A practical guide to multilevel modeling.

By J. Peugh. Journal of School Psychology, 2010

1,392 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/27c3ffe4bae61bdf2ac64c13eafb5d6b6433014f

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Citations in the corpus

A review of competitive sport motivation research (2016, positive): “Multilevel modelling is another strategy recommended for sport research (Ntoumanis, 2014; Peugh, 2010), in that it allows person-level variables (e.g., intrinsic motivation, injury status) to be examined within higher levels (e.g., team, league, national governing body).”
Examining the Rule of Thumb of Not Using Multilevel Modeling: The “Design Effect Smaller Than Two” Rule (2015, background): “Peugh (2010), in a pedagogical article on how to apply multilevel modeling to educational data, recommended applied researchers to use multilevel modeling when the design effect was larger than two.”
Classroom context, teacher expectations, and cognitive level: Predicting children's math ability judgments (2013, background): “quantities resulting in more accurate variance estimates and less susceptibility to type I errors with smaller sample sizes (Peugh, 2010). Model building procedures, as outlined by Raudenbush and Bryk (2002), were followed throughout the analysis.”
ROLE OF THE EMOTIONS AND CLASSROOM ENVIRONMENT IN WILLINGNESS TO COMMUNICATE (2017, background): “This violation produces Type I error in which a null hypothesis is incorrectly rejected (Peugh, 2010).”
Best-Practice Recommendations for Estimating Cross-Level Interaction Effects Using Multilevel Modeling (2013, methodology): “A log-likelihood value quantifies the probability that the model being estimated produced the sample data (Peugh, 2010).”
A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED (2012, background): “Previous literature has suggested as a local effect size the proportional reduction in variance (PRV) after the variable of interest has been added to a mixed-effects model or hierarchical linear model, compared to an A-only model (Peugh, 2010).”
Challenge versus hindrance job demands and well‐being: A diary study on the moderating role of job resources. (2015, methodology): “We employed a common centring strategy with multilevel models (Enders & Tofighi, 2007; Peugh, 2010; Snijders & Bosker, 1999), namely we centred the level 1 predictor variables – variables that fluctuate on a within-person level – at the respective person mean.”
Authoritative School Climate and Student Academic Engagement, Grades, and Aspirations in Middle and High Schools (2016, background): “, 2) for the purpose of ignoring the application of multilevel analysis (Peugh, 2010).”
Persistence of social signatures in human communication (2012, methodology): “[33] Bryk AS, Raudenbush SW (1992) Hierarchical linear models (Sage, Newbury Park, CA) [34] Peugh JL (2010) A practical guide to multilevel modeling.”
Early Behavioral Self-Regulation, Academic Achievement, and Gender: Longitudinal Findings From France, Germany, and Iceland (2014, background): “These coefficients suggested that children could not be considered as independent cases, and multilevel modeling (MLM) was needed to obtain unbiased standard errors (Peugh, 2010; Raudenbush & Bryk, 2002).”