Computer-Aided Multivariate Analysis, Fourth Edition: (Chapman & Hall/CRC Texts in Statistical Science)

Increasingly, researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory and neglect to explain how to perform and interpret multivariate analyses on real-life data. For years, Afifi and Clark's Computer-Aided Multivariate Analysis has been a welcome exception-helping researchers choose the appropriate analyses for their data, carry them out, and interpret the results. Only a limited knowledge of statistics is assumed, and geometrical and graphical explanations are used to explain what the analyses do. However, the basic model is always given, and assumptions are discussed. Reflecting the increased emphasis on computers, the Third Edition includes three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis. Another new chapter focuses on log-linear analysis of multi-way frequency tables. Students in a wide range of fields-ranging from psychology, sociology, and physical sciences to public health and biomedical science-will find Computer-Aided Multivariate Analysis especially informative and enlightening.

Table of Contents:
PREPARATION FOR ANALYSIS What is Multivariate Analysis? How is Multivariate Analysis Defined? Examples of Studies in Which Multivariate Analysis is Useful Multivariate Analyses Discussed in this Book Organization and Content of this Book Characterizing Data for Future Analyses Variables: Their Definition, Classification, and Use Defining Statistical Variables How Variables are Classified: Stevens's Classification System Examples of Classifying Variables Other Characteristics of Data Preparing for Data Analysis Processing the Data so they Can Be Analyzed Choice of Computer for Statistical Analysis Choice of a Statistical Package Techniques for Data Entry Data Management for Statistics Data Example: Los Angeles Depression Study Data Screening and Data Transformation Making Transformations and Assessing Normality and Independence Common Transformations Assessing the Need for and Selecting a Transformation Assessing Independence Selecting Appropriate Analyses Which Analyses? Why Selection of Analyses is Often Difficult Appropriate Statistical Measures Under Stevens's Classification Appropriate Multivariate Analyses Under Stevens's Classification APPLIED REGRESSION ANALYSIS Simple Linear Regression and Correlation Using Linear Regression and Correlation to Examine the Relationship Between Two Variables When are Regression and Correlation Used? Data Example Description of Methods of Regression: fixed-X Case Description of Methods of Regression and Correlation: variable-X Case Further Examination of Computer Output Robustness and Transformations for Regression Analysis Other Options in Computer Programs Special Applications of Regression Discussion of Computer Programs What to Watch Out For Multiple Regression and Correlation Using Multiple Linear Regression to Examine the Relationship Between One Dependent Variable and Multiple Independent Variables When are Multiple Regression and Correlation Used? Data Example Description of Techniques: Fixed-X Case Description of Techniques: Variable-X Case How to Interpret the Results: Fixed-X Case How to Interpret the Results: Variable-X Case Residual Analysis and Transformations Other Options in Computer Programs Discussion of Computer Programs What to Watch Out For Variable Selection in Regression Analysis Using Variable Selection Techniques in Multiple Regression Analysis When are Variable Selection Methods Used? Data Example Criteria for Variable Selection A General F Test Stepwise Regression Subset Regression Discussion of Computer Programs Discussion and Extensions What to Watch Out For Special Regression Topics Special Topics in Regression Analysis Missing Values in Regression Analysis Dummy Variables Constraints on Parameters Methods for Obtaining a Regression Equation When Multicollinearity is Present Ridge Regression MULTIVARIATE ANALYSIS Canonical Correlation Analysis Using Canonical Correlation Analysis to Analyze Two Sets of Variables When is Canonical Correlation Analysis Used? Data Example Basic Concepts of Canonical Correlation Other Topics Related to Canonical Correlation Discussion of Computer Programs What to Watch Out For Discriminant Analysis Using Discriminant Analysis to Classify Cases When is Discriminant Analysis Used? Data Example Basic Concepts of Classification Theoretical background Interpretation Adjusting the Value of the Dividing Point How Good is the Discriminant Function? Testing for the Contributions of Classification Variables Variable Selection Classification Into More than Two Groups Use of Canonical Correlation in Discriminant Function Analysis Discussion of Computer Programs What to Watch Out For Logistic Regression Using Logistic Regression to Analyze a Dichotomous Outcome Variable When is Logistic Regression Used? Data Example Basic Concepts of Logistic Regression Interpretation: Categorical Variables Interpretation: Continuous and Mixed Variables Refining and Evaluating Logistic Regression Analysis Applications of Logistic Regression Discussion of Computer Programs What to Watch Out For Regression Analysis Using Survival Data Using Survival Analysis to Analyze Time-to-Event Data When is Survival Analysis Used? Data Examples Survival Functions Common Distributions Used in Survival Analysis The Log-Linear Regression Model The Cox Proportional Hazards Regression Model Some Comparisons of the Log-Linear, Cox, and Logistic Regression Models Discussion of Computer Programs What to Watch Out For Principal Components Analysis Using Principal Components Analysis to Understand Intercorrelations When is Principal Components Analysis Used? Data Example Basic Concepts of Principal Components Analysis Interpretation Use of Principal Components Analysis in Regression and Other Applications Discussion of Computer Programs What to Watch Out For Factor Analysis Using Factor Analysis to Examine the Relationship Among P Variables When is Factor Analysis Used? Data Example Basic Concepts of Factor Analysis Initial Factor Extraction: Principal Components Analysis Initial Factor Extraction: Iterated Principal Components Factor Rotations Assigning Factor Scores to Individuals An Application of Factor Analysis to the Depression Data Discussion of Computer Programs What to Watch Out For Cluster Analysis Using Cluster Analysis to Group cases When is Cluster Analysis Used? Data Example Basic Concepts: Initial Analysis and Distance Measures Analytical Clustering Techniques Cluster Analysis for Financial Data Set Discussion of Computer Programs What to Watch Out For Log-Linear Analysis Using Log-Linear Models to Analyze Categorical Data When is Log-Linear Analysis Used Data Example Notation and Sample Considerations tests of Hypotheses and Models for Two-Way Tables Example of a Two-Way Table Models for Multiway Tables Tests of Hypotheses for Multiway Tables: Exploratory Model Building Tests of Hypotheses: Specific Models Sample Size Issues The Logit Model Discussion of Computer Programs What to Watch Out For Appendix A: Lung Function Data Appendix B: Lung Cancer Survival Data