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Linear Mixed-Effects Regression

Linear Mixed-Effects RegressionNathaniel E. HelwigAssistant Professor of Psychology and StatisticsUniversity of Minnesota (Twin Cities)Updated 04-Jan-2017 Nathaniel E. Helwig (U of Minnesota) Linear Mixed-Effects RegressionUpdated 04-Jan-2017 : Slide 1 CopyrightCopyright 2017 by Nathaniel E. HelwigNathaniel E. Helwig (U of Minnesota) Linear Mixed-Effects RegressionUpdated 04-Jan-2017 : Slide 2 Outline of Notes1) Correlated Data:Overview of problemMotivating ExampleModeling correlated data2) One-Way RM-ANOVA:Model Form & AssumptionsEstimation & InferenceExample: Grocery Prices3) Linear Mixed-Effects Model:Random Intercept ModelRandom Intercepts & SlopesGeneral FrameworkCovariance StructuresEstimation & InferenceExample: TIMSS DataNathaniel E. Helwig (U of Minnesota) Linear Mixed-Effects RegressionUpdated 04-Jan-2017 : Slide 3 Correlated DataCorrelated DataNathaniel E.

To model correlated data, we include random effects in the model. Random effects relate to assumed correlation structure for data Including different combinations of random effects can account for different correlation structures present in the data Goal is to estimate fixed effects parameters (e.g., b^) and random effects variance parameters.

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