Report Quality of Generalized Linear Mixed Models in Psychology: A Systematic Review

Area of Science:

• Psychology
• Statistics
• Quantitative Research Methods

Background:

• Generalized linear mixed models (GLMMs) are statistical tools for analyzing non-normally distributed data and repeated measures.
• GLMMs accommodate complex data structures, including autocorrelation, making them suitable for various psychological research designs.

Purpose of the Study:

• To investigate the trends and frequency of GLMM application in psychological research from 2014-2018.
• To evaluate the quality of reporting for GLMM analyses in published psychological articles.

Main Methods:

• A trend analysis of 316 empirical articles using GLMMs published between 2014 and 2018.
• A systematic review of 118 GLMM analyses from 80 articles published in 2018 to assess reporting quality.

Main Results:

• The application of GLMMs in psychology has shown a consistent increase over the study period.
• A significant proportion (86.4%) of the reviewed articles were published in high-impact (first- or second-quartile) journals.
• Despite increased usage, critical information regarding GLMM application was frequently omitted in published reports.

Conclusions:

• GLMMs are becoming more prevalent in psychological research, particularly in top-tier journals.
• There is a substantial need to enhance the reporting quality of GLMM analyses to meet methodological standards.
• Adherence to reporting guidelines is crucial for the transparent and reproducible use of GLMMs in psychology.