keenan a pituch, james p stevens applied multivariate statistics for the social sciences analyses with SAS and IBMs SPSS routledge (2015)

APPLIED MULTIVARIATE STATISTICS FOR THE SOCIAL SCIENCES Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally Features new to this edition include:  NEW chapter on Logistic Regression (Ch 11) that helps readers understand and use this very flexible and widely used procedure  NEW chapter on Multivariate Multilevel Modeling (Ch 14) that helps readers understand the benefits of this “newer” procedure and how it can be used in conventional and multilevel settings  NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles  NEW coverage of missing data (Ch 1) to help students understand and address problems associated with incomplete data  Completely re-written chapters on Exploratory Factor Analysis (Ch 9), Hierarchical Linear Modeling (Ch 13), and Structural Equation Modeling (Ch 16) with increased focus on understanding models and interpreting results  NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach  Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)  A free online resources site www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises) Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed Keenan Pituch is Associate Professor in the Quantitative Methods Area of the Department of Educational Psychology at the University of Texas at Austin James P Stevens is Professor Emeritus at the University of Cincinnati APPLIED MULTIVARIATE STATISTICS FOR THE SOCIAL SCIENCES Analyses with SAS and IBM‘s SPSS Sixth edition Keenan A Pituch and James P Stevens Sixth edition published 2016 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor€& Francis Group, an informa business © 2016 Taylor€& Francis The right of Keenan A Pituch and James P Stevens to be identified as authors of this work has been asserted by them in accordance with sections€77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Fifth edition published by Routledge 2009 Library of Congress Cataloging-in-Publication Data Pituch, Keenan A â•… Applied multivariate statistics for the social sciences / Keenan A Pituch and James P Stevens –– 6th edition â•…â•…pages cm â•… Previous edition by James P Stevens â•… Includes index ╇1.╇ Multivariate analysis.â•… 2.╇ Social sciences––Statistical methods.â•… I.╇ Stevens, James (James Paul)â•…II.╇ Title â•… QA278.S74 2015 â•… 519.5'350243––dc23 â•… 2015017536 ISBN 13: 978-0-415-83666-1(pbk) ISBN 13: 978-0-415-83665-4(hbk) ISBN 13: 978-1-315-81491-9(ebk) Typeset in Times New Roman by Apex CoVantage, LLC Commissioning Editor: Debra Riegert Textbook Development Manager: Rebecca Pearce Project Manager: Sheri Sipka Production Editor: Alf Symons Cover Design: Nigel Turner Companion Website Manager: Natalya Dyer Copyeditor: Apex CoVantage, LLC Keenan would like to dedicate this: To his Wife: Elizabeth and To his Children: Joseph and Alexis Jim would like to dedicate this: To his Grandsons: Henry and Killian and To his Granddaughter: Fallon This page intentionally left blank CONTENTS Preface xv Introduction 1.1 Introduction 1.2 Type I€Error, Type II Error, and Power 1.3 Multiple Statistical Tests and the Probability of Spurious Results 1.4 Statistical Significance Versus Practical Importance 1.5 Outliers 1.6 Missing Data 1.7 Unit or Participant Nonresponse 1.8 Research Examples for Some Analyses Considered in This Text 1.9 The SAS and SPSS Statistical Packages 1.10 SAS and SPSS Syntax 1.11 SAS and SPSS Syntax and Data Sets on the Internet 1.12 Some Issues Unique to Multivariate Analysis 1.13 Data Collection and Integrity 1.14 Internal and External Validity 1.15 Conflict of Interest 1.16 Summary 1.17 Exercises Matrix Algebra 2.1 Introduction 2.2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar 2.3 Obtaining the Matrix of Variances and Covariances 2.4 Determinant of a Matrix 2.5 Inverse of a Matrix 2.6 SPSS Matrix Procedure 1 10 12 18 31 32 35 35 36 36 37 39 40 40 41 44 44 47 50 52 55 58 viii õổáồđ õổáồđ Contents 2.7 2.8 2.9 SAS IML Procedure Summary Exercises Multiple Regression for Prediction 3.1 Introduction 3.2 Simple Regression 3.3 Multiple Regression for Two Predictors: Matrix Formulation 3.4 Mathematical Maximization Nature of Least Squares Regression 3.5 Breakdown of Sum of Squares and F Test for Multiple Correlation 3.6 Relationship of Simple Correlations to Multiple Correlation 3.7 Multicollinearity 3.8 Model Selection 3.9 Two Computer Examples 3.10 Checking Assumptions for the Regression Model 3.11 Model Validation 3.12 Importance of the Order of the Predictors 3.13 Other Important Issues 3.14 Outliers and Influential Data Points 3.15 Further Discussion of the Two Computer Examples 3.16 Sample Size Determination for a Reliable Prediction Equation 3.17 Other Types of Regression Analysis 3.18 Multivariate Regression 3.19 Summary 3.20 Exercises 60 61 61 65 65 67 69 72 73 75 75 77 82 93 96 101 104 107 116 121 124 124 128 129 Two-Group Multivariate Analysis of Variance 4.1 Introduction 4.2 Four Statistical Reasons for Preferring a Multivariate Analysis 4.3 The Multivariate Test Statistic as a Generalization of the Univariate t Test 4.4 Numerical Calculations for a Two-Group Problem 4.5 Three Post Hoc Procedures 4.6 SAS and SPSS Control Lines for Sample Problem and Selected Output 4.7 Multivariate Significance but No Univariate Significance 4.8 Multivariate Regression Analysis for the Sample Problem 4.9 Power Analysis 4.10 Ways of Improving Power 4.11 A Priori Power Estimation for a Two-Group MANOVA 4.12 Summary 4.13 Exercises 142 142 143 K-Group MANOVA: A Priori and Post Hoc Procedures 5.1 Introduction 175 175 144 146 150 152 156 156 161 163 165 169 170 Contents 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 Multivariate Regression Analysis for a Sample Problem Traditional Multivariate Analysis of Variance Multivariate Analysis of Variance for Sample Data Post Hoc Procedures The Tukey Procedure Planned Comparisons Test Statistics for Planned Comparisons Multivariate Planned Comparisons on SPSS MANOVA Correlated Contrasts Studies Using Multivariate Planned Comparisons Other Multivariate Test Statistics How Many Dependent Variables for a MANOVA? Power Analysis—A Priori Determination of Sample Size Summary Exercises õổáồđ õổáồđ 176 177 179 184 187 193 196 198 204 208 210 211 211 213 214 Assumptions in MANOVA 6.1 Introduction 6.2 ANOVA and MANOVA Assumptions 6.3 Independence Assumption 6.4 What Should Be Done With Correlated Observations? 6.5 Normality Assumption 6.6 Multivariate Normality 6.7 Assessing the Normality Assumption 6.8 Homogeneity of Variance Assumption 6.9 Homogeneity of the Covariance Matrices 6.10 Summary 6.11 Complete Three-Group MANOVA Example 6.12 Example Results Section for One-Way MANOVA 6.13 Analysis Summary Appendix 6.1 Analyzing Correlated Observations Appendix 6.2 Multivariate Test Statistics for Unequal Covariance Matrices 6.14 Exercises 219 219 220 220 222 224 225 226 232 233 240 242 249 250 255 Factorial ANOVA and MANOVA 7.1 Introduction 7.2 Advantages of a Two-Way Design 7.3 Univariate Factorial Analysis 7.4 Factorial Multivariate Analysis of Variance 7.5 Weighting of the Cell Means 7.6 Analysis Procedures for Two-Way MANOVA 7.7 Factorial MANOVA With SeniorWISE Data 7.8 Example Results Section for Factorial MANOVA With SeniorWise Data 7.9 Three-Way MANOVA 265 265 266 268 277 280 280 281 259 262 290 292 ix ... Nonresponse 1.8 Research Examples for Some Analyses Considered in This Text 1.9 The SAS and SPSS Statistical Packages 1.10 SAS and SPSS Syntax 1.11 SAS and SPSS Syntax and Data Sets on the Internet... 5.16 Multivariate Regression Analysis for a Sample Problem Traditional Multivariate Analysis of Variance Multivariate Analysis of Variance for Sample Data Post Hoc Procedures The Tukey Procedure... Contents Appendix 16.1 Abbreviated SAS Output for Final Observed Variable Path Model Appendix 16.2 Abbreviated SAS Output for the Final Latent Variable Path Model for Exercise Behavior 734 736 Appendix