This paper provides an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects. A worked-out example of how to use recent software for mixed-effects modeling is provided. Simulation studies illustrate the advantages offered by mixed-effects analyses compared to traditional analyses based on quasi-F tests, by-subjects analyses, combined by-subjects and by-items analyses, and random regression. Applications and possibilities across a range of domains of inquiry are discussed
A random effects model is presented to estimate multivariate data of mixed data types. Such data typ...
This chapter describes a class of statistical model that is able to account for most of the cases of...
Use of mixed models is advocated almost ubiquitously when regression analysis is applied in data set...
This paper provides an introduction to mixed-effects models for the analysis of repeated measurement...
Mixed-effect modeling is recommended for data with repeated measures, as often encountered in design...
A common problem in displaying within-subject data is that of how to show confidence intervals that ...
Psychologists, psycholinguists, and other researchers using language stimuli have been struggling fo...
The analysis of continuous hierarchical data such as repeated measures or data from meta-analyses ca...
Although Repeated Measures ANOVA is often used to analyze experimental designs, this method does not...
Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data an...
Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one o...
Experimental designs often are analyzed using a Repeated Measures ANOVA. Yet, this method does not s...
Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but th...
This paper provides motivation for the use of mixed linear models (i.e. fixed and random effects mod...
International audienceWide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Eff...
A random effects model is presented to estimate multivariate data of mixed data types. Such data typ...
This chapter describes a class of statistical model that is able to account for most of the cases of...
Use of mixed models is advocated almost ubiquitously when regression analysis is applied in data set...
This paper provides an introduction to mixed-effects models for the analysis of repeated measurement...
Mixed-effect modeling is recommended for data with repeated measures, as often encountered in design...
A common problem in displaying within-subject data is that of how to show confidence intervals that ...
Psychologists, psycholinguists, and other researchers using language stimuli have been struggling fo...
The analysis of continuous hierarchical data such as repeated measures or data from meta-analyses ca...
Although Repeated Measures ANOVA is often used to analyze experimental designs, this method does not...
Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data an...
Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one o...
Experimental designs often are analyzed using a Repeated Measures ANOVA. Yet, this method does not s...
Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but th...
This paper provides motivation for the use of mixed linear models (i.e. fixed and random effects mod...
International audienceWide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Eff...
A random effects model is presented to estimate multivariate data of mixed data types. Such data typ...
This chapter describes a class of statistical model that is able to account for most of the cases of...
Use of mixed models is advocated almost ubiquitously when regression analysis is applied in data set...