Linear Mixed-Effects Models and the Analysis of Nonindependent Data: A Unified Framework to Analyze Categorical and Continuous Independent Variables that Vary Within-Subjects and/or Within-Items

By Markus Brauer, John J. Curtin. Psychological methods, 2017

483 citations recorded by the source corpus.

Abstract In this article we address a number of important issues that arise in the analysis of nonindependent data. Such data are common in studies in which predictors vary within “units” (e.g., within-subjects, within-classrooms). Most researchers analyze categorical within-unit predictors with repeated-measures ANOVAs, but continuous within-unit predictors with linear mixed-effects models (LMEMs). We show that both types of predictor variables can be analyzed within the LMEM framework. We discuss designs with multiple sources of nonindependence, for example, studies in which the same subjects rate the same set of items or in which students nested in classrooms provide multiple answers. We provide clear guidelines about the types of random effects that should be included in the analysis of such designs. We also present a number of corrective steps that researchers can take when convergence fails in LMEM models with too many parameters. We end with a brief discussion on the trade-off between power and generalizability in designs with “within-unit” predictors.

Subfields: Computer Networks and Communications, Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/ef1b4c91d92486f5eab29d1577cfed0e80055a91

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Robert A. I. Davies
2. Lotte Meteyard

In Developmental and Educational Psychology

1. Robert A. I. Davies
2. Dominique Muller
3. Lotte Meteyard
4. A. Smeding
5. Andrew F. Hayes
6. E. Wan
7. Adam M. Grant

In Experimental and Cognitive Psychology

1. Robert A. I. Davies
2. Dominique Muller
3. Lotte Meteyard
4. A. Smeding

Citations in the corpus

• The Caucasian and North African French Faces (CaNAFF): A Face Database (2018, methodology): “Concerning attractiveness and approach/avoidance willingness, we carried out two separate mixed model analyses by having prototypicality (mean centred around each participant’s mean; Brauer & Curtin, 2017) as a fixed effect.”
• Best practice guidance for linear mixed-effects models in psychological science (2019, methodology): “Solutions to convergence problems may include the simplification of model structure (Brauer and Curtin, 2018; Matuscheck et al, 2017), using Principal Components Analysis to determine the most meaningful slopes (Bates et al.”