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An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches

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SKU 9780367182441 Categories ,
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This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes.

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social scie...

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Description

Product ID:9780367182441
Product Form:Paperback / softback
Country of Manufacture:GB
Series:Quantitative Methodology Series
Title:An Introduction to Multilevel Modeling Techniques
Subtitle:MLM and SEM Approaches
Authors:Author: Ronald Heck, Scott L. Thomas
Page Count:388
Subjects:Research methods: general, Research methods: general, Social research and statistics, Probability and statistics, Social research & statistics, Probability & statistics
Description:Select Guide Rating
This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes.

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives.

New to this edition:

  • An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals;
  • Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches;
  • Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements;
  • An expanded set of applied examples used throughout the text;
  • Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.

This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.


Imprint Name:Routledge
Publisher Name:Taylor & Francis Ltd
Country of Publication:GB
Publishing Date:2020-04-07