week 7 & 8

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Questions and Answers

What is a key characteristic of between-subjects experimental designs?

The same participants are used in all conditions.

It involves repeated measures of the same participants.

It minimizes individual differences among participants.

Each participant contributes one score only. (correct)

Which statistical test would be appropriate for analyzing data from a between-subjects design with three conditions?

Chi-square test

One-way ANOVA (correct)

Independent samples t-test

Paired t-test

What is an advantage of using a between-subjects design?

It requires fewer participants than within-subjects designs.

Results are less influenced by time-related factors. (correct)

No practice effects occur.

It eliminates all potential confounding variables.

What is a common disadvantage of between-subjects designs?

They may be vulnerable to individual differences. Signup and view all the answers

How can researchers avoid selection bias in between-subjects designs?

Use random selection or matching methods. Signup and view all the answers

What is a specific problem that can arise with special populations in between-subjects designs?

Limited generalizability to the population. Signup and view all the answers

Which of the following describes a 'within-subjects' design?

The same participants are tested across multiple conditions. Signup and view all the answers

Which effect is NOT a concern in a between-subjects design?

Order effects Signup and view all the answers

What specific confounding variable might be addressed by holding gender constant in a between-subjects study?

Individual differences Signup and view all the answers

Which of the following statements is true regarding the use of a between-subjects design?

It helps avoid order effects by using different participants. Signup and view all the answers

What is the main purpose of statistical tests in the context of variance?

To evaluate the ratio of between-group variance to within-group variance Signup and view all the answers

Which of the following methods can minimize individual differences?

Using the same location for all participants Signup and view all the answers

What is a consequence of compensatory equalization in experimental design?

Control group demands equal treatment Signup and view all the answers

Which threat to internal validity is associated with participants giving up when they learn about differences in treatment?

Resentful demoralisation Signup and view all the answers

What is an advantage of using within-subjects design?

Reduction of time-related factors Signup and view all the answers

Which statistical test would be appropriate for analyzing data from repeated measures with three or more conditions?

Repeated-measures ANOVA Signup and view all the answers

How does maturation affect the validity of an experiment's results?

It causes changes unrelated to the treatment. Signup and view all the answers

What does regression to the mean imply in the context of testing participants?

Extreme scores will tend toward average on retests. Signup and view all the answers

Which issue could arise from uncontrolled time-related factors in an experiment?

Apparent changes due to age development Signup and view all the answers

What is the main disadvantage of a within-subjects design?

Vulnerability to order effects Signup and view all the answers

Study Notes

Between-subjects Methods

Two Types of Experimental Designs:
- Between-subjects: Different participants in each condition. Also known as "independent measures."
- Within-subjects: Same participants in each condition. Also known as "repeated measures."
Data in Between-subjects:
- Only 1 score per participant.
Statistical Analysis for Between-subjects:
- 2 conditions: Independent samples t-test.
- 3+ conditions: One-way ANOVA

Advantages of Between-subjects Designs

Not influenced by time-related factors (history effects, maturation)
Not influenced by order effects (practice, fatigue)

Disadvantages of Between-subjects Designs

Requires larger number of participants (problem with special populations)
Vulnerable to confounds (threats to internal validity):
- Individual differences: Unique characteristics of each participant
- Environmental variables: Changes in the testing environment, e.g., temperature, noise
Difficult to avoid selection bias (creating equal groups)

Avoiding Selection Bias in Between-subjects Designs

Restricted randomisation: Creating equal groups.
- Hold variables constant: Restricting the range of a variable.
  - Example: Only male participants.
- Match for a potential confound: Participants are matched based on a potential confound variable.
  - Example: Matched based on age or IQ.
Block randomisation: Assign participants to groups randomly, but ensuring that older participants are evenly distributed across groups.
- Example: Participants are randomly assigned to groups based on the flip of a coin. If the oldest participants are assigned to group 1 by the coin flip, the oldest remaining participants are assigned to group 2, and so on.

Reducing Environmental Threats to Internal Validity in Between-subjects Designs

Run participants at the same time of day.
Use the same location for testing.

Other Threats to Internal Validity in Between-subjects Designs

Differential attrition: Differences in attrition rates between groups.
- Example: More participants drop out of one group, leaving samples of unequal size.
Diffusion: The treatment being spread from the experimental group to the control group.
- Example: Participants in the control group learn about the treatment being received by the experimental group and start to use it themselves.
Compensatory equalisation: Participants demand similar treatment in the control group as the experimental group.
- Example: The control group demands the same treatment as the experimental group.
Compensatory rivalry: Participants in the control group try harder to perform well to prove they are as good as the treatment group.
- Example: Control group members might work harder to compensate and show that they can perform just as well as those in the treatment group.
Resentful demoralisation: Control group participants become less productive and motivated when they learn about the treatment received by the experimental group.
- Example: Members of the control group become less productive, leading to a lower difference between groups than would otherwise be expected.

Within-subjects Methods

Also known as: Repeated measures.

Advantages of Within-subjects Designs

Removes or reduces threats from individual differences.
No threat from selection bias (same participants).
Avoids increased variance.
Fewewr participants needed, which increases statistical power.

Disadvantages of Within-subjects Designs

Vulnerable to environmental threats (e.g., time-related factors)
Vulnerable to order effects (e.g., practice, fatigue).

Analysis of Within-subjects Designs

2 conditions: Paired-samples t-test (Wilcoxon for non-parametric data).
3+ conditions: Repeated-measures ANOVA (Friedman’s ANOVA for non-parametric data).

Time Threats in Within-subjects Designs

History effects: Differences in results due to events that occur between measurements.
- Example: News reports about a new treatment could affect participants' wellbeing.
Maturation: Changes in results due to participants getting older or developing over time.
- Example: Participants might become more experienced or mature, which could affect their performance on a task.
Regression to the mean: Extreme scores (high or low) may be less extreme when measured again.
- Example: Participants who score extremely well on a test initially may improve or not perform as well when tested on that same test again. This trend is not necessarily due to the intervention.
Instrumentation: Changes in the measurement tools or procedures between measurements.
- Example: Changing the scoring criteria for a test could influence the results.