IBM SPSS Categories 19 Jacqueline J. Meulman Willem J. Heiser SPSS Inc. Note: Before using this information and the product it supports, read the general information under Notices on p. 302. This document contains proprietary information of SPSS Inc, an IBM Company. It is provided under a license agreement and is protected by copyright law. The information contained in this publication does not include any product warranties, and any statements provided in this manual should not be interpreted as such. When you send information to IBM or SPSS, you grant IBM and SPSS a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligation to you. © Copyright SPSS Inc. 1989, 2010. 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 SPSS Inc., an IBM Company SPSS Inc., an IBM Company, is a leading global provider of predictive analytic software and solutions. The company’s complete portfolio of products — data collection, statistics, modeling and deployment — captures people’s attitudes and opinions, predicts outcomes of future customer interactions, and then acts on these insights by embedding analytics into business processes. SPSS Inc. solutions address interconnected business objectives across an entire organization by focusing on the convergence of analytics, IT architecture, and business processes. Commercial, government, and academic customers worldwide rely on SPSS Inc. technology as a competitive advantage in attracting, retaining, and growing customers, while reducing fraud and mitigating risk. SPSS Inc. was acquired by IBM in October 2009. 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For more information on these seminars, contact your local of?ce, listed on the Web site at http://www.spss.com/worldwide. © Copyright SPSS Inc. 1989, 2010 iii Additional Publications The SPSS Statistics: Guide to Data Analysis, SPSS Statistics: Statistical Procedures Companion, and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija Norušis and published by Prentice Hall, are available as suggested supplemental material. These publications cover statistical procedures in the SPSS Statistics Base module, Advanced Statistics module and Regression module. Whether you are just getting starting in data analysis or are ready for advanced applications, these books will help you make best use of the capabilities found within the IBM® SPSS® Statistics offering. For additional information including publication contents and sample chapters, please see the author’s website: http://www.norusis.com 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 What Is Optimal Scaling? . 1 Why Use Optimal Scaling? . 1 Optimal Scaling Level and Measurement Level . 2 Selecting the Optimal Scaling Level . Transformation Plots . Category Codes . Which Procedure Is Best for Your Application? . 3 3 4 6 Categorical Regression . Categorical Principal Components Analysis . Nonlinear Canonical Correlation Analysis . Correspondence Analysis . Multiple Correspondence Analysis . Multidimensional Scaling. Multidimensional Unfolding . Aspect Ratio in Optimal Scaling Charts . 7 7 8 9 10 11 11 12 Recommended Readings . 12 2 Categorical Regression (CATREG) 14 Define Scale in Categorical Regression . 15 Categorical Regression Discretization . 16 Categorical Regression Missing Values . 17 Categorical Regression Options . 18 Categorical Regression Regularization . 20 Categorical Regression Output . 21 Categorical Regression Save . 23 Categorical Regression Transformation Plots . 24 CATREG Command Additional Features.