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mixed_effects_models

Resources for creating mixed-effcts models as analogues for ANOVA and for truly longitudinal models. DOI

Sections 1-4 were part of the original repository accompanying our peer-reviewed manuscript (Lohse, Kozlowski, & Strube, 2023, Communications in Kinesiology) as supplementary material. These sections have since been revised for clarity and updated code, but the changes are minimal relative to the published version. Sections 5-9 are brand new additions, extending beyond the scope of the original manuscript, and were not part of the peer-reviewed material.

Section 01 - Introduction to Mixed-Effects Models (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_01_introduction.html)

Section 02 - Mixed-Effects Models for Longitudinal Data (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_02_longitudinal.html)

Section 03 - Mixed-Effects Models for Factorial Designs (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_03_factorial.html)

Section 04 - Mixed-Effect Models for Time Series within Factorial Designs (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_04_timeSeriesWithin.html)

Section 05 - Fixed-Effect Coding, Centering, and Post-Hoc Contrasts with emmeans (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_05_posthocTesting.html)

Section 06 - Modeling Autocorrelation and Heteroscedasticity with nlme (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_06_residual_covariance_structures.html)

Section 07 - Generalized Linear Mixed Models (GLMMs) for Non-Normal Data (https://keithlohse.github.io/mixed_effects_models/lohse_MER_section_07_generalized_linear_mixed_models.html)

Section 08 - Crossed Random Effects (Participants and Stimuli/Items)

(Coming Soon)

Section 09 - Model Diagnostics for Mixed-Effects Models

(Coming Soon)

Further Readings and Recommendations

(https://keithlohse.github.io/mixed_effects_models/lohse_MER_chapter_the_last.html)

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Resources for creating mixed-effects models as analogues for ANOVA and for truly longitudinal models.

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