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Experiment Basics: Designs

Experiment Basics: Designs. Psych 231: Research Methods in Psychology. Exam 2 a week from today First draft of Class Experiment APA paper due in Labs this week. Announcements. So far we ’ ve covered a lot of the about details experiments generally

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Experiment Basics: Designs

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

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

  3. 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 Experimental designs

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

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

  6. B1 B2 B3 B4 A1 A2 • 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 Factorial experiments

  7. B1 Dependent Variable B2 A1 A2 A • 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 …” Factorial experiments

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

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

  10. 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 Results of a 2x2 factorial design

  11. Interaction of AB A1 A2 B1 mean B1 Main effect of B B2 mean B2 A1 mean A2 mean Marginal means Main effect of A Condition mean A1B1 What’s the effect of A at B1? What’s the effect of A at B2? Condition mean A2B1 Condition mean A1B2 Condition mean A2B2 2 x 2 factorial design

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

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

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

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

  16. main effect anxiety of difficulty easy low mod high medium 50 hard hard test performance 35 80 35 70 Test difficulty medium 65 65 80 80 easy 80 80 80 low mod high 60 80 60 main effect anxiety of anxiety Let’s add another variable: test difficulty. Yes: effect of anxiety depends on level of test difficulty Interaction ? Anxiety and Test Performance

  17. Consider the results of our class experiment ✓ • Main effect of word type ✓ • Main effect of depth of processing ✓ • An Interaction between word type and depth of processing 1.5 0.7 Factorial designs Resource: Dr. Kahn's reporting stats page

  18. Advantages • Interaction effects • Consider the interaction effects before trying to interpret the main effects • Adding factors decreases the variability • Because you’re controlling more of the variables that influence the dependent variable • This increases the statistical Power 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) Factorial Designs

  19. 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). Factorial Designs

  20. 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 Experimental designs

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

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

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

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

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

  26. Clock Chair Cab Clock Chair Cab Colored words participants Test BW words • 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 Between subjects designs

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

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

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

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

  31. 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 Dealing with Individual Differences

  32. matched matched matched matched Red Short 21yrs Blue tall 23yrs Green average 22yrs Brown tall 22yrs • 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 Group A Group B Red Short 21yrs Blue tall 23yrs Green average 22yrs Color Height Age Brown tall 22yrs Matching groups

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

  34. 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 Within subjects designs

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

  36. 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. Within subjects designs

  37. Condition 1 Condition 2 test test • 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? Order effects

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

  39. 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 Dealing with order effects

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

  41. 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 Counterbalancing

  42. A B C D Order 1 B C D A Order 2 C D A B Order 3 D A B C Order 4 • Example: consider four conditions • Recall: ABCD = 4! = 24 possible orders 1) Unbalanced Latin square: each condition appears in each position (4 orders) Partial counterbalancing

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

  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 Mixed factorial designs

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