Efficiency—Repeated measure designs allow many experiments to be completed more quickly, as fewer groups need to be trained to complete an entire experiment. For example, experiments in which each condition takes only a few minutes, whereas the training to complete the tasks take as much, if not more time.

When should repeated measures be used?

Repeated measures ANOVA is used when you have the same measure that participants were rated on at more than two time points. With only two time points a paired t-test will be sufficient, but for more times a repeated measures ANOVA is required.

What are two strengths of a repeated measures design?

Advantages

• No participant variables.
• fewer participants required than when using other designs.

 

What is the advantage of a repeated measures research study?

A benefit of using repeated-measures (using the same participants for both manipulations) is it allows the researcher to exclude the effects of individual differences that could occur if two different people were used instead (Howitt & Cramer, 2011).
 

What is the advantage of repeated measures vs between groups designs?

Summary: In user research, between-groups designs reduce learning effects; repeated-measures designs require fewer participants and minimize the random noise.
 

What are the pros and cons of a repeated measures design?

2. Repeated Measures:

• Pro: As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con: There may be order effects.
• Pro: Fewer people are needed as they take part in all conditions (i.e. saves time).

Are repeated measures reliable?

Reliability can be studied in a generalized way using repeated measurements. Linear mixed models are used to derive generalized test-retest reliability measures. The method allows for repeated measures with a different mean structure due to correction for covariate effects.

Is a repeated measures design more sensitive?

Repeated measure designs are also more powerful (sensitive) than independent sample designs because two scores from each person are compared so each person serves as his or her own control group (we analyze the difference between scores). A special type of repeated measures design is known as the matched pairs design.

When would you use a repeated measures or paired t-test?

The repeated-measures t-test, also known as the paired samples t-test, is used to assess the change in a continuous outcome across time or within-subjects across two observations.

When would you use a repeated measures one way Anova?

Introduction. A one-way repeated measures ANOVA (also known as a within-subjects ANOVA) is used to determine whether three or more group means are different where the participants are the same in each group. For this reason, the groups are sometimes called “related” groups.

When can you not use a repeated measures ANOVA design?

At the same time, this technique cannot be used in analyses when covariates determine your dependent variable in a not homogenous manner; for example, the repeated measures ANOVA does not enable the analysis of time-dependent variables.
 

What are reasons to not use a repeated measures t-test?

Question 4 Which of the following are reasons why we might NOT use a repeated measures t test? Group of answer choices It requires too many subjects. A within subjects design has less power than a between subjects design. Information the subjects pick up early in trials may influence their performance on later trials.

What are the pros and cons of a repeated-measures design?

2. Repeated Measures:

• Pro: As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con: There may be order effects.
• Pro: Fewer people are needed as they take part in all conditions (i.e. saves time).

What is the advantage of running a repeated measures t-test?

The major advantage of choosing a repeated-measures design (and therefore, running a dependent t-test) is that you get to eliminate the individual differences that occur between participants – the concept that no two people are the same – and this increases the power of the test.