Hutcheson G.D., Sofroniou N. The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models

Philadelphia: SAGE Publications Ltd, 1999. — 276 p. — ISBN: 0-7619-5200-4.Starting from simple hypothesis testing and then moving towards model-building, this valuable book takes readers through the basics of multivariate analysis including: which tests to use on which data; how to run analyses in SPSS for Windows and GLIM4; how to interpret results; and how to report and present the reports appropriately.
Using a unified conceptual framework (based around the Generalized Linear Model) the authors explain the commonalities and relationships between methods that include both the analysis of categorical and continuous data.Generalized Linear Models
Data Formats
Standard Statistical Analyses within the GLM Framework
Goodness-of-fit Measures and Model-Building
The Analysis of Deviance
Assumptions of GLMsData Screening
Levels of Measurement
Data Accuracy
Reliable Correlations
Missing Data
Outliers
Using Residuals to Check for Violations of Assumptions
Assumptions
Transformation and Curvilinear Models
Multicollinearity and Singularity
Diagnostics for Logistic Regression and Loglinear Models
Summary and Recommendations on the Order of Screening Practices
Statistical Software Commands
Ordinary Least-Squares Regression
Simple OLS Regression
A Worked Example of Simple Regression
Multiple OLS Regression
A Worked Example of Multiple Regression.Statistical Software Commands
Logistic Regression
Simple Logistic Regression
A Worked Example of Simple Logistic Regression
Multiple Logistic Regression
A Worked Example of Multiple Logistic RegressionStatistical Software Commands
Loglinear Analysis
Traditional Bivariate Methods
Loglinear ModelsStatistical Software Commands
Factor Analysis
Overview
Factor Analysis Equations
Preliminary Analyses
Factor Extraction
Factor Rotation
Including Factors in GLMs
A Worked Example of Factor AnalysisStatistical Software CommandsMain Points of the GLM Framework
Sampling Assumptions and GLMs
Measurement Assumptions of Explanatory Variables in GLMs
Ordinal Variables
GLM Variants of ANOVA and ANCOVA
Repeated Measurements
Time Series Analysis
Gamma Errors and the Link Function: an Alternative to Data Transformation
Survival Analysis
Exact Sample and Sparse Data Methods
Cross Validation of Models
Software Recommendations