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IBM SPSS Categories 21 i IBM SPSS Categories 21 Jacqueline J. Meulman Willem J. Heiser Note: Before using this information and the product it supports, read the general information under Notices on p. 301. This edition applies to IBM® SPSS® Statistics 21 and to all subsequent releases and modifications until otherwise indicated in new editions. Adobe product screenshot(s) reprinted with permission from Adobe Systems Incorporated. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation. Licensed Materials - Property of IBM © Copyright IBM Corporation 1989, 2012. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Preface IBM® SPSS® Statistics is a comprehensive system for analyzing data. The Categories optional add-on module provides the additional analytic techniques described in this manual. The Categories add-on module must be used with the SPSS Statistics Core system and is completely integrated into that system. About IBM Business Analytics IBM Business Analytics software delivers complete, consistent and accurate information that decision-makers trust to improve business performance. A comprehensive portfolio of business intelligence, predictive analytics, financial performance and strategy management,andanalytic applications provides clear, immediate and actionable insights into current performance and the ability to predict future outcomes. Combined with rich industry solutions, proven practices and professional services, organizations of every size can drive the highest productivity, confidently automate decisions and deliver better results. As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predict future events and proactively act upon that insight to drive better business outcomes. Commercial, government and academic customers worldwide rely on IBM SPSS technology as a competitive advantage in attracting, retaining and growing customers, while reducing fraud and mitigating risk. By incorporating IBM SPSS software into their daily operations, organizations become predictive enterprises – able to direct and automate decisions to meet business goals and achieve measurable competitive advantage. For further information or to reach a representative visit http://www.ibm.com/spss. Technical support Technical support is available to maintenance customers. Customers may contact Technical Support for assistance in using IBM Corp. products or for installation help for one of the supported hardware environments. To reach Technical Support, see the IBM Corp. web site at http://www.ibm.com/support. Be prepared to identify yourself, your organization, and your support agreement when requesting assistance. Technical Support for Students If you’re a student using a student, academic or grad pack version of any IBM SPSS software product, please see our special online Solutions for Education (http://www.ibm.com/spss/rd/students/) pages for students. If you’re a student using a university-supplied copy of the IBM SPSS software, please contact the IBM SPSS product coordinator at your university. Customer Service If you have any questions concerning your shipment or account, contact your local office. Please have your serial number ready for identification. © Copyright IBM Corporation 1989, 2012. iii Training Seminars IBM Corp. provides both public and onsite training seminars. All seminars feature hands-on workshops. Seminars will be offered in major cities on a regular basis. For more information on these seminars, go to http://www.ibm.com/software/analytics/spss/training. Acknowledgements The optimal scaling procedures and their implementation in IBM® SPSS® Statistics were developed by the Data Theory Scaling System Group (DTSS), consisting of members of the departments of Education and Psychology of the Faculty of Social and Behavioral Sciences at Leiden University. Willem Heiser, Jacqueline Meulman, Gerda van den Berg, and Patrick Groenen were involved with the original 1990 procedures. Jacqueline Meulman and Peter Neufeglise participated in the development of procedures for categorical regression, correspondence analysis, categorical principal components analysis, and multidimensional scaling. In addition, Anita van der Kooij contributed especially to CATREG, CORRESPONDENCE, and CATPCA. Willem Heiser, Jacques Commandeur, Frank Busing, Gerda van den Berg, and Patrick Groenen participated in the development of the PROXSCAL procedure. Frank Busing, Willem Heiser, Patrick Groenen, and Peter Neufeglise participated in the development of the PREFSCAL procedure. iv Contents Part I: User's Guide 1 Introduction to Optimal Scaling Procedures for Categorical Data 1 WhatIsOptimalScaling?....................................................... 1 WhyUseOptimalScaling?...................................................... 1 OptimalScalingLevelandMeasurementLevel ...................................... 2 SelectingtheOptimalScalingLevel............................................ 3 TransformationPlots....................................................... 3 CategoryCodes .......................................................... 4 WhichProcedureIsBestforYourApplication?...................................... 6 CategoricalRegression..................................................... 7 CategoricalPrincipalComponentsAnalysis ..................................... 7 NonlinearCanonicalCorrelationAnalysis....................................... 8 CorrespondenceAnalysis................................................... 9 MultipleCorrespondenceAnalysis............................................10 MultidimensionalScaling....................................................11 MultidimensionalUnfolding..................................................11 AspectRatioinOptimalScalingCharts ............................................12 RecommendedReadings.......................................................12 2 Categorical Regression (CATREG) 14 DefineScaleinCategoricalRegression............................................15 CategoricalRegressionDiscretization.............................................16 CategoricalRegressionMissingValues............................................17 CategoricalRegressionOptions..................................................18 CategoricalRegressionRegularization.............................................20 CategoricalRegressionOutput...................................................21 CategoricalRegressionSave....................................................23 CategoricalRegressionTransformationPlots........................................24 CATREGCommandAdditionalFeatures.............................................25 © Copyright IBM Corporation 1989, 2012. v 3 Categorical Principal Components Analysis (CATPCA) 26 DefineScaleandWeightinCATPCA...............................................28 CategoricalPrincipalComponentsAnalysisDiscretization..............................30 CategoricalPrincipalComponentsAnalysisMissingValues.............................31 CategoricalPrincipalComponentsAnalysisOptions...................................32 CategoricalPrincipalComponentsAnalysisOutput....................................34 CategoricalPrincipalComponentsAnalysisSave.....................................35 CategoricalPrincipalComponentsAnalysisObjectPlots................................36 CategoricalPrincipalComponentsAnalysisCategoryPlots..............................37 CategoricalPrincipalComponentsAnalysisLoadingPlots..............................38 CATPCACommandAdditionalFeatures.............................................39 4 Nonlinear Canonical Correlation Analysis (OVERALS) 40 DefineRangeandScale........................................................43 DefineRange................................................................43 NonlinearCanonicalCorrelationAnalysisOptions....................................44 OVERALSCommandAdditionalFeatures............................................45 5 Correspondence Analysis 46 DefineRowRangeinCorrespondenceAnalysis......................................47 DefineColumnRangeinCorrespondenceAnalysis....................................48 CorrespondenceAnalysisModel.................................................49 CorrespondenceAnalysisStatistics...............................................51 CorrespondenceAnalysisPlots..................................................52 CORRESPONDENCECommandAdditionalFeatures....................................54 6 Multiple Correspondence Analysis 55 DefineVariableWeightinMultipleCorrespondenceAnalysis............................57 MultipleCorrespondenceAnalysisDiscretization.....................................57 MultipleCorrespondenceAnalysisMissingValues....................................58 MultipleCorrespondenceAnalysisOptions..........................................60 vi MultipleCorrespondenceAnalysisOutput..........................................61 MultipleCorrespondenceAnalysisSave............................................63 MultipleCorrespondenceAnalysisObjectPlots......................................63 MultipleCorrespondenceAnalysisVariablePlots.....................................64 MULTIPLECORRESPONDENCECommandAdditionalFeatures...........................65 7 Multidimensional Scaling (PROXSCAL) 67 ProximitiesinMatricesacrossColumns............................................69 ProximitiesinColumns.........................................................70 ProximitiesinOneColumn......................................................71
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