Experimental Research Strategy: Manipulating IV and Controlling Extraneous Vars

1. Chapter 7: The Experimental Research Strategy • Manipulating the IV • Controlling Extraneous Variance • Holding Extraneous Vars Constant • Between Subjects Designs • Within Subjects Designs • Multiple-Group Designs • Quantitative IVs • Qualitative IVs • Factorial Designs • Summary

2. Experiment: Characteristics • Manipulation of IV • Hold other vars constant • Participants in all conditions are equivalent • Personal attributes (on average) • Any variables relating to the DV • Usually done by • random ASSIGNMENT to conditions • (random selection is an external validity issue) • Why?

3. Statistics • Descriptive v. inferential • Parametric • Partition vars into ratio of treatment/error • Non-parametric • No assumptions about the distributions

4. Manipulation of IV • Conditions of the IV • Experimental and control conditions • Comparison Conditions • Additional Control and Comparison Conditions • Hypothesis testing • Ruling out specific alternative explanations • Characteristics of a good manipulation • Construct validity • Reliability • Strength • Salience

5. Manipulation of IV • Conditions of the IV • Experimental and control conditions • Equivalence of ? Allows you to rule out nonspecific treatment effects • Any differences between the conditions other than treatment • Similar to placebo effects • Comparison Conditions • How does comparison group differ from control? • It doesn’t • Additional Control and Comparison Conditions • Hypothesis testing • (Bransford &Johnson, ’72) Why three conditions? • No context, context before, context after • Ruling out specific alternative explanations • (Alloy, Abramson, & Viscusi, ’81) added control conditions • Neutral mood, role-play to mood state-> demand

6. Manipulation of IV (con’t) • Characteristics of a good manipulation • Construct validity • Use manipulation check (e.g. Mood from essay writing) • Debrief interview; include in DV; pilot testing • Is it sensitive enough? Are Ps attending to IV? • Reliability • Automate instructions; detailed scripts • Strength • Realistic level (for external validity, and mundane realism), • Salience • Make sure they notice it

7. Manipulation of IV (con’t) • Using multiple stimuli • IV Stimulus: person, object, event • Examples from your project? • Use only one stimulus for a condition • E.g. training program to increase cooperation • What would possible stimuli be? • Avoid confounding: stimulus person (multiple char) • Physical char; personal char

8. Manipulations (con’t) • Controlling Extraneous Variance • External (keep environment; time same) • Internal to P (more difficult) • Random assignment Ps > conditions • Use homogenous sample • Repeated measures (within subjects) • Between subjects designs • To ensure group equivalence • 1. Simple random assignment of Ps • 2. Matched random assignment

9. Between-Subjects Designs • Simple random assignment (most used) • How does this help to ensure group equivalence? • Individual differences (error variance) is randomly distributed across all conditions • How does Kidd &Greenwald’s (’88) do this? • What individual difference variable that may affect the outcome is randomly distributed across conditions? • Memorization skill (does not differentially affect group means) • Is it ok to use “quasi-random” assignment? • What the hell is that?!!!!

10. Between-Subjects Designs • If random assignment doesn’t guarantee group equivalence, what can help? (why doesn’t it?) • Matched random assignment can! • What are some Variables to match on? • Categorical v. continuous vars • Which ones are more difficult to match on? • Compare gender and IQ • Which need a pretest? • Any downside to pretesting? • Does the pretest variable need to be related to the DV?

11. Within-Subjects DesignsPs participate in each condition • Advantages • Control individual differences (Perfect match) • What does this do? • Reduce error (random) variance • Fewer Ps needed (increased power) • Disadvantages • Order effects • Practice effects • Carryover • Sensitization • E.g. Wexley et al. (’72) what was the problem? • Demand effects

12. Within-Ss Controls • Order effects • Counterbalancing • Latin Square • Basic v. balanced • What’s the difference? = Sequence v. order • What’s a washout period? • Differential order effect (Table 7-4) • Sensitization / demand characteristics • Don’t use repeated measures • Order effects can be of theoretical interest • Build into the experiment

13. Multiple Group Designs • Quantitative IVs • Linear relationships • What is an e.g. of a linear IV for your project? • Positive / negative / curvilinear? • What is the minimum levels necessary for quantitative? Why? • 3… 2 points can only define a straight line • DeJong et al. (’76); Feldman & Rosen (’78); Whitley (’82) • What happened? • Qualitative IVs • Give an e.g. of a qualitative IV for your project

14. Multiple-Group Designs • Interpreting the Results • One way ANOVA • Post hoc or Contrasts (Planned comparisons) • What’s the difference? • A priori (Before=contrasts) v. Post hoc (After) • Compare omnibus F with focused F tests • What is the benefit of a priori?

15. INCREASING THE NUMBER OF LEVELS OF AN INDEPENDENT VARIABLE • Provides more information about the relationship than a two level design • Curvilinear Relationship • Inverted-U • Comparing Two or More Groups • I.E. How dogs, cats, and birds as opposed to dogs alone have beneficial effects on nursing home residents

16. LINEAR VERSUS POSITIVE MONOTONIC FUNCTIONS

17. LINEAR VERSUS POSITIVE MONOTONIC FUNCTIONS

18. Factorial Designs • Nature of Factorial Designs • Describing them • 2X2 (how many factors? Levels? Conditions? • 2 factors, 2 levels each = 4 conditions • 4X2 • 2 factors, 4 and 2 levels= 8 conditions • 2X3X2 • 3 factors, 2, 3, & 2 levels =12 conditions • Information provided • Main effects (how many in each example above?) • Interactions (how many 2 way; three way?) • What did Platz & Hosch (’88) find? • What caused the interaction to occur?

19. Factorial Designs • Displaying interactions • Which is clearer? Line or bar graph? (fig 7-5) • Convert from table of means to graph • (fig 7-6, p. 208 -209) • Interpreting interactions • Main effects, interactions, both? • Theory driven? (a priori v. post hoc)

20. Factorial Designs: Forms • Forms of Factorial Designs • Between & Within-Subjects Designs • Between: Each subject participates in only one condition • Within: Each subject participates in all conditions • Mixed: Each subject participates in more than one condition • Platz & Hosch (’88) • Store clerk (between) could it be within? • Customer (within) could it be between? • Manipulated & Measured IVs • Manipulated IV: true experimental design • Measured IV: correlational aspect of design • Caveat: Don’t dichotomize when not needed

21. Factorial Designs: Forms • Design Complexity • Factors and levels (already discussed) • How many Ps needed for Between design • With 10 per condition? • 2X3? • 60 Ps • 3X4X2? • 240 Ps

22. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS

23. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS

24. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS

25. Uses of Factorial Designs • Testing Moderator Hypotheses • Moderator: changes the effects of IV • E.g. Platz & Hosch (’88) race of clerk • Use of ANCOVA & MR • Detecting Order Effects • Table 7-6 • Top: main for condition; no main for order; no interaction • Middle: main for condition; no main for order; interaction • Bottom: main for condition & order; interaction

26. Blocking on Extraneous Vars • Including it as an IV • Ps are grouped on extraneous var and tested by ANOVA as a factorial • Blocking reduces the error term (fig 7-9) • Caveat: Remember that the blocking var cannot be explained as cause

27. Experimental Strategy:Summary • Manipulating the IV • Controlling Extraneous Variance • Holding Extraneous Vars Constant • Between Subjects Designs • Within Subjects Designs • Multiple-Group Designs • Quantitative IVs • Qualitative IVs • Factorial Designs • Summary