02638 a2200397 4500 0429594372 20250317100404.0 250312042020GB 118 eng 9780429594373 Taylor & Francis GBP 54.99 BB 01 eng JMB thema JNM thema GPS thema KJ thema JHB thema JMB bic JNM bic GPS bic KJ bic JHB bic PSY030000 bisac BUS061000 bisac EDU027000 bisac PSY000000 bisac REF018000 bisac 001.422 bisac Ronald Heck Introduction to Multilevel Modeling Techniques MLM and SEM Approaches 4 Oxford Routledge 20200324 404 p 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. Scott L. Thomas A01 1575 1575