A multilevel model is often referred as a “hierarchical,” “random-effect” or “mixed-effect” model. Very roughly speaking, it is a repeated-measure version of linear models or GLMs. Multilevel models can accommodate such differences. Although these models are powerful for analyzing the data gained from HCI experiments, one concern we have is that they do not carefully handle “repeated-measure”-ness ( e.g., individual differences of the participants). ![]() These models are also used for prediction: Predicting the possible outcome if you have new values on your independent variables (and this is why independent variables are also called predictors).
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