1. Single-Factor Experimental Designs
Passer Chapter 8
Slides Prepared by Alison L. O’Malley

2. What does it mean to have experimental control?
Here, the focus in on experiments with
one independent variable (i.e., single-factor designs).

3. Experimental Control Manipulation of one or more IVs Measured DV(s)
Everything else held constant
Need to rule out all possible influences on the DV other than the IV!

4. Experimentation: Some Review
What are the three criteria that must be met in order to make a causal inference?
What is a confounding variable?

5. Causality and Confounds
What are the three criteria that must be met in order to make a causal inference?
Covariation of X and Y
Temporal order
Absence of plausible alternative explanations
What is a confounding variable?
A factor that covaries with the IV
Cannot tell whether the IV or the confound affects the DV
How can confounding factors be reduced?

6. Confounding Variables
Many environmental factors can be held constant
This is why laboratory settings are so attractive!
Those environmental factors that cannot be held constant (e.g., time of day) can be balanced across different experimental conditions

7. Confounding Variables
Confounds dealing with participant characteristics (e.g., personality, age) are further addressed through experimental design
One major distinction to attend to is whether psychological scientists employ a between-subjects design or a within- subjects design

8. Research Design
Description
Confounds minimized through…
Between-subjects
Different participants in each condition
Random assignment
Within-subjects
Participants encounter all levels of experiment
Counterbalancing

9. Between-Subjects Designs
Age, cognitive ability, personality, mood, ethnicity, eating habits…and so on In theory, randomly assigning participants to levels of the IV (1, 2, or 3) will distribute individual differences throughout the conditions so experimental groups are equivalent in every way except the IV.
Level 1
Participant ID: 1 2 3 4 5
Level 2
Participant ID: 6 7 8 9 10
Level 3
Participant ID: 11 12 13 14 15

10. Within- Subjects Designs
Participants serve as their own controls Counterbalancing ensures that participants experience the IV levels in different orders.
Level 1
Participant ID: 1 2 3 4 5
Level 2
Participant ID: 1 2 3 4 5
Level 3
Participant ID: 1 2 3 4 5

11. Manipulating an IV
REMEMBER: Above all else, hypotheses must be TESTABLE!

12. The Case for Multilevel Designs
A two-group design would miss this nonlinear effect
3 or more levels of the IV

13. Experimental and Control Conditions
More experimental terminology
Participants in an experimental condition are exposed to a “treatment,” whereas participants in a control condition do not receive the treatment. E.g., In goal setting theory research, participants in the experimental condition are given a difficult, specific goal to achieve, whereas participants in the control condition are simply told to do their best.

14. Between-Subjects Designs: Advantages
No carryover effects
Less likely that participants will catch on to the hypothesis
Exposure to multiple levels of the IV may be impossible or ethically and practically difficult

15. Between-Subjects Designs: Disadvantages
Different people in each condition generate more “noise” (i.e., variability), making it more difficult to establish an effect of the IV on the DV
More participants required

16. Between-Subjects Designs: Independent-Groups
Participants randomly assigned to conditions
What are potential issues with random assignment?
Block randomization helps overcome these issues
Run through random order of blocks (rounds of conditions) until desired sample size reached

17. Between-Subjects Designs: Matched-Groups
Identify a relevant characteristic (a matching variable) and randomly assign participants to conditions based on their standing (e.g., high, average, low) on this characteristic
Wise to use possible confounds as matching variables

18. Between-Subjects Designs: Natural-Groups
Rather than manipulate IV, create different groups of participants based on naturally occurring attributes called subject variables (e.g., age, gender, personality)
Subject variables often referred to as quasi-IVs
But is this truly an experimental design?

19. Concept Clarification
What is the difference between random sampling and random assignment?

20. Random Sampling vs. Random Assignment

21. Within-Subjects Designs: Advantages
Also called repeated-measures designs
Need fewer participants
Can collect more data per condition
Required to investigate certain research questions
Don’t have to worry about non- equivalent groups

22. Within-Subjects Designs: Disadvantages
Greater likelihood that participants will catch on to experiment’s purpose
Cannot accommodate all research questions
Order/sequence effects occur when participants are affected by the order in which they encounter conditions

23. Within-Subjects Designs: More on order effects
Progressive effects occur when participants are altered by the sequence of conditions they encounter (e.g., acquiring experience with the task and thus improving performance)
Carryover effects occur when participants’ responses in one condition are affected by the prior condition (e.g., taste of stimulus from Trial 1 lingers during Trial 2)
The cure? COUNTERBALANCING

24. Within-Subjects Designs: Counterbalancing Goals
Every condition of the IV appears equally often in each position
Every condition appears equally often before and after every other condition
Every condition appears with equal frequency before and after every other condition within each pair of positions in the overall sequence

25. Within-Subjects Designs: All Possible Orders
Also known as complete counterbalancing
Establish all possible sequences (n!) of IV conditions, and assign equal number of participants to each sequence
Every possible confounding effect is counterbalanced (hence the name!)
Requires a large number of participants to satisfy all counterbalancing goals

26. Within-Subjects Designs: Latin Square
Matrix design wherein matrix structured so that each condition appears only once in each column and each row

27. Within-Subjects Designs: Latin Square
Accomplishes goals of all-possible- orders design except Goal 3
When IV has odd number of conditions, cannot construct a single matrix that accomplishes Goal 2 if using a Williams Latin Square
Alternative method is random starting order with rotation

28. Within-Subjects Designs: Random-Selected-Orders
Subset of orders is randomly selected from the set of all possible orders
Each order administered to one participant
Refrain from using with small number of participants

29. Within-Subjects Designs: Other Options
Two designs wherein participants encounter each condition more than once
May be more practical than other counterbalancing designs, lets you examine reliability of participants’ responses, and extends generalizability of results

30. Within-Subjects Designs: Block-Randomization-Design
Participants encounter all conditions within a block and are exposed to multiple blocks
Each block contains a newly randomized order of conditions
How does this block randomization design differ from that applied in between- subjects designs?

31. Within-Subjects Designs: Reverse-Counterbalancing
Also called an ABBA-counterbalancing design
Participants first receive random order of all conditions
Participants then receive conditions once more in reverse order
Watch out for nonlinear order effects!

32. Making Sense of Experimental Data
First compute descriptive statistics (e.g., mean for each condition)
Inferential statistics then used to establish whether findings are statistically significant
Are differences between conditions due to chance?
Remember that p < .05 criterion?

33. Making Sense of Experimental Data
t test examines mean difference between two conditions
ANOVA (analysis of variance) used with three or more conditions
Would researchers
use a t test or ANOVA
here?

34. Making Sense of Experimental Data
An ANOVA would be preferable given that there are 3 conditions
If ANOVA reveals a statistically significant overall pattern of mean differences, post-hoc tests can reveal which which means differ