Two group Designs
• One independent variable with 2 levels:– IV: color of walls
• Two levels: white walls vs. baby blue– DV: anxiety
White walls Baby blue wallsBaby blue walls
Two group Designs – within subjects
• One independent variable with 2 levels:– IV: color of walls
• Two levels: white walls vs. baby blue– DV: anxiety
– All participants are tested in both white classroom and baby blue classroom
White walls Baby blue wallsBaby blue walls
Two group Designs – between subjects
• One independent variable with 2 levels:– IV: color of walls
• Two levels: white walls vs. baby blue– DV: anxiety
– Some participants are tested in white classroom and another set of participants are tested in baby blue classroom.
White walls Baby blue wallsBaby blue walls
More than two groups
• One independent variable with 3 levels:– IV: color of walls
• 3 levels: white walls vs. baby blue vs. red– DV: anxiety
– Can use within-subjects design or between-subjects design.
White walls Baby blue wallsBaby blue walls Red wallsRed walls
Why conduct studies with more than two groups? Can answer more sophisticated questions with a multiple
group design—more efficient.
Compare more than 2 kinds of treatment in one study. Compare 2 kinds of treatment and a control group. Compare a treatment vs. placebo vs. control group.
To go from a two groups design to a multiple groups design, you add another level to your IV.
Analysis of variance: One-way Randomized ANOVA
One-way Repeated Measures ANOVA
Factorial Designs• Experiments with more than 1 independent variable.
• Factorial designs– More than 1 factor (or IV) is being manipulated in the study.– IV 1: gender (male vs. female)– IV 2: color of walls (white vs. baby blue) – DV: anxiety
• Benefits:– Assess how variables interact with each other. – Increases generalizability of results because we are measuring how
multiple variables affect behavior, at the same time.
Factorial Designs
• 2 X 2 factorial design– The number of digits tells us how many independent
variables are being manipulated. – The value of digits tells us the number of levels of each IV.
• 3 X 3 ?• 2 X 3?• 2 X 2 X 3?• __ X __ X __ ?
2 X 2 factorial design• Clerks (male & female) responded
faster to hearing customers.
• Overall speed for males and females was the same. – Average 2 dots on male line and
2 dots on female line.
• Hearing customer: male clerks responded faster.
• Deaf customer: female clerks responded faster.
• The effects of customer hearing seemed to vary according to sex of salesclerk.
4 min
3
3.7
Factorial designs
• Provides information about each factor separately. – Gender of clerks– Hearing of customers
• Saves time to run 1 factorial design versus 2 separate experiments (i.e., gender; hearing ability).
• Provides information about how the 2 factors interact.
• Main effects: – The effect of each
independent variable separately.
– Main effect for factor A– Main effect for factor B
• Interaction– Joint effect of
independent variables on the DV.
Interaction
• An interaction is present when the effects of one independent variable change as the levels of the other independent variable changes.
• An interaction is present when the effects of one independent variable depend on the level of the other independent variable.
Statistical Analysis
• Two-way ANOVA– 2 independent variables
• ANOVA for each type of design: – Two-way Randomized ANOVA (both variables are
between- subjects)
– Two-way Repeated measures ANOVA (both variables are within-subjects)
– Two-way mixed ANOVA (one variable within-subjects and the other variable between subjects)
Rationale of ANOVA
• F = between-groups variance within-groups variance
• F = systematic variance + error variance error variance
IV has an effect: F > 1; must pass a cutoff for statistical significance.
IV has no effect or small effect: F ≤ 1
Two-way Randomized ANOVA
• 3 F-ratios– F-ratio for factor A– F-ratio for factor B – F-ratio for interaction
• F-ratios obtained by dividing each MS (variance) by Mserror (within-groups variance)
• Table 11.8 11.9, pg 254