基本説明
Provides coverage of most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling.
Full Description
Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioral sciences are exposed to the complex multivariate statistical techniques such as correlation and multiple regression, exploratory factor analysis, MANOVA, path analysis and structural equation modeling. This book is designed to provide full coverage of the wide range of multivariate topics in a conceptual, non-mathematical, approach. It is geared toward the needs, level of sophistication, and interest in multivariate methodology of students in applied programs in the social and behavioral sciencesathat need to focus on design and interpretation rather than the intricacies of specific computations.
Contents
Part I. The Basics of Multivariate DesignChapter 1. An Introduction to Multivariate DesignChapter 2. Some Fundamental Research Design ConceptsChapter 3A. Data ScreeningChapter 3B. Data Screening Using IBM SPSSPart II. Comparisons of MeansChapter 4A. Univariate Comparison of MeansChapter 4B. Univariate Comparison of Means Using IBM SPSSChapter 5A. Multivariate Analysis of Variance (MANOVA)Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSSPart III. Predicting the Value of a Single VariableChapter 6A. Bivariate Correlation and Simple Linear RegressionChapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSSChapter 7A. Multiple Regression: Statistical MethodsChapter 7B. Multiple Regression: Statistical Methods Using IBM SPSSChapter 8A. Multiple Regression: Beyond Statistical RegressionChapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSSChapter 9A. Multilevel ModelingChapter 9B. Multilevel Modeling Using IBM SPSSChapter 10A. Binary and Multinomial Logistic Regression and ROC AnalysisChapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSSPart IV. Analysis of StructureChapter 11A. Discriminant Function AnalysisChapter 11B. Discriminant Function Analysis Using IBM SPSSChapter 12A. Principal Components and Exploratory Factor AnalysisChapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSSChapter 13A. Canonical Correlation AnalysisChapter 13B. Canonical Correlation Analysis Using IBM SPSSChapter 14A. Multidimensional ScalingChapter 14B. Multidimensional Scaling Using IBM SPSSChapter 15A. Cluster AnalysisChapter 15B. Cluster Analysis Using IBM SPSSPart V. Fitting Models to DataChapter 16A. Confirmatory Factor AnalysisChapter 16B. Confirmatory Factor Analysis Using AmosChapter 17A. Path Analysis: Multiple RegressionChapter 17B. Path Analysis: Multiple Regression Using IBM SPSSChapter 18A. Path Analysis: Structural ModelingChapter 18B. Path Analysis: Structural Modeling Using AmosChapter 19A. Structural Equation ModelingChapter 19B. Structural Equation Modeling Using AmosChapter 20A. Model Invariance: Applying a Model to Different GroupsChapter 20B. Assessing Model Invariance Using Amos
