1. Experiments: Part 1

2. Overview Experimental versus observational research Variables Designs
Between-group
Within-subject
Similarities and differences
Mixed-model

3. Background on Experiments
Study where a researcher systematically manipulates one variable in order to examine its effect(s) on one or more other variables
Two components
Includes two or more conditions
Participants are randomly assigned by the researcher
Random = Equal odds of being in any particular condition
Examples
People with GAD randomly assigned to three treatments so the researchers can examine which one best reduces anxiety
Students assigned to a “mortality salience” or control condition so the research can examine the impact on “war support”

4. Variables Independent Variable Dependent Variable
Manipulated by the researcher
Typically categorical
Also called a “factor” that has “levels”
Factor = Type of anxiety treatment
Level = CBT (or Psychodynamic or Control)
Dependent Variable
Outcome variable that is presumably influenced by (depends on the effects of) the independent variable
Behavior frequencies, mood, attitudes, symptoms
Typically continuous

5. Variables Confounds (extraneous variables, 3rd variables)
Happens when unwanted differences (age, gender, researchers, environments, etc.) across experimental conditions
Plan: Think of potential confounds up front
Control for them methodologically
Measure them to examine whether they have an effect
Control for them statistically

6. Experimental Designs Three main designs Between-group design
Also called a “between-subjects design,” or “randomized controlled trial” (if clinically focused)
Within-subject design
Also called a “repeated-measures design”
Mixed-model design
Combines both of the above

7. Between-group Design IV: 2 or more randomly-assigned groups of people
DV: Usually a continuous variable

8. Within-subject Design
Any time that a study assess participants on the DV on more than one occasion
Example: Participants go through more than one experimental condition

9. Similarities Uses the same type of analyses
p-values obtained from t-tests (if two conditions) or F-tests/ANOVA (if more than two conditions)
Is the result statistically significant, reliable, trustworthy?
Cohen’s d used to compute effect size
Tells the number of standard deviations by which two groups differ (kind of like r but on a scale from -∞ to ∞)
Effect r r2 d
Small
≥ .1
≥ .01
≥ 0.2
Medium
≥ .3
≥ .09
≥ 0.5
Large
≥ .5
≥ .25
≥ 0.8

10. Cohen’s d Calculator http://www.psychmike.com/calculators.php
Usually use the first formula, requires M, SD, and n
Can calculate by hand with a simple formula, but it doesn’t account for differences in sample size across conditions, so less accurate
d = = (Mean difference) / standard deviation
s = average standard deviation across groups

11. +/- sign is arbitrary, so usually just dropped
Calculation Example: Does athletic involvement improve physical health?
M1 = 6.47
M2 = 6.75
s = ( ) / 2 = 1.91
d = (6.47 – 6.75) / 1.91 = / 1.91 = = 0.15
weak effect!
+/- sign is arbitrary, so usually just dropped

12. 2014 article in Lancet (impact factor: 45
2014 article in Lancet (impact factor: 45.2) Take-home from the abstract:

14. Differences
Between-group design required when it is impossible or impractical to put participants through more than one condition
Within-subject design is more powerful
More likely to get significant p-value and bigger effect sizes. Why? It allows each participant to serve as their own control, canceling out a lot of cross-participant variability
Between-group design requires more people
Within-subject design is prone to ordering effects (order of conditions can effect results), such as progressive effects, or carryover effects
Solution: Counterbalancing

15. Mixed-model Design Many different types, but requires
Random assignment of people to different groups
Repeated measurement of dependent variable over time
Benefits of both designs
Example: Pre-post between-group design
Experimental Group:
pretest
Treatment
posttest
Control Group: