![]() For example, suppose that Model A predicts mood from stress and age whereas Model B predicts mood from age only. Models are considered nested when one model is a restricted or constrained version of another model. We can broadly classify the type of comparison based on whether Model A and B are nested or non-nested models. To begin with, let’s imagine we are just trying to compare two models: Model A and Model B. Just as there are many different kinds of models we can fit, even with LMMs (e.g., with or without random slopes, etc.), so to there are many different kinds and purposes for different model comparisons. This is a type of model comparison, and what we learned in that example was that the results of the model were not sensitive to the two extreme values. In fact, we already saw one example in the topic on Moderation where we compared the results, by eye, from two models that differed only in whether some extreme values were included or excluded. We will look at examples of the different uses of model comparisons in this topic. Calculate effect sizes for one or multiple predictors.Calculate the significance of multiple predictors.Evaluate / compare how the results for a particular predictor(s) of interest change across two (or more) models.Evaluate which of two (or more) models provides the best fit to the data.Here are some examples, although they are not meant to be exhaustive. Comparing different models can be used in lots of different ways. For many statistical models, including LMMs, it can be informative to compare different models.
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