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Often asked: Why is repeated measures Anova more powerful?

More statistical power: Repeated measures designs can be very powerful because they control for factors that cause variability between subjects. Fewer subjects: Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size.

What are the advantages of using a repeated measures design?

The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups.

Why use a repeated measures ANOVA?

The benefits of repeated measures designs are that they reduce the error variance. This is because for these tests the within group variability is restricted to measuring differences between an individual’s responses between time points, not differences between individuals.

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What is the primary advantage of the repeated measures ANOVA compared to the between subjects ANOVA?

What is the primary advantage of the repeated-measures ANOVA, compared to the between-subjects ANOVA? Repeated-measures ANOVA maximizes error. Repeated-measures ANOVA allows us to compare more than three groups of participants. Calculation of error is easier in a repeated-measures design.

Do repeated measures increase power?

For instance, collecting repeated measurements of key variables can provide a more definitive evaluation of within-person change across time. Moreover, collecting repeated measurements can simultaneously increase statistical power for detecting changes while reducing the costs of conducting a study.

Why is within-subjects more powerful?

A within-subjects design is more statistically powerful than a between-subjects design, because individual variation is removed. To achieve the same level of power, a between-subjects design often requires double the number of participants (or more) that a within-subjects design does.

What is the advantage of a repeated measures research study?

 The main advantage of a repeated-measures study is that it uses exactly the same individuals in all treatment conditions. That, there is no risk that the participants in one condition are substantially different from the participants from another.

Is repeated measures ANOVA robust to violations of normality?

These assumptions need to be tested before you can run a repeated measures ANOVA. Fortunately, the repeated measures ANOVA is fairly “robust” to violations of normality. “Robust”, in this case, means that the assumption can be violated (a little) and still provide valid results.

What does a two way repeated measures ANOVA tell you?

The Two-Way Repeated-Measures ANOVA compares the scores in the different conditions across both of the variables, as well as examining the interaction between them.

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Why is the repeated measures ANOVA more powerful than the between groups ANOVA?

More statistical power: Repeated measures designs can be very powerful because they control for factors that cause variability between subjects. Fewer subjects: Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size.

What is the difference between ANOVA and repeated measures ANOVA?

ANOVA is short for ANalysis Of VAriance. All ANOVAs compare one or more mean scores with each other; they are tests for the difference in mean scores. The repeated measures ANOVA compares means across one or more variables that are based on repeated observations.

Which of the following is an advantage of using repeated measure designs over independent group designs?

Identify the advantages of using repeated measures design over independent groups design. (Check all that apply.) There is a decrease in the number of participants required to complete the experiment. Repeated measures design has greater ability to detect an effect of the independent variable.

How does correlation affect power?

Higher correlation within subject gets you more power when the test being done is a differencing, equivalent to a paired t-test. The standard deviation used in calculating effect size is multiplied by √1−ρ.

Is ANOVA Multivariate analysis?

Multivariate analysis of variance (MANOVA) is an extension of the univariate analysis of variance (ANOVA). In an ANOVA, we examine for statistical differences on one continuous dependent variable by an independent grouping variable.

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