chapter 7: the experimental research strategy
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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. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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?
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Statistics
Descriptive v. inferential Parametric
Partition vars into ratio of treatment/error Non-parametric
No assumptions about the distributions
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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
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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
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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
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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
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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
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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?!!!!
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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?
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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
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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
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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
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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?
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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
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LINEAR VERSUS POSITIVE MONOTONIC FUNCTIONS
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LINEAR VERSUS POSITIVE MONOTONIC FUNCTIONS
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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?
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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)
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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
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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
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INCREASING THE NUMBER OF INDEPENDENT VARIABLES:
FACTORIAL DESIGNS
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INCREASING THE NUMBER OF INDEPENDENT VARIABLES:
FACTORIAL DESIGNS
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INCREASING THE NUMBER OF INDEPENDENT VARIABLES:
FACTORIAL DESIGNS
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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
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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
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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