Multilevel Modeling

By Ulrich Frank. Business & Information Systems Engineering, 2014

844 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/537932ef565c8ff5d24c0c92703b3d943f47fe5a

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Citations in the corpus

The Complexity of Public Attitudes Toward Sex Crimes (2017, positive): “{Table 3 about here} In order to examine explanatory power, the R 2 for each level of data in the model was also computed (Luke, 2004).”
Social modeling of eating: a review of when and why social influence affects food intake and choice. (2015, neutral): “Firstly, because food intake is nonindependent between participants, an appropriate statistical method of analysis would be multi-level modeling (Luke, 2004) – although often this is not performed.”
Can Paranoid Thoughts be Reduced by Targeting Negative Emotions and Self-Esteem? An Experimental Investigation of a Brief Compassion-Focused Intervention (2013, methodology): “Post hoc plotting was used to interpret significant interactions (Aiken and West 1991) and the Sobel-test (Krull and MacKinnon 2001) was used to test the final mediation effect.”
Work–Family Policy Trade-Offs for Mothers? Unpacking the Cross-National Variation in Motherhood Earnings Penalties (2016, methodology): “The two methods produce the same fixed-effects estimates (Luke, 2004; Snijders & Bosker, 1999).”
College on Credit: A Multilevel Analysis of Student Loan Default (2013, background): “, public, private nonprofit, or for‐profit) (Luke, 2004).”
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).”
Situational job characteristics and job satisfaction: The moderating role of national culture (2015, background): “Owing to the hierarchical data structure (employees nested in nations) and our hypotheses and assumptions about the influence of national culture, multilevel approaches (Heck et al., 2010; Luke, 2004) are the appropriate analysis technique.”
Overstating and Understating Interaction Results in International Business Research (2017, neutral): “While we do not explicitly address issues related to nonlinearity (e.g. Zelner, 2009), nested or multi-level data (e.g. Hox, 2002; Kreft & de Leeuw,1998; Luke, 2004; Peterson, Arregle, & Martin, 2012), threeway interactions (e.g. Andersson, Cuervo-Zazurra, & Nielson, 2014; Dawson, 2014), or nonmonot”
Eyes as windows to the soul: Gazing behavior is related to personality (2012, methodology): “Model comparisons by means of Log-Likelihood (LR) indicate whether dispositional variables can significantly reduce prediction error (measured in R(2); Luke, 2004; Snijders & Bosker, 1999) over and above condition.”
Isolated and Integrated form-focused instruction: Effects on different types of L2 knowledge (2014, methodology): “The pretest score predictor was grand-mean centered to simplify interpretation, and residuals were examined for statistical assumptions for MLM (Barkaoui, 2010; Luke, 2004, Tabachnick & Fidell, 2007).”
A Multilevel Investigation of Leadership and Turnover Behavior (2015, background): “R(2) is the proportional reduction of prediction error for Level 1 (compared to a fully unconstrained model; Luke, 2004).”
Who likes math where? Gender differences in eighth-graders’ attitudes around the world (2014, background): “error terms), and standard errors would likely be too small (Luke, 2004; Raudenbush & Bryk, 2002).”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, background): “Conversely, MLM books (e.g., Hox et al., 2018; Luke, 2004; Raudenbush & Bryk, 2002) and articles (Singer, 1998) discuss cross-sectional clustered data first and then introduce the concept of growth modeling in later sections.”
Creating College Opportunity: School Counselors and Their Influence on Postsecondary Enrollment (2013, methodology): “After preprocessing my data via CEM, I used a generalized linear model with a random intercept to identify significant student- and school-based predictors of postsecondary enrollment, and to increase the efficiency of my parameter estimates (Luke 2004; Raudenbush and Bryk 2002).”