On multi-level modeling of data from repeated measures designs: a tutorial

By Hugo Quené, H. van den Bergh. Speech Communication, 2004

672 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ea17809be7ba15846734f271af0edd2fbdabbc0e

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Citations in the corpus

The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? (2010, background): “If differences rather than correlations between each combination of time points are calculated, then the data are said to be spherical if these difference scores all have the same variance (see reference [67] for a discussion).”
The value of job crafting for work engagement, task performance, and career satisfaction: longitudinal and quasi-experimental evidence (2019, background): “…ANOVA are that the variance is correctly distributed to the different analysis levels and it is less affected by violations of sphericity and homoscedasticity meaning that effects are estimated more accurately and Type 1 error rates are lower (Quené & Van Den Bergh, 2004; Snijders & Bosker, 2003).”
Analyzing ‘visual world’ eyetracking data using multilevel logistic regression (2008, result): “However, it is inconsistent with another literature concer ning memories for spoken words which finds speaker-specific facilitation due to perceptual priming (e.g., Church & Schacter, 1994; Goldinger, 1996; Nygaard & Pisoni, 1998 ).”
Causal connectives in discourse processing: How differences in subjectivity are reflected in eye movements (2013, methodology): “This technique has several advantages over traditional ANOVA, such as its ability to handle missing data and the fact that it deals with item and subject variability in one model (See Quene & van den Bergh, 2004 , for more details).”
The role of contrastive intonation contours in the retrieval of contextual alternatives (2010, methodology): “This analysis has been chosen over the more traditional separate subject and items analyses as it is less dependent on normality and sphericity assumptions, can cope well with missing data, and allows for the combined analysis of categorical and continuous predictors (eg, Baayen, 2008 ; Barr, 2008”
Random effects structure for confirmatory hypothesis testing: Keep it maximal (2013, neutral): “Such a Monte Carlo procedure is standard for this type of problem (e.g., Baayen et al., 2008; Davenport & Webster, 1973; Forster & Dickinson, 1976; Quené & van den Bergh, 2004; Santa, Miller, & Shaw, 1979; Schielzeth & Forstmeier, 2009; Wickens & Keppel, 1983), and guarantees that as the number of…”
Multilevel models for the experimental psychologist: Foundations and illustrative examples (2007, methodology): “The multilevel model is relatively common in the educational and developmental literatures, but is less well known in other areas of psychology, with a few exceptions (see Allen, Sliwinski, & Bowie, 2002; Quene & van den Bergh, 2004; Wright, 1998).”