Repeated measure analyses come in many forms, but the main goal of each of them is to test whether a score changes over time as a result of random fluctuations, or if there is evidence for something more… trendy going on. Simple examples of these tests include the dependent samples t-test or repeated measures analysis of variance (ANOVA); both of these, in their simplest form, test differences in a score over time. While the dependent samples t-test only allows researchers to test two measurements (usually a pretest and a post-test), the repeated measures ANOVA allows researchers to test multiple time points beyond the two that a t-test is restricted to (such as a pre-intervention, during intervention, and post-intervention scores).
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In addition, the repeated measures ANOVA allows researchers to incorporate different effects into their models, such as grouping variables or covariates. A test that includes a grouping variable is often called a one-within one-between ANOVA, which refers to the within effect of time (which could influence everyone within the sample), and the between effect of group (which describes differences between two or more groups). A common setup here is to have some kind of treatment or intervention condition compared to a control group with measurements taken at two times – before and after treatment. This kind of analysis allows researchers to see if scores changed as a result of the treatment, but also compare the changes over time between a group who should have shown a change (the treatment group) and one who should not have changed (the control group). This wrinkle in the design can help account for threats to internal validity, such as maturation and testing effects.
There are a few other analyses that could be considered repeated measure analyses, including risk analysis and time series. Risk analysis is a way to review a set of participants over time, regarding the time point where they show some trait, to determine whether there are any attributes that correspond with the timing of expressing that trait. This kind of analysis is how we showed the association between smoking and cancer. The final analysis, time series, is a way of modeling changes throughout time, and is often useful for forecasting how those changes might continue into the future. For this reason, it is also often called trend analysis. Time series also has applications when trying to determine whether some change or outside factor influenced the way a trend appeared over time. Because this analysis often relies on repeating trends, it is commonly used for weather forecasting or predicting economic trends.