1. Chapter 7:
Single Factor Designs

2. Single-Factor—Two Levels
Factor = Independent Variable
Between-subjects, single factor designs
Independent groups designs
Manipulated independent variable (separate groups)
Random assignment to create equivalent groups
Matched Groups Designs
Manipulated independent variable (separate groups)
Matching to produce equivalent groups
Nonequivalent groups design (ex post facto designs)
Subject variable as an independent variable
Deliberate attempts to select Ss to reduce nonequivalence

3. Single-Factor—Two Levels
Within-subjects, single factor designs
Also called repeated measures designs
Manipulated independent variable
(all Ss participate in all levels of the independent variable)

4. Single-Factor—Two Levels

5. Single-Factor—Two Levels
Analyzing single-factor, two level designs
t test assumptions
Interval or ratio scale data
Data normally distributed
Homogeneity of variance
t test for independent samples, for
Independent groups designs
Nonequivalent groups designs
t test for dependent samples (paired, repeated measures) for
Matched groups designs
Repeated measures designs

6. Single-Factor—More Than Two Levels
Between-subjects, multilevel designs
Advantage #1  ability to discover nonlinear effects
RT study with 2 levels (1 and 3 mg of caffeine)
Adding levels (2 and 4 mg)  possible nonlinear effect

7. Another nonlinear example…

8. Single-Factor—More Than Two Levels
Between-subjects, multilevel designs
Advantage #2  ability to rule out alternative explanations

9. Multilevel Designs
If the balloons popped, the sound wouldn’t be able to carry, since everything would be too far away from the correct floor. A closed window would also prevent the sound from carrying, since most buildings tend to be well insulated. Since the whole operation depends on a steady flow of electricity, a break in the middle of the wire would also cause problems. Of course, the fellow could shout, but the human voice is not loud enough to carry that far. An additional problem is that a string could break on the instrument. Then there could be no accompaniment to the message. It is clear that the best situation would involve less distance. Then there would be fewer potential problems. With face to face contact, the least number of things could go wrong. (Bransford & Johnson, 1972, p. 392)

10. Single-Factor—More Than Two Levels
Left: context sketch
Right: partial context sketch

11. Single-Factor—More Than Two Levels
Between-subjects, multilevel designs
Effects of practice ruled out (1 rep = 2 reps)
Context has to accurately reflect content (“partial context” condition poor)
Context must be there when studying content (“context after” condition poor)

12. Single-Factor—More Than Two Levels
Within-subjects, multilevel designs
Research Example: Debunking the Mozart effect
Multilevel repeated measures
IV  listening experience
Listening to Mozart
Listening to gentle rainstorm
Control – no listening
DV  recall of digits
Results 
No “Mozart” effect
Significant practice effect instead

13. Single-Factor—More Than Two Levels
Presenting the data
Sentence and paragraph form
Table form (e.g., for the balloon study)

14. Single-Factor—More Than Two Levels
Presenting the data
Graph form
Continuous variable – unlimited intermediate values exist
e.g., drug dosage level
Line graph preferred, but bar graph OK
Discrete variable – no intermediate values
e.g., the five levels of the context experiment
Use a bar graph, line graph inappropriate, for example:

15. Single-Factor—More Than Two Levels
Analyzing single-factor, multilevel designs
Multiple t tests inappropriate
Increases chances of Type I error
1-factor Analysis of Variance (ANOVA)
“1-factor” = 1 IV
“2-factor” = 2 IVs (factorial design – Chapter 8)
Once overall significant effect found, then post hoc testing
Comparing each level of IV against each other level

16. Special-Purpose Control Group Designs
Placebo control groups
Placebo – inactive substance
Ss think they are being treated but they are not
Placebo effect
When performance of placebo group = experimental group, Ss expectations explain the effect of treatment
Wait list control groups
To insure equivalent groups in a study of program effectiveness
Wait list group later administered treatment only if shown to be effective (unethical to deny treatment)

17. Special-Purpose Control Group Designs
Research example 15: Placebo + Wait List
IV  exposure to subliminal recordings
Experimental  weight loss recording
Placebo control  dental pain recording (but told was weight loss tape)
Waiting list control  no tape until wait was over
Double blind procedure used
DV  weight loss
Results  equal amounts of weight loss for all three groups

18. Special-Purpose Control Group Designs
Yoked control groups
Each control group subject “yoked” to an experimental group subject.
In experimental designs in which members of an experimental group and a control group are paired, the yoked control group members receive the same stimuli, reinforcements, or punishments as the experimental group members but without the possibility of influencing these effects through their own behavior.
Example: Reward and test performance.

19. Summary
Depending on your empirical question, you may choose which type of design to use:
Between-subjects vs. within-subjects
Single factor, two level vs. Single factor, multi-level
Special-purpose control group designs
Depending on the type of design, you will choose the appropriate statistical test to test your hypothesis
e.g., independent samples t-test, dependent samples t-test, 1-way ANOVA + post-hoc tests
Once you have your results, you share them with others both in writing and in visual form (tables, graphs)