Linear mixed-effects models for within-participant psychology experiments: an introductory tutorial and free,...

Area of Science:

• Cognitive Neuroscience
• Experimental Psychology
• Statistical Modeling

Background:

• Within-participant designs are common in cognitive neuroscience and experimental psychology.
• Linear mixed-effects models (LMMs) are increasingly adopted for analyzing such data.
• Existing methods may present challenges for researchers new to LMMs.

Purpose of the Study:

• To provide an introductory review of Linear Mixed-Effects Models (LMMs) for within-participant data analysis.
• To introduce a free, user-friendly graphical interface (LMMgui) for applying LMMs.
• To facilitate the adoption of LMMs in cognitive and psychological research.

Main Methods:

• Review of Linear Mixed-Effects Models (LMMs) principles for within-participant designs.
• Description of the LMMgui software, a graphical user interface.
• Utilizes the lme4 package within the R statistical environment.

Main Results:

• LMMs offer a robust framework for analyzing complex within-participant data.
• LMMgui provides an accessible tool for researchers to implement LMMs.
• The combination of LMMs and LMMgui simplifies data analysis in relevant fields.

Conclusions:

• Linear mixed-effects models (LMMs) are a powerful tool for cognitive neuroscience and psychology.
• The LMMgui software enhances accessibility to LMM analysis for researchers.
• This work promotes the use of advanced statistical methods in psychological and neuroscience research.