Practical Multivariate Analysis

This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods, Practical Multivariate Analysis, Fifth Edition shows readers how to perform multivariate statistical analyses and understand the results. For each of the techniques presented in this edition, the authors use the most recent software versions available and discuss the most modern ways of performing the analysis. New to the Fifth Edition Chapter on regression of correlated outcomes resulting from clustered or longitudinal samples Reorganization of the chapter on data analysis preparation to reflect current software packages Use of R statistical software Updated and reorganized references and summary tables Additional end-of-chapter problems and data sets The first part of the book provides examples of studies requiring multivariate analysis techniques; discusses characterizing data for analysis, computer programs, data entry, data management, data clean-up, missing values, and transformations; and presents a rough guide to assist in choosing the appropriate multivariate analysis. The second part examines outliers and diagnostics in simple linear regression and looks at how multiple linear regression is employed in practice and as a foundation for understanding a variety of concepts. The final part deals with the core of multivariate analysis, covering canonical correlation, discriminant, logistic regression, survival, principal components, factor, cluster, and log-linear analyses. While the text focuses on the use of R, S-PLUS, SAS, SPSS, Stata, and STATISTICA, other software packages can also be used since the output of most standard statistical programs is explained. Data sets and code are available for download from the book’s web page and CRC Press Online.

PREPARATION FOR ANALYSISWhat Is Multivariate Analysis?Defining multivariate analysisExamples of multivariate analysesMultivariate analyses discussed in this bookOrganization and content of the book Characterizing Data for AnalysisVariables: their definition, classification, and useDefining statistical variablesStevens’s classification of variablesHow variables are used in data analysisExamples of classifying variablesOther characteristics of data Preparing for Data AnalysisProcessing data so they can be analyzedChoice of a statistical packageTechniques for data entryOrganizing the dataExample: depression study Data Screening and TransformationsTransformations, assessing normality and independenceCommon transformationsSelecting appropriate transformationsAssessing independence Selecting Appropriate AnalysesWhich analyses to perform?Why selection is often difficultAppropriate statistical measuresSelecting appropriate multivariate analyses APPLIED REGRESSSION ANALYSISSimple Regression and CorrelationChapter outlineWhen are regression and correlation used?Data exampleRegression methods: fixed-X caseRegression and correlation: variable-X caseInterpretation: fixed-X caseInterpretation: variable-X case Other available computer outputRobustness and transformations for regressionOther types of regressionSpecial applications of regressionDiscussion of computer programsWhat to watch out for Multiple Regression and CorrelationChapter outlineWhen are regression and correlation used?Data exampleRegression methods: fixed-X caseRegression and correlation: variable-X caseInterpretation: fixed-X caseInterpretation: variable-X caseRegression diagnostics and transformationsOther options in computer programsDiscussion of computer programsWhat to watch out for Variable Selection in RegressionChapter outlineWhen are variable selection methods used?Data exampleCriteria for variable selectionA general F testStepwise regressionSubset regressionDiscussion of computer programsDiscussion of strategiesWhat to watch out for Special Regression TopicsChapter outlineMissing values in regression analysisDummy variables Constraints on parametersRegression analysis with multicollinearityRidge regression MULTIVARIATE ANALYSISCanonical Correlation AnalysisChapter outlineWhen is canonical correlation analysis used?Data exampleBasic concepts of canonical correlationOther topics in canonical correlationDiscussion of computer programWhat to watch out for Discriminant AnalysisChapter outlineWhen is discriminant analysis used?Data exampleBasic concepts of classification Theoretical backgroundInterpretationAdjusting the dividing pointHow good is the discrimination?Testing variable contributions Variable selectionDiscussion of computer programsWhat to watch out for Logistic RegressionChapter outlineWhen is logistic regression used?Data exampleBasic concepts of logistic regressionInterpretation: Categorical variablesInterpretation: Continuous variablesInterpretation: InteractionsRefining and evaluating logistic regressionNominal and ordinal logistic regressionApplications of logistic regressionPoisson regressionDiscussion of computer programsWhat to watch out for Regression Analysis with Survival DataChapter outlineWhen is survival analysis used?Data examplesSurvival functionsCommon survival distributionsComparing survival among groupsThe log-linear regression model The Cox regression modelComparing regression modelsDiscussion of computer programsWhat to watch out for Principal Components AnalysisChapter outlineWhen is principal components analysis used?Data exampleBasic conceptsInterpretationOther usesDiscussion of computer programsWhat to watch out for Factor AnalysisChapter outlineWhen is factor analysis used?Data exampleBasic conceptsInitial extraction: principal componentsInitial extraction: iterated componentsFactor rotationsAssigning factor scoresApplication of factor analysisDiscussion of computer programsWhat to watch out for Cluster AnalysisChapter outlineWhen is cluster analysis used?Data exampleBasic concepts: initial analysisAnalytical clustering techniquesCluster analysis for financial data setDiscussion of computer programsWhat to watch out for Log-Linear AnalysisChapter outlineWhen is log-linear analysis used?Data exampleNotation and sample considerationsTests and models for two-way tablesExample of a two-way tableModels for multiway tablesExploratory model buildingAssessing specific modelsSample size issuesThe logit modelDiscussion of computer programsWhat to watch out for Correlated Outcomes RegressionChapter outlineWhen is correlated outcomes regression used?Data exampleBasic conceptsRegression of clustered dataRegression of longitudinal dataOther analyses of correlated outcomesDiscussion of computer programsWhat to watch out for Appendix References Index A Summary and Problems appear at the end of each chapter.