Introduction Multilevel Modeling Techniques by Heck Ronald (59 results)

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Paperback. Condition: Good. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes.

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Paperback. Condition: New. 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.…

Condition: New. 2020. 4th Edition. Paperback. . . . . .

Hardback. Condition: New. By Ronald H. Heck and Scott Loring Thomas, this text treats the analysis of hierarchical data, such as students nested within schools, through two traditions: multilevel regression (MLM) and multilevel structural equation modeling with latent variables (SEM). It covers models for both univariate and multivariate outcomes, sets the two approaches side by side, and considers which suits a given research aim. Longitudinal data are handled with both random-effect and fixed-effect methods, two-level latent variable models are run in Mplus, and a separate chapter deals with data considerations in examining multilevel models.…