Chapter 7: The Experimental Research Strategy

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## Chapter 7: The Experimental Research Strategy

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**1. **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. **2

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

**4. **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. **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. **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. **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. **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. **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. **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. **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. **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. **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. **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. **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. **16 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?

**17. **17 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)

**18. **18 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

**19. **19 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

**20. **20 INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS N = 40
© 2007 The McGraw-Hill Companies, Inc.N = 40
© 2007 The McGraw-Hill Companies, Inc.

**21. **21 INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS n=10
© 2007 The McGraw-Hill Companies, Inc.n=10
© 2007 The McGraw-Hill Companies, Inc.

**22. **22 INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS n=20
© 2007 The McGraw-Hill Companies, Inc. n=20
© 2007 The McGraw-Hill Companies, Inc.

**23. **23 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

**24. **24 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

**25. **25 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