“Using IBM® SPSS® Statistics: An Interactive HandsOn Approach, 2nd Edition (2015)” là một cuốn sách hướng dẫn sử dụng phần mềm SPSS® một cách trực quan và thực tế. Cuốn sách này cung cấp cho người đọc một hướng dẫn toàn diện và dễ tiếp cận để sử dụng SPSS®, giúp họ có được kiến thức từng bước để phân tích dữ liệu của họ một cách hiệu quả. Từ việc nhập dữ liệu đến làm việc với cơ sở dữ liệu hiện có, và làm việc với menu trợ giúp thông qua thực hiện phân tích nhân tố, cuốn sách này bao gồm mọi khía cạnh của SPSS® từ thống kê giới thiệu đến trung cấp. Cuốn sách được chia thành các phần tập trung vào việc nắm vững các khái niệm cơ bản của SPSS®, xử lý thống kê đơn biến và đồ thị, thống kê suy luận, thống kê quan hệ và nhiều hơn nữa. Được viết bằng phiên bản IBM® SPSS® 25 và 24, và tương thích với các phiên bản trước đó, cuốn sách này là một trong những hướng dẫn SPSS® toàn diện nhất hiện có. Cuốn sách này là một nguồn tài liệu hữu ích cho những ai muốn tìm hiểu về phần mềm SPSS®.
u s i ng iBM SPSS ® ® StatiSticS Second edition I dedicate this textbook to my three children, Sally, James (1965–1996), and Wendy The encouragement and support for their father and his educational pursuits was (and is) above the call of duty —James O Aldrich I dedicate this book to my son, Randy Cunningham, and my friend, Glenn Bailey —James B Cunningham u s i ng iBM SPSS ® ® StatiSticS An Interactive Hands-On Approach Second edition James o aldrich James B cunningham california State University, northridge FOR INFORMATION: Copyright 2016 by SAGE Publications, Inc SAGE Publications, Inc All rights reserved No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher 2455 Teller Road Thousand Oaks, California 91320 E-mail: order@sagepub.com SAGE Publications Ltd Oliver’s Yard 55 City Road London EC1Y 1SP United Kingdom SAGE Publications India Pvt Ltd B 1/I Mohan Cooperative Industrial Area Mathura Road, New Delhi 110 044 India All trademarks depicted within this book, including trademarks appearing as part of a screenshot, figure, or other image are included solely for the purpose of illustration and are the property of their respective holders The use of the trademarks in no way indicates any relationship with, or endorsement by, the holders of said trademarks SPSS is a registered trademark of International Business Machines Corporation SAGE Publications Asia-Pacific Pte Ltd Church Street #10-04 Samsung Hub Singapore 049483 Printed in the United States of America ISBN 978-1-4833-8357-6 This book is printed on acid-free paper Acquisitions Editor: Vicki Knight Editorial Assistant: Yvonne McDuffee Production Editor: Bennie Clark Allen Copy Editor: QuADS Prepress (P) Ltd Typesetter: C&M Digitals (P) Ltd Proofreader: Gretchen Treadwell Indexer: Wendy Allex Cover Designer: Janet Kiesel Marketing Manager: Nicole Elliott 15 16 17 18 19 10 Brief Contents Preface xv Acknowledgments xxi About the Authors xxiii Chapter First Encounters Chapter Navigating in SPSS Chapter Getting Data In and Out of SPSS 17 Chapter Levels of Measurement 26 Chapter Entering Variables and Data and Validating Data 38 Chapter Working With Data and Variables 53 Chapter Using the SPSS Help Menu 65 Chapter Creating Graphs for Nominal and/or Ordinal Data 73 Chapter Graphs for Continuous Data 88 Chapter 10 Printing Data View, Variable View, and Output Viewer Screens 100 Chapter 11 Basic Descriptive Statistics 110 Chapter 12 One-Sample t Test and a Binomial Test of Equality 127 Chapter 13 Independent-Samples t Test and Mann-Whitney U Test 139 Chapter 14 Paired-Samples t Test and Wilcoxon Test 150 Chapter 15 One-Way ANOVA and Kruskal-Wallis Test 161 Chapter 16 Two-Way (Factorial) ANOVA 176 Chapter 17 One-Way ANOVA Repeated Measures Test and Friedman Test 189 Chapter 18 Analysis of Covariance 204 Chapter 19 Pearson’s Correlation and Spearman’s Correlation 219 Chapter 20 Single Linear Regression 232 Chapter 21 Multiple Linear Regression 250 Chapter 22 Logistic Regression 269 Chapter 23 Factor Analysis 286 Chapter 24 Chi-Square Goodness of Fit 300 Chapter 25 Chi-Square Test of Independence 310 Appendix A Class Survey Database (Entered in Chapter 5) 322 Appendix B Basic Inferential Statistics 325 Appendix C Answers to Review Exercises 333 Index 439 Detailed Contents Preface xv Acknowledgments xxi About the Authors Chapter First Encounters 1.1 1.2 1.3 1.4 Introduction and Objectives Entering, Analyzing, and Graphing Data Summary Review Exercises Chapter Navigating in SPSS 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Introduction and Objectives SPSS Variable View Screen SPSS Data View Screen SPSS Main Menu Data Editor Toolbar Variable View Screen: A Closer Look Summary Review Exercises Chapter Getting Data In and Out of SPSS 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 Introduction and Objectives Typing Data Using the Computer Keyboard Saving Your SPSS Data Files Saving Your SPSS Output Files Opening Your Saved SPSS Files Opening SPSS Sample Files Copying and Pasting Data to Other Applications Importing Files From Other Applications Exporting SPSS Files to Other Applications xxiii 1 8 9 11 11 12 15 16 17 17 18 18 19 21 22 23 23 24 3.10 Summary 3.11 Review Exercises Chapter Levels of Measurement 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 Introduction and Objectives Variable View Screen: Measure Column Variables Measured at the Nominal Level Variables Measured at the Ordinal Level Variables Measured at the Scale Level Using SPSS to Suggest Variable Measurement Levels Summary Review Exercises Chapter Entering Variables and Data and Validating Data 5.1 5.2 5.3 5.4 Introduction and Objectives Entering Variables and Assigning Attributes (Properties) Entering Data for Each Variable Validating Data for Databases Validation of Nominal and Ordinal Data Validation of Scale Data 5.5 Summary 5.6 Review Exercises Chapter Working With Data and Variables 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Introduction and Objectives Computing a New Variable Recoding Scale Data Into a String Variable Data Transformation Split Cases for Independent Analysis Inserting New Variables and Cases Into Existing Databases Data View Page: Copy, Cut, and Paste Procedures Summary Review Exercises Chapter Using the SPSS Help Menu 7.1 7.2 7.3 7.4 7.5 7.6 Introduction and Objectives Help Options Using Help Topics Using the Help Tutorial Using Help Case Studies Getting Help When Using Analyze on the Main Menu 24 25 26 26 27 29 30 31 32 36 36 38 38 39 45 45 47 49 51 51 53 53 54 56 59 60 61 62 63 64 65 65 66 67 69 70 71 7.7 Summary 7.8 Review Exercises Chapter Creating Graphs for Nominal and/or Ordinal Data 8.1 8.2 8.3 8.4 8.5 8.6 Introduction and Objectives A Brief Introduction to the Chart Builder Using the Chart Builder to Build a Simple 3-D Graph Building a Population Pyramid Summary Review Exercises Chapter Graphs for Continuous Data 9.1 9.2 9.3 9.4 9.5 9.6 Introduction and Objectives Creating a Histogram Creating a Boxplot Creating a Panel Graph Summary Review Exercises Chapter 10 Printing Data View, Variable View, and Output Viewer Screens 10.1 Introduction and Objectives 10.2 Printing Data From the Variable View Screen Printing a Selected Portion of Your Data 10.3 Printing Variable Information From the Output Viewer 10.4 Printing Tables From the Output Viewer 10.5 Summary 10.6 Review Exercises Chapter 11 Basic Descriptive Statistics 11.1 Introduction and Objectives 11.2 Measures of Central Tendency The Mode The Median The Mean 11.3 Measures of Dispersion Range, Mean, Standard Deviation, and Variance The Shape of the Distribution (Skewness) The Shape of the Distribution (Kurtosis) 11.4 The Big Question: Are the Data Normally Distributed? 11.5 Descriptive Statistics for the Class Survey 72 72 73 73 74 76 82 86 86 88 88 89 91 95 98 99 100 100 101 103 104 105 108 108 110 110 111 112 114 117 118 120 120 121 122 125 434 Using IBM® SPSS® Statistics to determine if the numbers of “A” grades are equal in the five categories This is a one-sample goodness-of-fit chi-square test The principal believes that the teachers not award an equal number of “A” grades; therefore, this becomes the alternative hypothesis The alternative hypothesis (HA) is that the grades are not equally dispersed among the five teachers, and the null hypothesis (H0) is that the grades are equally dispersed among the five teachers We input the data in two columns—one for teacher and one for frequency—then use the weighted measure function found under Data on the Main Menu Finally, we use the nonparametric one-sample test to obtain the results A chi-square value of 2.61 at degrees of freedom and significance of 688 informs us that we must fail to reject the null hypothesis Therefore, we conclude that any grading differences are simply attributable to chance—the principal should not be overly concerned with the complaining students and parents C Appendix 435 Chapter 25: Chi-Square Test of Independence 25.1 A community activist believed that there was a relationship between membership in the police SWAT Team and prior military experience He collected data from several police departments in an effort to support his belief He found that there were 57 members of the SWAT team with prior military experience and 13 members with no prior military service There were also 358 police personnel who had military experience but were not members of SWAT and another 413 with no military experience and not members of SWAT You must write the null and alternative hypotheses, select the correct statistical method, the analysis, and interpret the results Answer: The activist believes that individuals having prior military experience tend to seek out and join the police department’s SWAT team He is basically saying that there is a relationship between military experience and SWAT team membership—this is the alternative hypothesis (HA) The null hypothesis (H0) is that there is no relationship between prior military experience and SWAT team membership We have frequency data and four categories; therefore, a logical choice for analysis is the chi-square test of independence Input the data as three variables—“military experience,” “SWAT,” and “numbers”—in each of the four categories (mil + swat), (no mil + swat), (mil + no swat), and (no mil + no swat) Run the data using Analyze/Descriptive/Crosstabs, then click Statistics and Options Interpretation of the chi-square value of 31.442 at degrees of freedom and significance of 000 informs us that we must reject the null hypothesis of independence The community activist has statistical evidence to support his idea that there is a relationship between membership in the SWAT team and prior military experience 436 Using IBM® SPSS® Statistics 25.2 For this exercise, you will open the SPSS sample file bankloan.sav and determine if there is a relationship between gender and the size of their hometown for these 5,000 bank customers The bank official conducting the research believes that “size of hometown” is definitely related to “gender.” Your task is to assist the bank official in uncovering evidence in support of his belief Write the null and alternative hypotheses, select the appropriate statistical method, conduct the analysis, and interpret the results Answer: You have categorical data consisting of the two variables: “size of hometown,” which has five categories, and two categories for “gender.” The sample consists of 5,000 cases, which must be subdivided into gender and size of hometown Once this is done, you must determine the expected number in each category and then determine if the difference is significant Begin by writing the null and alternative hypotheses, which will serve as a guide for your work The null hypothesis is H0: Gender is independent of (not related to) the size of one’s hometown Another way to state the same thing is that the numbers of males and females coming from hometowns of different sizes are the same The alternative hypothesis is HA: Gender and the size of hometown are related That is, the numbers of females and males coming from different-sized hometowns are not the same Remember that the alternative hypothesis is the bank official’s belief and you are attempting to develop evidence to support his belief You select the chi-square test of independence and use Analyze/ Descriptive/Crosstabs to generate the Crosstabulation table and request a chi-square statistic and expected values for each of the 10 categories The calculated chi-square of 3.021 at degrees of freedom has the significance level of 554 and informs you that the null cannot be rejected You must inform the bank official that there is no statistical evidence of a difference in the numbers of females and males originating from different-sized C Appendix 437 hometowns—they are equal You hope he doesn’t get angry, as this is really important to him You decide not to tell him until tomorrow and go home and have a single malt scotch 25.3 A nutritionist was developing a healthy-eating educational program and was seeking evidence to support her belief that males and females not consume the same amount of vegetables She conducted a survey that categorized people by gender and whether they consumed low, medium, or high amounts of vegetables The numbers for males were low = 29, medium = 21, and high = 16 The numbers for females were low = 21, medium = 25, and high = 33 Write the null and alternative hypotheses, select the correct test, the analysis (include percentages for all categories), and interpret the results Answer: You have categorized the data with simple counts in each category You wish to determine if the counts in the categories are equal or unequal The investigator suspects that they are unequal; therefore, you seek evidence to support this contention Let’s begin by writing the null and 438 Using IBM® SPSS® Statistics alternative hypotheses The null is H0: Gender is independent of (not related to) the amount of vegetables consumed You could also say that males and females consume the same amount of vegetables The alternative hypothesis is HA: Gender and the amount of vegetables consumed are related Or females and males consume different quantities of vegetables We choose chi-square analysis since we have frequency data in unique categories The data are inputted as three variables—“gender,” “vegetable consumption,” and “frequency”—in each of the six categories (male + low veg = 29), (male + med veg = 22), (male + high veg = 16), (female + low veg = 21), (female + med veg = 25), and (female + high veg = 33) Use Analyze/Descriptive./Crosstabs, then click Statistics for chi-square test and the Options tab to request all categorized percentages Interpretation of the chi-square value of 6.412 at degrees of freedom and significance of 041 tells us that we can reject the null hypothesis of independence The nutritionist has statistical evidence to support her idea that there is a relationship between gender and quantity of vegetables consumed She may now proceed with the development of her educational program better informed about her target audience Index Age, levels of measurement for, 34 (figure), 35 Align column, 12 (figure), 15 Alternative hypotheses, 129 See also specific topics Analysis of covariance (ANCOVA): about, 204–205 data input, 206, 207 (figure), 208 homogeneity of regression slopes, testing for, 208–210, 209 (figure), 210 (figure), 211 (figure) main analysis for, 211–216, 212 (figure), 213 (figure), 214 (figure), 215 (figure) research question and null hypothesis, 206 research scenario and test selection, 205 Analysis of variance (ANOVA) tables: multiple linear regression, 261, 262 (figure) single linear regression, 242–243, 243 (figure) See also One-way ANOVA repeated measures test; One-way ANOVA test; Two-way (factorial) ANOVA Analyze option, help for, 71–72, 71 (figure) Analyzing data, 4, (figure) ANCOVA See Analysis of covariance ANOVA See Analysis of variance tables; One-way ANOVA repeated measures test; One-way ANOVA test; Two-way (factorial) ANOVA Attribute column headings, 12–15, 12 (figure), 13 (figure), 14 (figure), 15 (figure) Bartlett’s test of sphericity, 292, 293 (figure) Binomial test of equality, 133–137, 134 (figure), 135 (figure), 136 (figure) See also One-sample t test Bivariate Correlations window, 227–228, 227 (figure), 274, 274 (figure) Boxplots, 92–95, 94 (figure) Cases: inserting into databases, 61, 62, 62 (figure) splitting for independent analysis, 60–61 weighing, 313 Case Studies option, on Help Menu, 66–67, 70 Central tendency measures See Measures of central tendency Chart Builder: about, 74–76, 75 (figure) boxplots, 92–93 histograms, 89–92, 90 (figure), 91 (figure) panel graphs, 95–96, 96 (figure) Pearson’s correlation coefficient, 224–225, 224 (figure) population pyramids, 82–86, 83 (figure), 84 (figure), 85 (figure) 3-D pie graphs, 76–82, 77 (figure), 78 (figure), 79 (figure), 80 (figure), 81 (figure) Chart Editor: boxplots, 92–93 histograms, 89–91, 90 (figure) panel graphs, 96–97 population pyramids, 82–85, 84 (figure) 3-D pie graphs, 77–81, 78 (figure), 79 (figure), 80 (figure) 439 440 Using IBM® SPSS® Statistics Charts See Graphs Chi-square goodness of fit: about, 300–301 data input, analysis, and interpretation of output, 303–304, 303–305 (figure), 306–308, 307–308 (figure) Legacy Dialogs method, 302–304, 303 (figure), 304 (figure), 305 (figure) One Sample method, 305–308, 307 (figure), 308 (figure) research question and null hypothesis, 302–303, 306 research scenario and test selection, 302, 305–306 Chi-square omnibus tests of model coefficients, 278–279, 279 (figure) Chi-square test of independence: about, 310 data input, analysis, and interpretation of output, 312–315, 312 (figure), 314–315 (figure), 316–317, 317–319 (figure), 319–320 raw data, 315–317, 317 (figure), 318 (figure), 319–220, 319 (figure) research question and null hypothesis, 311, 316 research scenario and test selection, 311, 315 summarized data, 311–315, 312 (figure), 314 (figure), 315 (figure) Chi-Square Tests window, 313–315, 315 (figure), 319 (figure), 320 Classification table, 277–278, 278 (figure), 280–281, 280 (figure) Codebooks, 100–101 Coefficients table: multiple linear regression, 262, 262 (figure) single linear regression, 243–244, 244 (figure) Columns column, 12 (figure), 15 Communalities table, 288, 292–293, 293 (figure) Component Matrix table, 294–295, 296 (figure) Computer keyboard, typing data using, 18 Compute Variable: Type and Label window, 54, 55 (figure) Compute Variable window: about, 54, 55 (figure), 56 multiple linear regression, 263–264, 263 (figure) single linear regression, 245, 245 (figure) Computing new variables, 54, 55 (figure), 56 Confidence intervals, 132–133, 145, 145 (figure) Contingency tables, 310, 313, 314 (figure), 317, 319–320, 319 (figure) Continuous data, graphs for, 89–98, 90–91 (figure), 94 (figure), 96 (figure), 98 (figure) Copy-and-paste procedures, 23, 62–63 Correlation coefficients, 219–221, 220 (figure), 286 See also Pearson’s correlation coefficient; Spearman’s correlation coefficient Correlation Matrix table, 290, 291 Covariates, 204 See also Analysis of covariance Cox & Snell R square test, 279–280, 279 (figure) Crosstabs: Cell Display window, 316, 318 (figure) Crosstabs: Statistics window, 316, 318 (figure) Crosstabs window, 313, 314 (figure), 316, 317 (figure) Cross tabulation: chi-square test of independence, 310, 313, 314 (figure), 317, 319–320, 319 (figure) help on, 69 Cut point box, 134, 134 (figure) Cut procedure, 63 Data: analyzing, 4, (figure) copying and pasting to other applications, 23 entering, 2–4, (figure), (figure), (figure), 45 entering for variables, 45 graphing, 5, (figure) interval, 32 printing, 101–103, 102 (figure), 103 (figure) ratio, 32 transformation of, 59, 60 (figure) typing using computer keyboard, 18 validating for databases, 45–46, 46 (figure) validating nominal and ordinal data for databases, 47–49, 47 (figure), 48 (figure), 49 (figure) validating scale data for databases, 49–51, 50 (figure), 51 (figure) See also Nominal data; Ordinal data; Scale data Index 441 Databases: inserting new variables and cases into, 61–62, 61 (figure) validating data for, 45–46, 46 (figure) validating nominal and ordinal data for, 47–49, 47 (figure), 48 (figure), 49 (figure) validating scale data for, 49–51, 50 (figure), 51 (figure) Data Editor, See also Data View screen; Variable View screen Data Editor Toolbar, 11–12, 12 (figure) Data files: exporting to other applications, 24 importing from other applications, 23–24 opening saved, 21 saving, 4, 18, 19 (figure) Data transformation, 59, 60 (figure) Data View screen: about, 2, 3, (figure), 9–10, 10 (figure) analysis of covariance, 207 (figure) chi-square goodness of fit, 304 (figure) chi-square test of independence, 312, 312 (figure) copy, cut, and paste procedures, 62–63 entering data for variables, 45 logistic regression, 273, 273 (figure) multiple linear regression, 264, 264 (figure) one-way ANOVA test, 166, 167 (figure) printing, 101–103, 102 (figure), 103 (figure) single linear regression, 235, 236 (figure) Decimals column, 12 (figure), 14 Define Groups window, 143, 144 (figure) Define Variable Properties window, 33, 33 (figure), 34 (figure), 35 Depth & Angle tab, in Chart Editor, 79–80, 79 (figure), 80 (figure) Descriptive/exploratory factor analysis, 286–287 See also Factor analysis Descriptives: Options window, 50, 50 (figure), 119 (figure) Descriptive statistics, 110–111 See also specific techniques Descriptive Statistics table: about, 51, 51 (figure) analysis of covariance, 212, 214 (figure) factor analysis, 291 measures of dispersion, 119–120, 119 (figure) Descriptive Statistics window: one-way ANOVA repeated measures test, 195, 196 (figure) two-way (factorial) ANOVA, 183–184, 184 (figure) Descriptives window, 20, 20 (figure) Directional hypotheses, 153n Dispersion measures See Measures of dispersion Eigenvalues, 288, 294, 294 (figure) Entering data, 2–4, (figure), (figure), (figure), 45 Entering variables and assigning attributes, 39–42, 40 (figure), 41 (figure), 42 (figure), 43 (figure), 44 (figure) Error terms: multiple linear regression, 259, 260 (figure) single linear regression, 240–241, 240 (figure) Excel: copying and pasting data to, 23 exporting data files to, 24 importing data files from, 23–24 Exporting data files to other applications, 24 Factor analysis: about, 286–288 data input, analysis, and interpretation of output, 289–298, 290–297 (figure) research question and null hypothesis, 289 research scenario and test selection, 289 Factor Analysis: Descriptives window, 290, 291 (figure) Factor Analysis: Extraction window, 290, 292 (figure) Factor Analysis window, 290, 290 (figure) Factor extraction, 287–288, 290–291, 292 (figure) Factor loadings, 287, 294–295, 296 (figure) Factor rotation, 288, 295–296, 297 (figure) Factors, 287 Find icon, 48, 48 (figure) Frequencies: help on, 70, 72 median and, 115, 115 (figure), 116 (figure) Frequency tables: mode and, 112–114, 113 (figure), 114 (figure) validation of nominal and ordinal data, 47–48, 48 (figure) 442 Using IBM® SPSS® Statistics Friedman test, 190, 198–202, 199 (figure), 200 (figure), 201 (figure) See also One-way ANOVA repeated measures test F statistic, 161, 162 See also One-way ANOVA test Gender, as nominal variable, 29 Graphing data, 5, (figure) Graphs: boxplots, 92–95, 94 (figure) for continuous data, 89–98, 90–91 (figure), 94 (figure), 96 (figure), 98 (figure) histograms, 89–92, 90 (figure), 91 (figure), 94–95, 95–97, 98 (figure) for nominal data, 76–86 for ordinal data, 76–86 panel graphs, 95–98, 96 (figure), 98 (figure) population pyramids, 82–86, 83 (figure), 84 (figure), 85 (figure) printing from Output Viewer, 106n for scale data, 89–98 3-D pie graphs, 76–82, 77 (figure), 78 (figure), 79 (figure), 80 (figure), 81 (figure) Grouping variables, 142 Help: for Analyze option, 71–72, 71 (figure) on cross tabulation, 69 on frequencies, 70, 72 on levels of measurement, 68–69, 68 (figure) on missing value analysis, 71, 71 (figure) on variable properties, 67–68, 67 (figure) Help Menu: about, 65 Case Studies option, 66–67, 70 Topics option, 66, 67–69, 67 (figure), 68 (figure) Tutorial option, 66, 69–70 Histograms: boxplots versus, 94–95 creating, 89–92, 90 (figure), 91 (figure) paneled, 95–97, 98 (figure) Homogeneity of regression slopes, testing, 208–210, 209 (figure), 210 (figure), 211 (figure) Homoscedasticity, 234, 241, 252 Hosmer-Lemeshow Goodness of Fit Test, 280, 280 (figure) Hypothesis Test Summary box: binomial test of equality, 135, 136 (figure) chi-square goodness of fit, 308, 308 (figure) Friedman test, 199, 200 (figure), 201 Kruskal-Wallis test, 171, 171 (figure) Mann-Whitney U test, 136 (figure), 147–148, 147 (figure) multiple linear regression, 254, 255 (figure), 256 (figure) single linear regression, 237, 237 (figure) Icons: Find, 48, 48 (figure) Insert Cases, 62 (figure) Insert Variable, 61 (figure) for levels of measurement, 36 for nominal data, 36 for ordinal data, 36 for scale data, 36 Importing data files from other applications, 23–24 Independent-samples t test, 139, 141–143, 142 (figure), 144 (figure), 145–146, 145 (figure) See also Mann-Whitney U test Inferential statistics, 110 See also specific techniques Insert Cases icon, 62 (figure) Inserting new variables and cases into databases, 61–62, 61 (figure) Insert Variable icon, 61 (figure) Interaction effects, 177, 177 (figure) See also Two-way (factorial) ANOVA Interval data, 32 Kaiser-Meyer-Olkin measure of sampling adequacy, 292, 293 (figure) Kolmogorov-Smirnov test: about, 123–125, 124 (figure) multiple linear regression, 254–255, 255 (figure), 256 (figure) Pearson’s correlation coefficient, 226–227, 226 (figure) single linear regression, 237, 237 (figure) Kruskall-Wallis test, 163, 170–171, 171–173 (figure), 173 See also One-way ANOVA test Kurtosis, 121–122, 122 (figure) Index 443 Label column, 12 (figure), 14, 14 (figure) Legacy Dialogs: about, 133–134, 134 (figure) chi-square goodness of fit, 302–304, 303 (figure), 304 (figure), 305 (figure) Leptokurtic distribution, 122, 122 (figure) Levels of measurement: defined, 26 help on, 68–69, 68 (figure) hierarchy of, 28–29 icons for, 36 using SPSS to suggest, 32–33, 33 (figure), 34 (figure), 35, 35 (figure) See also Nominal data; Ordinal data; Scale data Levene’s test, 184, 185 (figure), 213, 214 (figure) Lilliefors correction, 254, 255 (figure) Linear regression See Multiple linear regression; Single linear regression Linear Regression: Plots window: multiple linear regression, 258, 259 (figure) single linear regression, 239–240, 239 (figure) Linear Regression: Statistics window: multiple linear regression, 258, 258 (figure) single linear regression, 239, 239 (figure) Linear Regression window: multiple linear regression, 257–258, 257 (figure) single linear regression, 238–239, 238 (figure) Linear relationship between variables, checking for, 223–225, 224 (figure), 225 (figure) Logistic regression: about, 270 analysis, 275–277, 276 (figure), 277 (figure) data input, 272–275, 273 (figure), 274 (figure), 275 (figure) interpretation of output, 277–282, 278 (figure), 279 (figure), 280 (figure), 281 (figure) research question and null hypothesis, 272 research scenario and test selection, 271–272 Logistic Regression: Define Categorical Variables window, 276–277, 277 (figure) Logistic Regression window, 275–276, 276 (figure) Main effects, 177, 177 (figure) See also Two-way (factorial) ANOVA Main Menu, 11, 11 (figure) Mann-Whitney U test, 139–140, 146–148, 147 (figure) See also Independent-samples t test Marital satisfaction, as ordinal data, 31 Mauchly’s test for equality of variances, 195–196, 196 (figure) Mean, 117–118, 117 (figure), 120 Measure column, 12 (figure), 15, 27–29, 28 (figure) Measures of central tendency, 111–118 about, 111–112 mean, 117–118, 117 (figure) median, 114–115, 115 (figure), 116 (figure) mode, 112–114, 113 (figure), 114 (figure) Measures of dispersion, 118–122 about, 118–120, 119 (figure) kurtosis, 121–122, 122 (figure) mean, 120 range, 120 skewness, 120–121, 121 (figure) standard deviation, 120 variance, 120 Median, 114–115, 115 (figure), 116 (figure) Missing column, 12 (figure), 14, 15 (figure) Missing Value Analysis window, 71, 71 (figure) Missing Values window, 14, 15 (figure) Mode, 112–114, 113 (figure), 114 (figure) Model Summary: logistic regression, 279–280, 279 (figure) multiple linear regression, 261, 261 (figure) single linear regression, 242, 242 (figure) Model Viewer window: Friedman test, 200 (figure), 201 Kruskal-Wallis test, 171, 172 (figure) Multicollinearity, 270, 275, 275 (figure) Multiple linear regression: about, 250–251 data assumptions (normality), 254–255, 255 (figure), 256 (figure) data input, 252–254, 253 (figure) interpretation of output (data assumptions), 259, 260 (figure) interpretation of output (regression and prediction), 261–265, 261 (figure), 262 (figure), 263 (figure), 264 (figure) regression and prediction, 257–258, 257 (figure), 258 (figure), 259 (figure) 444 Using IBM® SPSS® Statistics research question and null hypothesis, 252 research question answered, 265 research scenario and test selection, 251–252 Nagelkerke R square tests, 279–280, 279 (figure) Name column, 12 (figure), 13 Navigating in SPSS, 9–15 Negative kurtosis, 122, 122 (figure) Negative skew, 121, 121 (figure) Nominal data: about, 29–30 defined, 26 graphs for, 76–86 icon for, 36 validating for databases, 47–49, 47 (figure), 48 (figure), 49 (figure) Nonparametric test for normality, 123–125, 124 (figure) Normality, checking for: about, 122–125, 123 (figure), 124 (figure) Pearson’s correlation coefficient, 225–227, 226 (figure) Null hypotheses, 129 See also specific topics Omnibus Tests of Model Coefficients table, 278–279, 279 (figure) One Sample method (chi-square goodness of fit): data input, analysis, and interpretation of output, 306–308, 307 (figure), 308 (figure) research question and null hypothesis, 306 research scenario and test selection, 305–306 One-Sample Nonparametric Tests window: about, 123–125, 124 (figure) binomial test of equality, 135–137, 135 (figure), 136 (figure) chi-square goodness of fit, 306, 307 (figure) One-sample t test, 130, 131 (figure), 132 See also Binomial test of equality One-sided hypotheses, 153n One-way ANOVA repeated measures test: about, 189 data input, analysis, and interpretation of output, 192–197 (figure), 192–198 research question and null hypothesis, 191 research scenario and test selection, 190–191 See also Friedman test One-way ANOVA test: about, 161–162 data input, analysis, and interpretation of output, 164–168, 165–169 (figure), 170 research question and null hypothesis, 164 research scenario and test selection, 163 See also Kruskall-Wallis test Opening: saved data files, 21 saved output files, 21–22 saved sample files, 22–23 Ordinal data: about, 30–31 defined, 26 graphs for, 76–86 icon for, 36 validating for databases, 47–49, 47 (figure), 48 (figure), 49 (figure) Output files: opening saved, 21–22 saving, 19–20, 20 (figure), 21 (figure) Output Viewer: Descriptive Statistics table, 51, 51 (figure) logistic regression, 275, 275 (figure), 277 printing graphs from, 106n printing tables from, 105–106, 107 (figure), 108, 108 (figure) printing variable information from, 104–105, 104 (figure), 105 (figure) single linear regression, 245, 246 (figure) Paired-samples t test, 141, 150–156, 154 (figure), 155 (figure) See also Wilcoxon signed-ranks test Pairwise comparisons: analysis of covariance, 215, 215 (figure) Friedman test, 199, 200 (figure), 201–202, 201 (figure) Kruskal-Wallis test, 163, 173 one-way ANOVA repeated measures test, 197–198, 197 (figure) Panel graphs, 95–98, 96 (figure), 98 (figure) Pearson’s correlation coefficient: about, 221 calculation and test of significance, 227–228, 227 (figure), 228 (figure) data input, analysis, and interpretation of output, 223–228, 224–228 (figure) Index 445 linear relationship between variables, checking for, 223–225, 224 (figure), 225 (figure) normality, checking variables for, 225–227, 226 (figure) research question and null hypothesis, 223 research scenario and test selection, 222 See also Multiple linear regression; Single linear regression; Spearman’s correlation coefficient Platykurtic distribution, 122, 122 (figure) Population pyramids, 82–86, 83 (figure), 84 (figure), 85 (figure) Positive kurtosis, 122, 122 (figure) Positive skew, 121, 121 (figure) Post hoc analysis, 162 P-P Plot: about, 123, 123 (figure) multiple linear regression, 259, 260 (figure) single linear regression, 240–241, 240 (figure) Prediction equation: about, 233 multiple linear regression, 262–265, 262 (figure), 263 (figure), 264 (figure) single linear regression, 243–246, 244 (figure), 245 (figure), 246 (figure) Pretest and posttest research methodology, 150–151 See also Paired-samples t test; Wilcoxon signed-ranks test Principal component factor analysis, 286–287 See also Factor analysis Printing: Data View screen, 101–103, 102 (figure), 103 (figure) graphs from Output Viewer, 106n selected portion of data, 103 tables from Output Viewer, 105–106, 107 (figure), 108, 108 (figure) variable information from Output Viewer, 104–105, 104 (figure), 105 (figure) Probability plots: about, 123, 123 (figure) multiple linear regression, 259, 260 (figure) single linear regression, 240–241, 240 (figure) Properties window: boxplots, 93 histograms, 90–91, 91 (figure) panel graphs, 96–97 population pyramids, 83–85, 84 (figure) 3-D pie charts, 78–81, 79 (figure), 80 (figure) Range, 120 Ranked data See Ordinal data Ratio data, 32 Recode into Different Variables: Old and New Values window, 57–58, 58 (figure) Recode into Different Variables window, 56–57, 57 (figure) Recoding scale data into string variables, 56–59, 57 (figure), 58 (figure) Repeated Measures: Options window, 195, 195 (figure) Repeated Measures Define Factor(s) window, 193, 193 (figure) Repeated Measures window, 193–194, 194 (figure) Research questions, 129 See also specific topics Role column, 12 (figure), 15 Rotated Component Matrix table, 295–296, 297 (figure) Sample files, opening saved, 22–23 Save Data As window, 18, 19 (figure) Save or Replace window, 18, 19 (figure) Save Output As window, 20, 21 (figure) Saving: data files, 4, 18, 19 (figure) output files, 19–20, 20 (figure), 21 (figure) Scale data: about, 31–32 defined, 26 graphs for, 89–98 icon for, 36 recoding into string variables, 56–59, 57 (figure), 58 (figure) validating for databases, 49–51, 50 (figure), 51 (figure) Scatterplots: multiple linear regression, 259, 260 (figure) Pearson’s correlation coefficient, 224–225, 225 (figure) single linear regression, 241, 241 (figure) Scheffe test, 168, 169 (figure), 170 Scree plots, 288, 294, 295 (figure) 446 Using IBM® SPSS® Statistics Significance, 130, 131 (figure), 132, 145–146 Single linear regression: about, 232–233 data assumptions (normality), 236–237, 237 (figure) data input, 235, 235 (figure), 236 (figure) interpretation of output (data assumptions), 240–242, 240 (figure), 241 (figure) interpretation of output (regression and prediction), 242–246, 242 (figure), 243 (figure), 244 (figure), 245 (figure), 246 (figure) regression and prediction, 238–240, 238 (figure), 239 (figure) research question and null hypothesis, 234 research question answered, 246–247, 247 (figure) research scenario and test selection, 234 Skewness, 120–121, 121 (figure) Socioeconomic status, as ordinal data, 31 Spearman’s correlation coefficient: about, 221, 228–230, 229 (figure) logistic regression, 274–275, 275 (figure) See also Pearson’s correlation coefficient Sphericity, 292, 293 (figure) Splitting cases for independent analysis, 60–61 Square root transformation of data, 59, 60 (figure) Standard deviation, 120 String variables, recoding scale data into, 56–59, 57 (figure), 58 (figure) Suggest button, 33, 34 (figure), 35 Tables, printing from Output Viewer, 105–106, 107 (figure), 108, 108 (figure) Test Proportion box, 134, 134 (figure) Tests of within-subjects effects, 196, 197 (figure) Test Statistics, 304, 305 (figure) 3-D pie graphs, 76–82, 77 (figure), 78 (figure), 79 (figure), 80 (figure), 81 (figure) Topics option, on Help Menu, 66, 67–69, 67 (figure), 68 (figure) Total Variance Explained table, 294, 294 (figure) Transformation of data, 59, 60 (figure) t tests: independent-samples, 139, 141–143, 142 (figure), 144 (figure), 145–146, 145 (figure) one-sample, 130, 131 (figure), 132 paired-samples, 141, 150–156, 154 (figure), 155 (figure) Tutorial option, on Help Menu, 66, 69–70 Two-way (factorial) ANOVA: about, 176–177, 177 (figure) data input, analysis, and interpretation of output, 180–185, 180–186 (figure) research question and null hypothesis, 179 research scenario and test selection, 178–179 Type column, 12 (figure), 13, 13 (figure) Typing data using computer keyboard, 18 Univariate: Options window, 183 (figure) Univariate window, 182 (figure) Validate Data module, 45–46 Validating data for databases: about, 45–46, 46 (figure) nominal and ordinal data, 47–49, 47 (figure), 48 (figure), 49 (figure) scale data, 49–51, 50 (figure), 51 (figure) Value Labels window, 165, 166 (figure) about, 14, 14 (figure) entering variables and assigning attributes, 39, 40 (figure), 41 (figure), 42 (figure), 43 (figure), 44 (figure) Values column, 12 (figure), 14 Variables: computing new, 54, 55 (figure), 56 entering and assigning attributes, 39–42, 40 (figure), 41 (figure), 42 (figure), 43 (figure), 44 (figure) entering data for, 45 grouping, 142 help on properties, 67–68, 67 (figure) inserting into databases, 61–62, 61 (figure) linear relationship between, checking for, 223–225, 224 (figure), 225 (figure) nominal level, measurement at, 29–30, 36 ordinal level, measurement at, 30–31, 36 printing information from Output Viewer, 104–105, 104 (figure), 105 (figure) recoding scale data into string, 56–59, 57 (figure), 58 (figure) scale level, measurement at, 31–32, 36 Variables in the Equation window, 278, 281, 281 (figure) Variable Type window, 13–14, 13 (figure) Index 447 Variable View screen: about, 2, (figure), (figure), 9, (figure), 10 (figure) analysis of covariance, 207 (figure) attribute column headings, 12–15, 12 (figure), 13 (figure), 14 (figure), 15 (figure) chi-square test of independence, 312, 312 (figure) entering variables and assigning attributes, 39–42, 44 (figure) logistic regression, 272, 273 (figure) Measure column in, 12 (figure), 15, 27–29, 28 (figure) multiple linear regression, 253–254, 253 (figure) single linear regression, 235, 235 (figure), 247 (figure) two-way (factorial) ANOVA, 180, 180 (figure) Variance, 120 Wald statistical test, 278, 278 (figure) Weighing cases, 313 Width column, 12 (figure), 13 Wilcoxon signed-ranks test, 151, 156–158, 157 (figure), 158 (figure) See also Paired-samples t test