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    Data Analysis with Mplus

Data Analysis with Mplus

Christian Geiser

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      Format: Paperback / softback

      A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (www.guilford.com/geiser-materials) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.
      CONTRIBUTORS: Christian Geiser EAN: 9781462502455 COUNTRY: United States PAGES: WEIGHT: 454 g HEIGHT: 234 cm
      PUBLISHED BY: Guilford Publications DATE PUBLISHED: 2013-01-22 CITY: GENRE: PSYCHOLOGY / General, PSYCHOLOGY / Statistics WIDTH: 156 cm SPINE:

      Book Themes:

      Psychological theory, systems, schools and viewpoints, Psychological methodology, Social, group or collective psychology, Psychotherapy

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      Christian Geiser, PhD, is CEO of Quantfish and former Professor of Psychology at Utah State University. His research interests are in psychometrics and structural equation modeling, particularly in longitudinal data analysis and multitrait–multimethod modeling. As part of his methodological work, he has presented new longitudinal structural equation modeling approaches for examining effects of situations and person–situation interactions, as well as models for integrating information from multiple reporters or other methods in longitudinal analyses. He offers Mplus workshops at www.goquantfish.com.

      Format: Paperback / softback

      A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (www.guilford.com/geiser-materials) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.
      CONTRIBUTORS: Christian Geiser EAN: 9781462502455 COUNTRY: United States PAGES: WEIGHT: 454 g HEIGHT: 234 cm
      PUBLISHED BY: Guilford Publications DATE PUBLISHED: 2013-01-22 CITY: GENRE: PSYCHOLOGY / General, PSYCHOLOGY / Statistics WIDTH: 156 cm SPINE:

      Book Themes:

      Psychological theory, systems, schools and viewpoints, Psychological methodology, Social, group or collective psychology, Psychotherapy

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      Be the first to write a review
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      Christian Geiser, PhD, is CEO of Quantfish and former Professor of Psychology at Utah State University. His research interests are in psychometrics and structural equation modeling, particularly in longitudinal data analysis and multitrait–multimethod modeling. As part of his methodological work, he has presented new longitudinal structural equation modeling approaches for examining effects of situations and person–situation interactions, as well as models for integrating information from multiple reporters or other methods in longitudinal analyses. He offers Mplus workshops at www.goquantfish.com.

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