I explain what “random effects” and “fixed effects” (opposite to random effects) mean in this page however, people say different opinions about them (as Gelman and Hill's book explains). In this wiki, I follow Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill. The term of “random effects” is often confusing because it is used to mean different things. Very roughly speaking, it is a repeated-measure version of linear models or GLMs.Ī multilevel model is often referred as a “hierarchical,” “random-effect” or “mixed-effect” model. Multilevel models can accommodate such differences. 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).Īlthough 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). Linear Models and Generalized Linear Models (GLM) are a very useful tool to understand the effects of the factor you want to examine.
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