Data Analysis in Management with SPSS Software

Transcription

1 Data Analysis in Management with SPSS Software

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3 J.P. Verma Data Analysis in Management with SPSS Software

4 J.P. Verma Research and Advanced Studies Lakshmibai National University of Physical Education Gwalior, MP, India ISBN ISBN (ebook) DOI / Springer New Delhi Heidelberg New York Dordrecht London Library of Congress Control Number: The IBM SPSS Statistics has been used in solving various applications in different chapters of the book with the permission of the International Business Machines Corporation, # SPSS, Inc., an IBM Company. The various screen images of the software are Reprinted Courtesy of International Business Machines Corporation, # SPSS. SPSS was acquired by IBM in October, IBM, the IBM logo, ibm.com, and SPSS are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at IBM Copyright and trademark information at # Springer India 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (

5 To my elder sister Sandhya Mohan for having me introduced in statistics Brother-in-law Rohit Mohan for his helping gesture And their angel daughter Saumya

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7 Preface While serving as a faculty of statistics for the last 30 years, I have experienced that the non-statistics faculty and research scholars in different disciplines find it difficult to use statistical techniques in their research problems. Even if their theoretical concepts are sound its troublesome for them to use statistical software. This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. The book strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. It enhances readers capability in using data analysis techniques to a broader spectrum of research problems. The book is intended for the undergraduate and postgraduate courses along with pre-doctoral and doctoral course work on data analysis, statistics, and/or quantitative methods taught in management and other allied disciplines like psychology, economics, education, nursing, medical, or other behavioral and social sciences. This book is equally useful to the advanced researchers in the area of humanities and behavioural and social sciences in solving their research problems. The book has been written to provide solutions to the researchers in different disciplines for using one of the powerful statistical software SPSS. The book will serve the students as a self-learning text of using SPSS for applying statistical techniques in their research problems. In most of the research studies, data are analyzed using multivariate statistics which poses an additional problem for the beginners. These techniques cannot be understood without in-depth knowledge of statistical concepts. Further, several fields in science, engineering, and humanities have developed their own nomenclature assigning different names to the same concepts. Thus, one has to gather sufficient knowledge and experience in order to analyze their data efficiently. This book covers most of the statistical techniques including some of the most powerful multivariate techniques along with their detailed analysis and interpretation of the SPSS output that are required by the research scholars in different discipline to achieve their research objectives. vii

8 viii Preface The USP of this book is that even without having the indepth knowledge of statistics, one can learn various statistical techniques and their applications on their own. Each chapter is self-contained and starts with the topics like Introductory concepts, application areas, statistical techniques used in the chapter and step-bystep solved example with SPSS. In each chapter in depth interpretation of SPSS output has been made to help the readers in understanding the application of statistical techniques in different situations. Since the SPSS output generated in different statistical applications are raw and cannot be directly used for reporting hence model way of writing the results has been shown wherever it is required. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, and social and behavioral sciences by using SPSS. Looking at the contents and prospects of learning computing skills using SPSS, this book is a must for every researcher from graduate-level studies onward. Towards the end of each chapter, short answer questions, multiple-choice questions, and assignments have been provided as a practice exercise for the readers. The common mistakes like using two-tailed test for testing one-tailed hypothesis, using the term level of confidence for defining level of significance or using the statement like accepting the null hypothesis instead of not able to reject the null hypothesis have been explained extensively in the text so that the readers may avoid such mistakes during organizing and conducting their research work. The faculty who uses this book will find it very useful as it presents many illustrations with either real or simulated data to discuss analytical techniques in different chapters. Some of the examples cited in the text are from my own and my colleagues research studies. This book consists of 14 chapters. Chapter 1 deals with the data types, data cleaning, and procedure to start SPSS on the system. Notations used throughout the book in using SPSS commands have been explained in this chapter. Chapter 2 deals with descriptive study. Different situations have been discussed under which such studies can be undertaken. The procedure of computing various descriptive statistics has been discussed in this chapter. Besides computing procedure through SPSS, a new approach has been shown towards the end of the second chapter to develop the profile graph which can be used for comparing different domains of the populations. Chapter 3 explains the chi-square and its different applications by means of solved examples. The step-by-step procedure of computing chi-square using SPSS has been discussed. Chi-square is the test of significance for association between the attributes, but it provides comparison of the two groups as well, in case of the responses being measured on the nominal scale. This fact has been discussed for the benefit of the readers. Chapter 4 explains the procedure of computing correlation matrix and partial correlations using SPSS. The emphasis has been given on how to interpret the relationships. In Chapter 5, computing multiple correlations and regression analysis have been discussed. Both the approaches of regression analysis in SPSS i.e. Stepwise and Enter methods have been discussed for estimating any measurable phenomenon.

9 Preface ix In Chapter 6, application of t-test in testing the significance of difference between groups in all the three situations, that is, in one sample, two independent samples, and two dependent samples, has been discussed in detail. Procedures of using one-tailed and two-tailed tests have been thoroughly detailed. Chapter 7 explains the procedure of applying one-way analysis of variance (ANOVA) with equal and unequal groups for testing the significance of variability among group means. The graphical approach has been discussed for post hoc comparisons of means besides using the p-value concept. In Chapter 8, two-way ANOVA for understanding the causes of variation has been discussed in detail by means of solved examples using SPSS. The model way of writing the results has been shown, which the students should note. Procedure for doing interaction analysis has been discussed in detail by using the SPSS output. In Chapter 9, the application of ANCOVA to study the role of covariate in experimental research has been discussed by means of a research example. Students can find the procedure of analyzing their data much easier after going through this chapter. In Chapter 10, cluster analysis technique has been discussed in detail for market segmentation. The readers will come to know about the situations where cluster analysis can be used in their research studies. Discussions of all its basic concepts have been elaborated so that even a non-statistician can also appreciate and use it for their research data. Chapter 11 deals with the factor analysis, one of the most widely used multivariate statistical techniques in management research. By going through this chapter, the readers can understand to study the characteristics of a group of data by means of few underlying structures instead of a large number of parameters. The procedure of developing the test battery using the factor analysis technique has also been discussed in detail. In Chapter 12, we have discussed discriminant analysis and its application in various research situations. By learning this technique, one can develop classificatory model in classifying a customer into any of the two categories based on their relevant profile parameters. The technique is very useful in classifying a customer as good or bad for offering various services in the area of banking and insurance. Chapter 13 explains the application of logistic regression for probabilistic classification of cases into one of the two groups. Basics of this technique have been discussed before explaining the procedure in solving logistic regression with SPSS. Interpretations of each and every output have been very carefully explained for easy understanding of the readers. In Chapter 14, multidimensional scaling has been discussed to find the brand positioning of different products. This technique is especially useful if the popularity of products is to be compared on different parameters. At each and every step, care has been taken so that the readers can learn to apply SPSS and understand minutest possible detail of analysis discussed in this book. The purpose of this book is to give a brief and clear description of how to apply variety of statistical analysis using any version of SPSS. We hope that this book will

10 x Preface provide students and researchers with a self-learning material of using SPSS to analyze their data. Students and other readers are welcome to me their query related to any portion of the book at vermajp@sancharnet.in, to which timely reply will be sent. Professor (Statistics) J.P. Verma

11 Acknowledgements I would like to extend my sincere thanks to my professional colleagues Prof. Y.P. Gupta, Prof. S. Sekhar, Dr. V.B. Singh, Prof. Jagdish Prasad and Dr. J.P. Bhukar for their valuable inputs in completing this text. I must thank to my research scholars who always motivated me to solve varieties of complex research problems which has contributed a lot in preparing this text. Finally I must appreciate the effort of my wife Hari Priya who not only provided me the peaceful environment in preparing this text but also helped me in correcting the manuscript language and format to a great extent. Finally I owe my loving gesture to my children Prachi and Priyam who have provided me the creative inputs in the preparation this manuscript. Professor (Statistics) J.P. Verma xi

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13 Contents 1 Data Management... 1 Introduction Types of Data Metric Data Nonmetric Data... 4 Important Definitions... 5 Variable... 5 Attribute Mutually Exclusive Attributes Independent Variable... 6 Dependent Variable Extraneous Variable... 6 The Sources of Research Data... 7 Primary Data Secondary Data Data Cleaning... 9 Detection of Errors Typographical Conventions Used in This Book How to Start SPSS Preparing Data File Defining Variables and Their Properties Under Different Columns.. 13 Defining Variables for the Data in Table Entering the Data Importing Data in SPSS Importing Data from an ASCII File Importing Data File from Excel Format Exercise xiii

14 xiv Contents 2 Descriptive Analysis Introduction Measures of Central Tendency Mean Median Mode Summary of When to Use the Mean, Median, and Mode Measures of Variability The Range The Interquartile Range The Standard Deviation Variance The Index of Qualitative Variation Standard Error Coefficient of Variation (CV) Moments Skewness Kurtosis Percentiles Percentile Rank Situation for Using Descriptive Study Solved Example of Descriptive Statistics using SPSS Computation of Descriptive Statistics Using SPSS Interpretation of the Outputs Developing Profile Chart Summary of the SPSS Commands Exercise Chi-Square Test and Its Application Introduction Advantages of Using Crosstabs Statistics Used in Cross Tabulations Chi-Square Statistic Chi-Square Test Application of Chi-Square Test Contingency Coefficient Lambda Coefficient Phi Coefficient Gamma Cramer s V Kendall Tau Situation for Using Chi-Square Solved Examples of Chi-square for Testing an Equal Occurrence Hypothesis

15 Contents xv Computation of Chi-Square Using SPSS Interpretation of the Outputs Solved Examples of Chi-square for Testing the Significance of Association Between Two Attributes Computation of Chi-Square for Two Variables Using SPSS Interpretation of the Outputs Summary of the SPSS Commands Exercise Correlation Matrix and Partial Correlation: Explaining Relationships Introduction Details of Correlation Matrix and Partial Correlation Product Moment Correlation Coefficient Partial Correlation Situation for Using Correlation Matrix and Partial Correlation Research Hypotheses to Be Tested Statistical Test Solved Example of Correlation Matrix and Partial Correlations by SPSS 117 Computation of Correlation Matrix Using SPSS Interpretation of the Outputs Computation of Partial Correlations Using SPSS Interpretation of Partial Correlation Summary of the SPSS Commands Exercise Regression Analysis and Multiple Correlations: For Estimating a Measurable Phenomenon Introduction Terminologies Used in Regression Analysis Multiple Correlation Coefficient of Determination The Regression Equation Multiple Regression Application of Regression Analysis Solved Example of Multiple Regression Analysis Including Multiple Correlation Computation of Regression Coefficients, Multiple Correlation, and Other Related Output in the Regression Analysis Interpretation of the Outputs Summary of the SPSS Commands For Regression Analysis Exercise

16 xvi Contents 6 Hypothesis Testing for Decision-Making Introduction Hypothesis Construction Null Hypothesis Alternative Hypothesis Test Statistic Rejection Region Steps in Hypothesis Testing Type I and Type II Errors One-Tailed and Two-Tailed Tests Criteria for Using One-Tailed and Two-Tailed Tests Strategy in Testing One-Tailed and Two-Tailed Tests What Is p Value? Degrees of Freedom One-Sample t-test Application of One-Sample Test Two-Sample t-test for Unrelated Groups Assumptions in Using Two-Sample t-test Application of Two-Sampled t-test Assumptions in Using Paired t-test Testing Protocol in Using Paired t-test Solved Example of Testing Single Group Mean Computation of t-statistic and Related Outputs Interpretation of the Outputs Solved Example of Two-Sample t-test for Unrelated Groups with SPSS 201 Computation of Two-Sample t-test for Unrelated Groups Interpretation of the Outputs Solved Example of Paired t-test with SPSS Computation of Paired t-test for Related Groups Interpretation of the Outputs Summary of SPSS Commands for t-tests Exercise One-Way ANOVA: Comparing Means of More than Two Samples Introduction Principles of ANOVA Experiment One-Way ANOVA Factorial ANOVA Repeated Measure ANOVA Multivariate ANOVA One-Way ANOVA Model and Hypotheses Testing Assumptions in Using One-Way ANOVA Effect of Using Several t-tests Instead of ANOVA

17 Contents xvii Application of One-Way ANOVA Solved Example of One-Way ANOVA with Equal Sample Size Using SPSS Computations in One-Way ANOVA with Equal Sample Size Interpretations of the Outputs Solved Example of One-Way ANOVA with Unequal Sample Computations in One-Way ANOVA with Unequal Sample Size Interpretation of the Outputs Summary of the SPSS Commands for One-Way ANOVA (Example 7.2) Exercise Two-Way Analysis of Variance: Examining Influence of Two Factors on Criterion Variable Introduction Principles of ANOVA Experiment Classification of ANOVA Factorial Analysis of Variance Repeated Measure Analysis of Variance Multivariate Analysis of Variance (MANOVA) Advantages of Two-Way ANOVA over One-Way ANOVA Important Terminologies Used in Two-Way ANOVA Factors Treatment Groups Main Effect Interaction Effect Within-Group Variation Two-Way ANOVA Model and Hypotheses Testing Assumptions in Two-Way Analysis of Variance Situation Where Two-Way ANOVA Can Be Used Solved Example of Two-Way ANOVA Using SPSS Computation in Two-Way ANOVA Using SPSS Model Way of Writing the Results of Two-Way ANOVA and Its Interpretations Summary of the SPSS Commands for Two-Way ANOVA Exercise Analysis of Covariance: Increasing Precision in Comparison by Controlling Covariate Introduction Introductory Concepts of ANCOVA Graphical Explanation of Analysis of Covariance Analysis of Covariance Model

18 xviii Contents What We Do in Analysis of Covariance? When to Use ANCOVA Assumptions in ANCOVA Efficiency in Using ANCOVA over ANOVA Solved Example of ANCOVA Using SPSS Computations in ANCOVA Using SPSS Model Way of Writing the Results of ANCOVA and Their Interpretations Summary of the SPSS Commands Exercise Cluster Analysis: For Segmenting the Population Introduction What Is Cluster Analysis? Terminologies Used in Cluster Analysis Distance Measure Clustering Procedure Standardizing the Variables Icicle Plots The Dendrogram The Proximity Matrix What We Do in Cluster Analysis Assumptions in Cluster Analysis Research Situations for Cluster Analysis Application Steps in Cluster Analysis Solved Example of Cluster Analysis Using SPSS Stage Stage Stage 1: SPSS Commands for Hierarchal Cluster Analysis Stage 2: SPSS Commands for K-Means Cluster Analysis Interpretations of Findings Exercise Application of Factor Analysis: To Study the Factor Structure Among Variables Introduction What Is Factor Analysis? Terminologies Used in Factor Analysis Principal Component Analysis Factor Loading Communality Eigenvalues Kaiser Criteria

19 Contents xix The Scree Plot Varimax Rotation What Do We Do in Factor Analysis? Assumptions in Factor Analysis Characteristics of Factor Analysis Limitations of Factor Analysis Research Situations for Factor Analysis Solved Example of Factor Analysis Using SPSS SPSS Commands for the Factor Analysis Interpretation of Various Outputs Generated in Factor Analysis Summary of the SPSS Commands for Factor Analysis Exercise Application of Discriminant Analysis: For Developing a Classification Model Introduction What Is Discriminant Analysis? Terminologies Used in Discriminant Analysis Variables in the Analysis Discriminant Function Classification Matrix Stepwise Method of Discriminant Analysis Power of Discriminating Variables Box s M Test Eigenvalues The Canonical Correlation Wilks Lambda What We Do in Discriminant Analysis Assumptions in Using Discriminant Analysis Research Situations for Discriminant Analysis Solved Example of Discriminant Analysis Using SPSS SPSS Commands for Discriminant Analysis Interpretation of Various Outputs Generated in Discriminant Analysis 403 Summary of the SPSS Commands for Discriminant Analysis Exercise Logistic Regression: Developing a Model for Risk Analysis Introduction What Is Logistic Regression? Important Terminologies in Logistic Regression Outcome Variable Natural Logarithms and the Exponent Function Odds Ratio Maximum Likelihood

20 xx Contents Logit Logistic Function Logistic Regression Equation Judging the Efficiency of the Logistic Model Understanding Logistic Regression Graphical Explanation of Logistic Model Logistic Model with Mathematical Equation Interpreting the Logistic Function Assumptions in Logistic Regression Important Features of Logistic Regression Research Situations for Logistic Regression Steps in Logistic Regression Solved Example of Logistics Analysis Using SPSS First Step Second Step SPSS Commands for the Logistic Regression Interpretation of Various Outputs Generated in Logistic Regression Explanation of Odds Ratios Conclusion Summary of the SPSS Commands for Logistic Regression Exercise Multidimensional Scaling for Product Positioning Introduction What Is Multidimensional Scaling Terminologies Used in Multidimensional Scaling Objects and Subjects Distances Similarity vs. Dissimilarity Matrices Stress Perceptual Mapping Dimensions What We Do in Multidimensional Scaling? Procedure of Dissimilarity-Based Approach of Multidimensional Scaling Procedure of Attribute-Based Approach of Multidimensional Scaling 447 Assumptions in Multidimensional Scaling Limitations of Multidimensional Scaling Solved Example of Multidimensional Scaling (Dissimilarity-Based Approach of Multidimensional Scaling) Using SPSS

21 Contents xxi SPSS Commands for Multidimensional Scaling Interpretation of Various Outputs Generated in Multidimensional Scaling Summary of the SPSS Commands for Multidimensional Scaling Exercise Appendix: Tables References and Further Readings Index