Mixed Models in R - An Applied Introduction
- Henrik Singmann, University of Zurich, Zurich, Switzerland
AbstractIn order to increase statistical power and precision, many data sets in cognitive science contain more than one data point from each unit of observation (e.g., participant), often across different experimental conditions. Such repeated-measures pose a problem to most standard statistical procedures such as ordinary least-squares regression, (between-subjects) ANOVA, or generalized linear models (e.g., logistic regression) as these procedures assume that the data points are independent and identically distributed. In case of repeated measures, the independence assumption is expected to be violated. For example, observations coming from the same participant are usually correlated - they are more likely to be similar to each other than two observations coming from two different participants.
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