Students:
- Can recognize pre-post and other repeated measures designs (and distinguish within from between designs).
- Can make the decision when to enter a predictor variable as a fixed effect or a random effect.
- Can compute and interpret an intraclass correlation based on statistical output.
- Know how to use syntax to run a linear mixed model.
- Can interpret the output of a simple linear mixed model analysis for any within design, predict values, calculate confidence intervals and do hypothesis testing.
- Can report a simple linear mixed model analysis in APA format.
- Know when to use a nonparametric alternative for a linear mixed model.
- Know what nonparametric alternatives are available for linear mixed models.
- Know when to perform a logistic regression analysis, know how to do it, interpret and report the results APA style.
- Know how to compute probabilities from logoddsratios and vice versa.
- Know how to study and interpret interaction effects between two independent variables.
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This Data Analysis component in module 3 expands the linear model to the generalized linear mixed model. First we introduce linear mixed models and random effects. Departing from analyzing pre-post designs, we extend this to repeated measures analyses in general, including the analysis of mixed designs (the combination of within and between designs). Next, generalized linear models will be introduced, limited to the analysis of dichotomous (0/1) outcome data through logistic regression. Great care is given to choosing the right analysis and reshaping the data matrix appropriately. The interpretation of results and their generalization to populations are also emphasized. Students also practice with APA-style reporting.
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This study unit is part of the module Cognition and Development (202000330). A module is offered as one educational unity and students take it as such.
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