Podcast
Questions and Answers
What is a key characteristic of between-subjects experimental designs?
What is a key characteristic of between-subjects experimental designs?
Which statistical test would be appropriate for analyzing data from a between-subjects design with three conditions?
Which statistical test would be appropriate for analyzing data from a between-subjects design with three conditions?
What is an advantage of using a between-subjects design?
What is an advantage of using a between-subjects design?
What is a common disadvantage of between-subjects designs?
What is a common disadvantage of between-subjects designs?
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How can researchers avoid selection bias in between-subjects designs?
How can researchers avoid selection bias in between-subjects designs?
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What is a specific problem that can arise with special populations in between-subjects designs?
What is a specific problem that can arise with special populations in between-subjects designs?
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Which of the following describes a 'within-subjects' design?
Which of the following describes a 'within-subjects' design?
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Which effect is NOT a concern in a between-subjects design?
Which effect is NOT a concern in a between-subjects design?
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What specific confounding variable might be addressed by holding gender constant in a between-subjects study?
What specific confounding variable might be addressed by holding gender constant in a between-subjects study?
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Which of the following statements is true regarding the use of a between-subjects design?
Which of the following statements is true regarding the use of a between-subjects design?
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What is the main purpose of statistical tests in the context of variance?
What is the main purpose of statistical tests in the context of variance?
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Which of the following methods can minimize individual differences?
Which of the following methods can minimize individual differences?
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What is a consequence of compensatory equalization in experimental design?
What is a consequence of compensatory equalization in experimental design?
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Which threat to internal validity is associated with participants giving up when they learn about differences in treatment?
Which threat to internal validity is associated with participants giving up when they learn about differences in treatment?
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What is an advantage of using within-subjects design?
What is an advantage of using within-subjects design?
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Which statistical test would be appropriate for analyzing data from repeated measures with three or more conditions?
Which statistical test would be appropriate for analyzing data from repeated measures with three or more conditions?
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How does maturation affect the validity of an experiment's results?
How does maturation affect the validity of an experiment's results?
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What does regression to the mean imply in the context of testing participants?
What does regression to the mean imply in the context of testing participants?
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Which issue could arise from uncontrolled time-related factors in an experiment?
Which issue could arise from uncontrolled time-related factors in an experiment?
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What is the main disadvantage of a within-subjects design?
What is the main disadvantage of a within-subjects design?
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Study Notes
Between-subjects Methods
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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."
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Data in Between-subjects:
- Only 1 score per participant.
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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
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Restricted randomisation: Creating equal groups.
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Hold variables constant: Restricting the range of a variable.
- Example: Only male participants.
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Match for a potential confound: Participants are matched based on a potential confound variable.
- Example: Matched based on age or IQ.
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Hold variables constant: Restricting the range of a variable.
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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
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Differential attrition: Differences in attrition rates between groups.
- Example: More participants drop out of one group, leaving samples of unequal size.
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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.
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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.
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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.
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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
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History effects: Differences in results due to events that occur between measurements.
- Example: News reports about a new treatment could affect participants' wellbeing.
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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.
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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.
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Instrumentation: Changes in the measurement tools or procedures between measurements.
- Example: Changing the scoring criteria for a test could influence the results.
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