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Transcript of Research Methods in Psychology Complex Designs. Experiments that involve two or more independent...
![Page 1: Research Methods in Psychology Complex Designs. Experiments that involve two or more independent variables studies simultaneously at least one dependent.](https://reader035.fdocuments.us/reader035/viewer/2022081515/56649cba5503460f949816cf/html5/thumbnails/1.jpg)
Research Methods in Psychology
Complex Designs
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Complex Designs
Experiments that involve• two or more independent variables studies
simultaneously• at least one dependent variable
Simplest complex design• one independent variable with two levels• one dependent variable
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Complex Designs, continued
Factorial combination• combine independent variables in an experiment• pair each level of one IV with each level of the other
IV(s)• example:
Closer examination of the Dittmar, Halliwell, and Ive (2006) study: Barbie and young girls’ body image
They also examined the IV of grade in school (kindergarten, 1st, 2nd) as a natural groups variable
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Complex Designs, continued
Factorial combination of 2 IVs• version of picture book: Barbie, Emme, neutral• grade: kindergarten, 1st, 2nd • factorial combination: 9 conditions• referred to as a “3 x 3”—2 IVs, each with 3 levels
9872nd
6541st
321kindergarten
Grade
NeutralEmmeBarbie
Version of Picture Book
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Complex Designs, continued
Factorial combination allowed Dittmar et al. (2006) to examine• overall effect of Version of Picture book
Barbie images caused greater body dissatisfaction than Emme and neutral images
• overall effect of Grade body dissatisfaction increased as grade in school
increased
• combined effect of both IVs together results indicated interesting effects for
combinations of grade and exposure to the images
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Complex Designs, continued
Guidelines for identifying complex designs• at least two IVs• IVs can be independent groups designs
random groups, natural groups, matched groups
• IVs can be repeated measures designs when independent groups and repeated measures
designs are combined, it’s called a mixed design
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Complex Designs, continued
Main effects• overall effect of an IV in a complex design
effect on DV as if only that IV was studied
Interaction effects• combined effect of IVs considered
simultaneously• An interaction effect occurs when the effect of
an independent variable differs depending on the level of a 2nd independent variable.
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Research Example
Kassin, Goldstein, and Savitsky (2003)• pp. 275–283 in text• Research questions
Do interrogators’ expectations about a suspect’s guilt or innocence influence the interrogation tactics they use?
Do interrogators have a confirmation bias in which their initial beliefs about a suspect’s guilt cause them to interrogate more aggressively?
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Research Example, continued
Research design• complex design with 2 levels (a 2 x 2 design)
Interrogator Expectation (random groups)• guilty expectation• innocent expectation
Suspect Status (random groups)• actual guilt• actual innocence
• students participated as interrogators or suspects in a laboratory “mock crime”
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Research Example, continued
• Dependent Variables They measured many, we will focus on:
Number of guilt-presumptive questions the interrogator selects for the interview with suspect
Number of persuasive interrogation techniques used during the interview with the suspect
Ratings of the amount of effort the interrogator used to obtain a confession
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Research Example, continued
• factorial combination of 2 x 2 design→ 4 conditions
Interrogators were led to believe the suspect was innocent and the suspect did not commit the crime
Interrogators were led to believe the suspect was guilty and the suspect did not commit the crime
Actual Innocence
Interrogators were led to believe the suspect was innocent and the suspect actually committed the crime
Interrogators were led to believe the suspect was guilty and the suspect actually committed the crime
Actual GuiltSuspect
Status
InnocentGuilty
Interrogator Expectation
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Research Example, continued
• Hypothesis
Based on behavioral confirmation theory, interrogators were predicted to behave toward the suspect in ways that were consistent with their belief of guilt or innocence. In turn, suspects were predicted to respond in ways that support the interrogator’s belief.
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Research Example, continued
Kassin et al.’s (2003) findings• Main effects
A main effect is the effect of one IV, ignoring (or collapsing across) the effect of the other IV
• Two main effects are possible for each DV Interrogator Expectation Suspect Status
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Research Example, continued
• Main effect of Interrogator Expectation compare Guilty Expectation and Innocent Expectation DV: number of guilt-presumptive questions
2.663.70Actual Innocence
Suspect
Status
2.603.62
2.543.54Actual Guilt
Means for Interrogator Expectation
InnocentGuilty
Interrogator Expectation
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Research Example, continued
• Means for Interrogator Expectation Guilty: M = 3.62 ← (3.54 + 3.70) ÷ 2 Innocent: M = 2.60 ← (2.54 + 2.66) ÷ 2
• A test of statistical significance revealed that these two means are statistically different. Interrogators who suspected their suspect to be
guilty chose more guilt-presumptive questions (M = 3.62) than interrogators who expected an innocent suspect (M = 2.60)
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Research Example, continued
• Main effect of Suspect Status compare Actual Guilt and Actual Innocence DV: Number of Persuasive Techniques
Means for
Suspect
Status
7.15
11.4210.8811.96Actual Innocence
Suspect
Status6.597.71Actual Guilt
InnocentGuilty
Interrogator Expectation
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Research Example, continued
• Means for Suspect Status Actual Guilt: M = 7.15 ← (7.71 + 6.59) ÷ 2 Actual Innocence: M = 11.41 ← (11.96 + 10.88) ÷ 2
• A test of statistical significance revealed that these two means are statistically different Interrogators who interviewed a suspect who was
actually innocent used more persuasive techniques (M = 11.42) than interrogators who interviewed a suspect who was actually guilty (M = 7.15)
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Research Example, continued
Interaction effects• occurs when the effect of one independent
variable differs depending on the level of a second independent variable
• Kassin et al.’s (2003) experiment look at the effect of suspect status (actual guilt,
innocence) at each level of interrogator expectation variable
• An initial approach to examine interaction effects is the subtraction method
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Research Example, continued
• Interaction effect of Interrogator Expectation X Suspect Status DV: Ratings for effort to obtain a confession
5.857.17Actual Innocence
Suspect Status
–0.29–1.53Difference Between Means
5.565.64Actual Guilt
InnocentGuilty
Interrogator Expectation
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Research Example, continued
• Because the outcome of the subtraction method yielded very different values
–1.53and – 0.29
an interaction effect between the IVs is likely.
• A test of statistical significance would be needed to confirm this.
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Research Example, continued
Examine the means to understand the interaction effect• When suspects were actually guilty, the effort to
obtain a confession was not affected by whether the interrogator expected the suspect to be guilty (M = 5.64) or innocent (M = 5.56) Not a statistically significant difference
• However, when the suspects were actually innocent, the effort to obtain a confession was greater when the interrogator expected a guilty suspect (M = 7.17) compared to when the interrogator expected an innocent suspect (M = 5.85) A statistically significant difference
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Research Example, continued
• Interrogators differed in their effort to obtain a confession depending on their expectations and whether the suspect was actually guilty or innocent
= an interaction effect between Suspect Status and Interrogator Expectation independent variables
• The effect of one IV differed depending on the level of the 2nd IV this is the definition of an interaction effect
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Research Example, continued
Graphs (“Figures”) can be used to detect interaction effects easily• An interaction effect is likely when lines in the
graph that display the means are not parallel that is, the lines either intersect, converge, or
diverge
• However, a statistical test is always used to determine whether an interaction is statistically significant
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Research Example, continuedDV: Ratings of effort to obtain a confession (means)
Interrogator Expectation Guilty Innocent
0
2
4
6
8
10Actual Guilt
Actual Innocence
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Research Example, continued
ANOVA summary table• Statistical significance
p < .05
• Information in summary table is only useful in conjunction with descriptive statistics (e.g., means) for each condition of the experiment.
• Example DV: effort to obtain a confession Statistically significant effects:
• Interaction effect of Interrogator Expectation X Suspect Status• main effect of Interrogator Expectation• main effect of Suspect Status
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Research Example, continued
ANOVA Summary Table for DV: Effort to obtain a confession Source df SS MS F p
eta2Interrogator Expectation 1 34.82 34.82 4.96 .027
.017
Suspect Status 1 58.48 58.48 8.33 .004.029
Interrogator Expect. X Suspect Status 1 27.59 27.59 3.93 .048 .029
Error 294 2063.9 7.02_______________________________________Total 297 2184.77
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Analysis of Complex Designs
Steps for Data Analysis• Check the data for errors and outliers• Summarize the results using descriptive
statistics Factorial design tells you who many means you
need to analyze• e.g., a 2 x 2 → 4 conditions (4 means)
Graph the means
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Analysis of Complex Designs, continued
• Confirm what the data reveal. The means in an experiment will not all be the
same There will be some variability Key question: Is the variability greater than chance
(error variation)? Variability greater than chance is attributed to the
effect of the independent variable(s) Null hypothesis testing is used to decide whether
the IVs produced an effect on the DVs In complex designs, Analysis of Variance is used
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Analysis of Complex Designs, continued
Analysis of Variance (ANOVA)• tells us whether main effects and interaction
effects are statistically significant• when an effect is statistically significant
we say IV caused the effect of DV assuming experiment is internally valid
• results are presented in an ANOVA Summary Table statistics in ANOVA Summary Table are only
useful when descriptive statistics (e.g., mean) are also considered
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Analysis of Complex Designs, continued
How are these effects reported?• Main effect of Interrogator Expectation
On average, interrogators who expected a guilty suspect worked harder to obtain a confession than interrogators who expected an innocent suspect (Ms = 6.40 and 5.71, respectively), F(1, 294) = 4.96, p = .027, d = .26
• Main effect of Suspect StatusOn average, interrogators exerted more effort to obtain a
confession when the suspect was innocent (M = 6.51) than when the suspect was guilty (M = 5.60), F(1, 294) = 8.33, p = .004, d = .34
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Analysis of Complex Designs, continued
• Interrogator x Suspect Status interaction effect:
The interrogator Expectation x Suspect Status interaction was statistically significant, F(1, 294) = 3.93, p = .048, η2 = .029. Interrogators worked hardest to obtain a confession when they expected a guilty suspect and interviewed a suspect who was actually innocent (M = 7.17). The mean rating for this cell was statistically greater than the other three cells, which did not differ significantly from each other.
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Analysis of Complex Designs, continued
• Omnibus ANOVA initial test of main effects and interaction effects
• If interaction effect is statistically significant, conduct follow-up or “post-hoc” tests of statistical
significance, such as simple main effects comparisons of two means
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Analysis of Complex Designs, continued
Guidelines for the analysis of a complex design experiment• Step 1: determine whether interaction effects are
statistically significant in a 2-factor experiment, only 1 interaction effect is possible
• Step 2: If interaction effect is statistically significant, identify source of interaction simple main effects and comparisons of two means
• Then examine whether the main effects of each independent variable are statistically significant
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Analysis of Complex Designs, continued
Interaction effects• Definition
Effect of one independent variable differs depending on the level of a second independent variable
• Analyze the simple main effects to determine source of interaction
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Analysis of Complex Designs, continued
• Simple main effects the effect of one independent variable at one level
of a 2nd IV for example, the effect of the suspect status IV in
the• Expect-Guilty condition or the• Expect-Innocent condition
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Analysis of Complex Designs, continued
• Simple main effect of Suspect Status for the Guilty-Expectation condition
• Statistically significant
Interrogator Expectation Guilty Innocent
0
2
4
6
8
10
Actual Guilt
Actual Innocence
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Analysis of Complex Designs, continued
• Simple main effect of Suspect Status for the Innocent-Expectation condition
• Not statistically significant
Interrogator Expectation Guilty Innocent
0
2
4
6
8
10
Actual GuiltActual Innocence
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Analysis of Complex Designs, continued
Two other simple main effects in Kassin et al.’s experiment• the simple main effect of
Interrogator Expectation for Guilty Suspects
• the simple main effect of Interrogator Expectation for Innocent Suspects
• Which is statistically significant?
Interrogator Expectation Guilty Innocent
0
2
4
6
8
10Actual Guilt
Actual Innocence
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Interaction Effects and Theory Testing
Kassin et al. (2003) showed support for behavioral confirmation theory• “Interrogator expectations [triggered] a range
of behavioral confirmation effects, ultimately biasing perceptions of guilt … leading them to exert the most pressure on innocent suspects” (Kassin et al., 2003, p. 199)
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Interaction Effects and External Validity
Interaction effect is not statistically significant• generalize findings across conditions of
experiment• example
Kassin et al.’s findings for number of persuasive techniques used by interrogators
Interrogator Expectation x Suspect Status interaction not statistically significant
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Interaction Effects and External Validity, continued
• Interrogators used more persuasive tactics when the suspect was actually innocent than when the suspect was actually guilty This was true in the guilty-expectation condition
and
the innocent-expectation condition
• Effect of suspect status generalized across the levels of interrogator expectation
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Interaction Effects and External Validity, continued
• The presence of a statistically significant interaction effect sets limits on the external validity of a finding example: Suspect Status x Interrogator
Expectation interaction for number of persuasive tactics
• Not all interrogators who expected a suspect to be guilty exerted a high degree of effort to obtain a confession
• We can’t generalize findings for effort across guilty and innocent suspects, or across interrogator expectation
the number of persuasive tactics depends on• suspect status and interrogator expectations
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Interaction Effects and Ceiling/Floor Effects
Floor and ceiling effects• Sometimes an interaction effect can be
statistically significant “by mistake” This occurs when the means for one or more
condition(s) reach, on average, near • the highest possible score (ceiling effect)• the lowest possible score (floor effect)
• When floor or ceiling effects occur, an interaction effect is uninterpretable
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Ceiling Effect, example
• interaction effect between Task Difficulty (easy, hard) and Study Hours (10, 15) hours of study had an
effect only in the hard-test condition, not in the easy-test condition
How do we interpret this interaction when we know the highest possible test score is 50?
0
10
20
30
40
50
10 15
Hours of StudyTes
t Sco
re
EasyHard
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Ceiling Effect, example
• If we have enough “room” in our DV to assess the effect of the IV, the interaction effect disappears
• This graph shows two main effects: Study Hours and Test Difficulty 0
10
20
30
40
50
60
70
10 15
Hours of Study
Tes
t Sco
re
EasyHard
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Interaction Effects and Natural Groups Design
With complex designs,• researchers can test causal inferences for
natural groups variables but wait … isn’t it impossible to make causal
inferences for natural groups variables? natural groups variables are correlational so, how does one make causal inferences?
• Test a theory for why the natural groups differ
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Interaction Effects and Natural Groups Design, continued
Steps for making causal inferences about natural groups variables using complex designs• State your theory.
Why do the groups differ? What is the theoretical process?
• Identify a relevant independent variable. This IV should influence the likelihood that the
theorized process will occur
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Interaction Effects and Natural Groups Design, continued
• Look for an interaction effect. The natural groups variable and manipulated IV
should produce a statistically significant interaction effect in the predicted direction
This interaction effect allows a causal inference about why individuals differ