participants Colored words BW words Test Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab

28. Individual differences The groups are composed of different individuals participants Colored words BW words Test

29. Individual differences The groups are composed of different individuals participants Colored words BW words Test Excessive variability due to individual differences Harder to detect the effect of the IV if there is one R NR R

30. Individual differences The groups are composed of different individuals participants Colored words BW words Test Non-Equivalent groups (possible confound) The groups may differ not only because of the IV, but also because the groups are composed of different individuals

31. Dealing with Individual Differences Strive for Equivalent groups Created equally - use the same process to create both groups Treated equally - keep the experience as similar as possible for the two groups Composed of equivalent individuals Random assignment to groups - eliminate bias Matching groups - match each individuals in one group to an individual in the other group on relevant characteristics

32. Matching groups Group AGroup B Matched groups Trying to create equivalent groups Also trying to reduce some of the overall variability Eliminating variability from the variables that you matched people on Red Short 21yrs Blue tall 23yrs Green average 22yrs Brown tall 22yrs Color Height Age matched Red Short 21yrs matched Blue tall 23yrs matched Green average 22yrs matched Brown tall 22yrs

33. Within-subjects designs All participants participate in all of the conditions of the experiment. participants Colored words BW words Test participants Colored words BW words Test Between-subjects designs Each participant participates in one and only one condition of the experiment. Between vs. Within Subjects Designs

34. Within subjects designs Advantages: Don’t have to worry about individual differences Same people in all the conditions Variability between conditions is smaller (statistical advantage) Fewer participants are required

35. Within subjects designs Disadvantages Range effects Order effects: Carry-over effects Progressive error Counterbalancing is probably necessary to address these order effects

36. Within subjects designs Range effects – (context effects) can cause a problem The range of values for your levels may impact performance (typically best performance in middle of range). Since all the participants get the full range of possible values, they may “adapt” their performance (the DV) to this range.

37. test Condition 2Condition 1 test Order effects Carry-over effects Transfer between conditions is possible Effects may persist from one condition into another e.g. Alcohol vs no alcohol experiment on the effects on hand-eye coordination. Hard to know how long the effects of alcohol may persist. How long do we wait for the effects to wear off?

38. Order effects Progressive error Practice effects – improvement due to repeated practice Fatigue effects – performance deteriorates as participants get bored, tired, distracted

39. Dealing with order effects Counterbalancing is probably necessary This is used to control for “order effects” Ideally, use every possible order (n!, e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc ). All counterbalancing assumes Symmetrical Transfer The assumption that AB and BA have reverse effects and thus cancel out in a counterbalanced design

40. Counterbalancing Simple case Two conditions A & B Two counterbalanced orders: AB BA participants Colored words BW words Test Colored words BW words Test

41. Counterbalancing Often it is not practical to use every possible ordering Partial counterbalancing Latin square designs – a form of partial counterbalancing, so that each group of trials occur in each position an equal number of times

42. Partial counterbalancing Example: consider four conditions Recall: ABCD = 4! = 24 possible orders 1) Unbalanced Latin square: each condition appears in each position (4 orders) DCBA ADCB BADC CBAD Order 1 Order 2 Order 3 Order 4

43. Partial counterbalancing 2) Balanced Latin square: each condition appears before and after all others (8 orders) ABDC BCAD CDBA DACB ABCD BCDA CDAB DABC Example: consider four conditions Recall: ABCD = 4! = 24 possible orders

44. Mixed factorial designs Treat some factors as within-subjects (participants get all levels of that factor) and others as between-subjects (each level of this factor gets a different group of participants). This only works with factorial (multi-factor) designs