What Mixed models is
Mixed models are an extension of linear regression models that allow for the modeling of both fixed and random effects. Mixed models are used when there is not only interest in estimating fixed effects, but also in estimating random effects.
Steps for Mixed Models:
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Identify the response variable and the fixed and random effects of interest.
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Transform the data, if necessary, to meet the assumptions of the model.
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Fit the model using maximum likelihood estimation.
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Check the assumptions of the model.
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Estimate the effects of interest.
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Interpret the results and draw conclusions from the data.
Examples
1.Analysis of variance (ANOVA) with random effects. 2.Linear regression with fixed and random effects. 3.Mixed-effects logistic regression. 4.Multilevel modeling. 5.Hierarchical linear modeling. 6.Generalized linear mixed models. 7.Growth curve models. 8.Latent class analysis. 9.Latent transition analysis. 10.Multilevel structural equation modeling.