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Reading and Understanding Multivariate Statistics helps researchers, students, and other readers of research to understand the purpose and presentation of multivariate techniques. The editors focus on providing a conceptual understanding of the meaning of the statistics in the context of the research questions and results; they leave the subject of how to perform multivariate analysis to other texts.
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for—and the types of empirical questions that can best be addressed by—each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.
The most commonly used multivariate techniques are examined in detail: multiple regression and correlation; path analysis; principal-components analysis; exploratory and confirmatory factor analysis; multidimensional scaling; analysis of cross-classified data; logistic regression; multivariate analysis of variance (MANOVA); discriminant analysis; meta-analysis.
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for—and the types of empirical questions that can best be addressed by—each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.
The most commonly used multivariate techniques are examined in detail: multiple regression and correlation; path analysis; principal-components analysis; exploratory and confirmatory factor analysis; multidimensional scaling; analysis of cross-classified data; logistic regression; multivariate analysis of variance (MANOVA); discriminant analysis; meta-analysis.
