Relative Importance of Predictors in Multilevel Modeling
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This article is published in Journal of Modern Applied Statistical Methods.The article was published on 2014-05-01 and is currently open access. It has received 35 citations till now. The article focuses on the topics: Multilevel model & Structural equation modeling.read more
Citations
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Journal ArticleDOI
Social Media and Change in Psychological Distress Over Time: The Role of Social Causation
TL;DR: Findings revealed that home Internet and social network site (SNS) use are associated with decreased PD over time, and having extended family who are also Internet users further decreases PD.
Journal ArticleDOI
Cost of specific emergency general surgery diseases and factors associated with high-cost patients.
Gerald O. Ogola,Shahid Shafi +1 more
TL;DR: A small number of diseases constitute a vast majority of EGS hospitalizations and their cost, and attempts at reducing the cost will require controlling the cost of procedures.
Latent Variable Modeling in Heterogeneous Populations
TL;DR: MIMIC structural modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis, and random effects models connect with emerging methodology for multilevel structural equation modeling of hierarchical data.
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On Johnson's (2000) Relative Weights Method for Assessing Variable Importance: A Reanalysis.
TL;DR: The primary conclusion of the reanalysis is that J. W. Johnson's (2000) relative weights method is theoretically flawed and has no more validity than the discredited method of Green, Carroll, and DeSarbo (1978) on which it is based.
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Hydro-geophysical monitoring of the North Western Sahara Aquifer System's groundwater resources using gravity data
Ahmed Mohamed,Julio Gonçalvès +1 more
TL;DR: In this article, an integrated approach combining Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data was proposed to reconstruct groundwater storage variations between April 2002 and July 2016.
References
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Book
Hierarchical Linear Models: Applications and Data Analysis Methods
TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
Journal ArticleDOI
Hierarchical Linear Models: Applications and Data Analysis Methods.
TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
Book
Applied Longitudinal Data Analysis
Judith D. Singer,John B. Willett +1 more
TL;DR: In this paper, a framework for investigating change over time is presented, where the multilevel model for change is introduced and a framework is presented for investigating event occurrence over time.
Book
Multilevel Analysis: Techniques and Applications
TL;DR: This work focuses on the development of a single model for Multilevel Regression, which has been shown to provide good predictive power in relation to both the number of cases and the severity of the cases.
Book
Introducing multilevel modeling
Ita G. G. Kreft,Jan de Leeuw +1 more
TL;DR: Introduction Overview of Contextual Models Varying and Random Coefficient Models Analyses Frequently Asked Questions
Related Papers (5)
Multilevel analysis of mediation, moderation, and nonlinear effects in small samples, using expected a posteriori estimates of factor scores
Steffen Zitzmann,Christoph Helm +1 more