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1-4 of 4 results for Author: irini moustaki (sorted by Publication Date, showing all)
  1. Latent Variable and Latent Structure Models

    Edited by George A. Marcoulides, Irini Moustaki

    Series: Quantitative Methodology Series

    This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis,...

    Published July 8th 2013 by Psychology Press

  2. Analysis of Multivariate Social Science Data, Second Edition

    By David J. Bartholomew, Fiona Steele, Jane Galbraith, Irini Moustaki

    Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

    Drawing on the authors’ varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to...

    Published June 3rd 2008 by Chapman and Hall/CRC

  3. Latent Variable and Latent Structure Models

    Edited by George A. Marcoulides, Irini Moustaki

    Series: Quantitative Methodology Series for Business and Management

    This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis,...

    Published February 28th 2002 by Psychology Press

  4. The Analysis and Interpretation of Multivariate Data for Social Scientists

    By J.I. Galbraith, Irini Moustaki, David J. Bartholomew, Fiona Steele

    Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

    Multivariate analysis is an important tool for social researchers, but the subject is broad and can be quite technical for those with limited mathematical and statistical backgrounds. To effectively acquire the tools and techniques they need to interpret multivariate data, social science students...

    Published February 25th 2002 by Chapman and Hall/CRC

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