Using 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 Using IBM® SPSS® Statistics An Interactive Hands-On Approach Second Edition James O aldrich James B cunningham California State University, Northridge FOR INFORMATION: SAGE Publications, Inc 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 SAGE Publications Asia-Pacific Pte Ltd Church Street #10-04 Samsung Hub Singapore 049483 Copyright © 2016 by 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 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 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 Detailed Contents Detailed Contents Preface Acknowledgments About the Authors Chapter First Encounters 1.1 Introduction and Objectives 1.2 Entering, Analyzing, and Graphing Data 1.3 Summary 1.4 Review Exercises Chapter Navigating in SPSS 2.1 Introduction and Objectives 2.2 SPSS Variable View Screen 2.3 SPSS Data View Screen 2.4 SPSS Main Menu 2.5 Data Editor Toolbar 2.6 Variable View Screen: A Closer Look 2.7 Summary 2.8 Review Exercises Chapter Getting Data In and Out of SPSS 3.1 Introduction and Objectives 3.2 Typing Data Using the Computer Keyboard 3.3 Saving Your SPSS Data Files 3.4 Saving Your SPSS Output Files 3.5 Opening Your Saved SPSS Files 3.6 Opening SPSS Sample Files 3.7 Copying and Pasting Data to Other Applications 3.8 Importing Files From Other Applications 3.9 Exporting SPSS Files to Other Applications 3.10 Summary 3.11 Review Exercises Chapter Levels of Measurement 4.1 Introduction and Objectives 4.2 Variable View Screen: Measure Column 4.3 Variables Measured at the Nominal Level 4.4 Variables Measured at the Ordinal Level 4.5 Variables Measured at the Scale Level 4.6 Using SPSS to Suggest Variable Measurement Levels 4.7 Summary 4.8 Review Exercises Chapter Entering Variables and Data and Validating Data 5.1 Introduction and Objectives 5.2 Entering Variables and Assigning Attributes (Properties) 5.3 Entering Data for Each Variable 5.4 Validating Data for Databases 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) 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 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) 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) 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) 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) 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) 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) 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 [...]... Chapter 7 describes and explains the Help Menu available in SPSS and how to find information on various statistical tests and procedures Chapters 8 and 9 provide hands-on experience in creating and editing graphs and charts Chapter 10 provides explicit directions for printing files, the output from statistical analysis, and graphs Chapter 11 describes and explains basic descriptive statistics Finally, Chapters... Significance B.11 One- and Two-Tailed Tests B.12 Degrees of Freedom Appendix C Answers to Review Exercises Index Preface to the Second Edition Introduction to the Preface This second edition was written while using IBM SPSS Statistics* Version 22 The first edition was written while using Versions 18 and 20 Although Version 22 is the most recent version available, it is certainly compatible with the earlier... fall within the scope of testing for significance or prediction Furthermore, we have found that the principal component approach to factor analysis can be an exciting descriptive/exploratory method for the new student/statistician Discovering new latent variables can provide openings for creativity and can actually be fun! Such creativity and fun will be within the reach of anyone reading and practicing... and Hands-On Chemistry Activities With Real-Life Applications He used SPSS extensively during his tenure as director of the Credential Evaluation Unit in the College of Education He is a past fellow in the Center for Teaching and Learning at California State University, Northridge Chapter 1 First Encounters 1.1 Introduction and Objectives Hi, and welcome to IBM SPSS Statistics We assume you know little... analysis an exciting adventure into the unknown We felt that many (or most) of the SPSS instructional textbooks utilize existing databases and provide minimal, if any, guidance on how to structure and enter data In this second edition, we continue with the philosophy that it is wise to know how to enter data into the SPSS program On leaving the academy and finding work in the real world, the ability to analyze... background in statistics but will also provide basic information to those individuals who know little or nothing about statistics The book is for those who want SPSS to do the actual statistical and analytical work for them They want to know how to get their data into SPSS and how to organize and code the data so SPSS can make sense of them Once this is accomplished, they want to know how to ask SPSS to analyze... showing both descriptive univariate and exploratory bivariate graphing examples This edition gives the reader hands-on experience in producing quality graphs by using the SPSS feature known as the Chart Builder Knowledge of the Chart Builder will surely enhance one’s ability to better understand data through graphing and visualization of summarized databases Although our original intent in writing this... Attributes for class_survey1.sav Table A.2 Data for class_survey1.sav Appendix B Basic Inferential Statistics B.1 Introduction B.2 Populations and Samples B.3 Sampling Procedures B.4 Hypothesis Testing B.5 Parametric Statistical Tests B.6 Nonparametric Statistical Tests B.7 Data Transformation B.8 Type I and Type II Errors B.9 Tests of Significance B.10 Practical Significance Versus Statistical Significance... the American Statistical Association and has also taught biostatistics, epidemiology, social statistics, and research methods courses for 20 years The primary statistical software used for his coursework has been SPSS SAGE recently published, in 2013, Building SPSS Graphs to Understand Data, coauthored with Hilda M Rodriguez James B Cunningham (PhD in Science Education, Syracuse University) is Professor... Emeritus of Science and Computer Education and former chair of the Department of Secondary Education at California State University, Northridge, and of the Departments of Science and Mathematics in Washington State high schools He is the author of Teaching Metrics Simplified and coauthor of BASIC for Teachers, Authoring Educational Software, Hands-On Physics Activities With Real-Life Applications, and