9.1 Fundamentals of repeated measures experiments

Repeated measures experiments involve testing the same participants under different conditions. This design reduces the impact of individual differences and requires fewer participants. However, it can lead to confounds like carryover effects and practice effects.

To address these issues, researchers use counterbalancing techniques and check for sphericity. Understanding these fundamentals helps design more effective experiments and interpret results accurately. Repeated measures are crucial for studying changes over time and within individuals.

Repeated Measures Design Fundamentals

Definition and Characteristics

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• Repeated measures design involves each participant being exposed to all levels of the independent variable (IV) and measured on the dependent variable (DV) under each level
• Within-subjects design refers to the same participants being tested under all conditions of the experiment (levels of IV)
• Longitudinal study is a type of research design that involves repeated observations of the same variables over long periods of time (months or years)
• Time-series design involves measuring the DV at multiple time points before and after the introduction of the IV to assess its impact over time

Advantages and Disadvantages

• Repeated measures designs require fewer participants than between-subjects designs since each participant is exposed to all levels of the IV
• Within-subjects designs reduce the impact of individual differences on the results by having each participant serve as their own control
• Longitudinal studies allow researchers to track changes over time and establish temporal order between variables (causality)
• Time-series designs are useful for evaluating the effectiveness of interventions or treatments in real-world settings (clinical trials)
• Repeated measures designs are susceptible to confounds such as carryover effects, practice effects, and fatigue effects that can threaten internal validity
• Within-subjects designs may not be feasible or ethical for certain research questions that involve irreversible treatments or long-term exposure to conditions
• Longitudinal studies are time-consuming, expensive, and prone to participant attrition over time which can lead to biased results
• Time-series designs often lack a control group which makes it difficult to rule out alternative explanations for observed changes in the DV over time

Potential Confounds in Repeated Measures

Carryover and Order Effects

• Carryover effects occur when the effect of one level of the IV persists and influences performance on subsequent levels of the IV
  • For example, if a participant learns a skill in one condition, that learning may carry over and improve their performance in later conditions
• Order effects refer to the possibility that the order in which the levels of the IV are presented can influence the results
  • For example, participants may perform better on a task if they complete the easiest condition first and the hardest condition last (ascending difficulty) compared to the reverse order (descending difficulty)

Practice and Fatigue Effects

• Practice effects occur when participants' performance improves over time due to familiarity with the task or measurement instruments
  • For example, participants may get better at a memory task across trials simply because they have had more practice with the task
• Fatigue effects occur when participants' performance declines over time due to boredom, tiredness, or decreased motivation
  • For example, participants may put less effort into a task or make more errors as the experiment goes on because they become fatigued or lose interest

Addressing Confounds and Assumptions

Counterbalancing Techniques

• Counterbalancing involves varying the order in which the levels of the IV are presented across participants to control for order effects
  • Complete counterbalancing exposes each participant to all possible orders of the conditions (Latin Square design)
  • Partial counterbalancing involves creating a subset of orders that control for first-order carryover effects (balanced Latin Square design)
• Reverse counterbalancing presents the levels of the IV in opposite orders for half of the participants (A-B-C vs. C-B-A)
• Random counterbalancing assigns participants to orders randomly with the constraint that each order is used an equal number of times

Sphericity and Compound Symmetry

• Sphericity is the assumption that the variances of the differences between all pairs of conditions are equal
  • Violations of sphericity can lead to an increased Type I error rate (rejecting the null hypothesis when it is true)
  • Mauchly's test is used to assess sphericity and corrections (Greenhouse-Geisser, Huynh-Feldt) are applied if violated
• Compound symmetry is a more stringent assumption that requires both equal variances and equal covariances between all pairs of conditions
  • Compound symmetry is a sufficient but not necessary condition for sphericity (if CS is met, sphericity is also met)
  • Multilevel modeling (mixed effects models) can be used to analyze repeated measures data without assuming CS or sphericity