When responding to your classmates, provide your perspective on repeated measures designs and discuss other research designs that could have been used to answer the research question.

First peer below.

In a repeated-measures ANOVA, the same group of participants are measured on the dependent variable multiple times (Field, 2017). An example of a research design that would call for a repeated measures ANOVA, is the impact of greenspace on attention in children. In this example, the duration of play time spent in greenspace would be the independent variable and attention to task would be the dependent variable. The varying durations of time would be the independent variable levels; 15 minutes, 30 minutes, 1 hour. Using the same group of participants for each independent variable level would allow researchers to control for individual differences and develop a comprehensive understanding of the impacts that greenspace can have on attention.

Research Question

Does the amount of time spent playing in greenspace impact attention to tasks in school aged children?

Independent Variable

Level 1: 15 minutes of playtime in greenspace

Level 2: 30 minutes of playtime in greenspace

Level 3: 1 hour of playtime in greenspace

Dependent Variable

Attention to task

Between-Subjects Design:

This same research design could be conducted using a between-subjects design. If this were the case, researchers would use different groups of children to test each level of the independent variable. Each group of children would only be measured once under which condition they were assigned to. Between-subject design does not control for individual differences but does reduce the potential for carry over effects (Field, 2017).

Second peer below.

Consider a research design aimed at examining the impact of three different teaching methods on students' academic performance in mathematics over the course of a semester. The research question revolves around determining whether there are statistically significant differences in academic performance when students are exposed to distinct teaching methods. The independent variable is the teaching method, with three levels representing each approach (Traditional Lecture, Interactive Group Activities, and Online Learning Modules). The dependent variable is the students' academic performance, measured by their scores on standardized mathematics tests administered at the beginning, middle, and end of the semester.

In a repeated measures ANOVA, the same group of students is exposed to all three teaching methods sequentially, and their performance is measured at multiple time points. This design allows for an assessment of within-subject changes over time, providing insights into how each teaching method affects academic performance for the same group of students. It considers the potential individual variations and accounts for the inter-subject variability.

Alternatively, a between-subjects design could have been employed. In this scenario, three separate groups of students would be assigned to each teaching method, and their academic performance would be assessed at the end of the semester. While this approach would provide information about the average performance of each group, it wouldn't capture potential changes within individuals over time, potentially missing nuances in the learning process. The repeated measures ANOVA is better suited for this research question as it allows for a more comprehensive examination of how teaching methods impact individual students' academic progress throughout the semester.