An introduction to multilevel modeling techniques
This comprehensive, applied approach to multilevel analysis is distinguished by its wide range of applications relevant to the behavioral, educational, organizational, and social sciences. Univariate and multivariate models are used to understand how to design studies and analyze data. Readers are encouraged to consider what they are investigating, their data, and the strengths and limitations of each technique before selecting their approach. Numerous examples and exercises allow readers to test their understanding of the techniques. Input programs from HLM and Mplus demonstrate how to set up and run the models. A latent variable conceptual framework is emphasized to show the commonality of the approaches and to make each technique more accessible. The first section is devoted to conceptual issues underlying multilevel modeling, while the second section develops several types of multilevel analyses including univariate regression, structural equation, growth curve and latent change, and latent variable mixture modeling. The new edition features: New chapters on multilevel longitudinal and categorical models 80% new exercises and examples website at http://www.psypress.com/multilevel-modeling-techniques/ providing datasets and program setups in HLM, SPSS, Mplus, and LISREL Increased emphasis on how multilevel techniques are used to examine changes in individuals and organizations over time. Ideal for introductory graduate level courses on multilevel and/or latent variable modeling, this book is intended for students and researchers in psychology, business, education, health, and sociology interested in understanding multilevel modeling. Prerequisites include an introduction to data analysis and univariate statistics.
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Investigating Organizational Structures Processes
Development of Multilevel Modeling Techniques
Multilevel Regression Models
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achievement approach Asparouhov Bayesian between-group categorical latent variable chapter chi-square cluster coefficients coeﬂicients covariance matrix data set data structures deﬁned dependent variables examine example factor loadings factor variances ﬁles ﬁrst ﬁxed ﬂexibility formulation grand mean group-level groups growth mixture model growth model growth trajectories hypothesized individual individual-level inﬂuence initial status intercept and slope intraclass correlation latent classes latent factors level-1 level-1 model level-2 units likelihood likelihood function math Mehta & Neale missing data mixture models model ﬁt Mplus multilevel models multivariate Muthén & Muthén ncses nested normally distributed observed indicators observed variables organizational output parameter estimates predictors proposed model random effects random intercept random slope Raudenbush & Bryk relationships represent residual variance sample covariance matrix SCHCLIM schcode signiﬁcant single-level speciﬁc standard errors statistical structural equation modeling suggests Table two-level univariate variance components variation vector within-group