{"product_id":"performing-data-analysis-using-ibm-spss-isbn-9781118357019","title":"Performing Data Analysis Using IBM SPSS","description":"\u003cp\u003eFeatures easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS®\u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output.\u003c\/p\u003e \u003cp\u003eDesigned as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of \u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis.\u003c\/p\u003e \u003cp\u003eThe book provides in-depth chapter coverage of:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIBM SPSS statistical output\u003c\/li\u003e \u003cli\u003eDescriptive statistics procedures\u003c\/li\u003e \u003cli\u003eScore distribution assumption evaluations\u003c\/li\u003e \u003cli\u003eBivariate correlation\u003c\/li\u003e \u003cli\u003eRegressing (predicting) quantitative and categorical variables\u003c\/li\u003e \u003cli\u003eSurvival analysis\u003c\/li\u003e \u003cli\u003et Test\u003c\/li\u003e \u003cli\u003eANOVA and ANCOVA\u003c\/li\u003e \u003cli\u003eMultivariate group differences\u003c\/li\u003e \u003cli\u003eMultidimensional scaling\u003c\/li\u003e \u003cli\u003eCluster analysis\u003c\/li\u003e \u003cli\u003eNonparametric procedures for frequency data\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Getting Started with Ibm Spss® 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1 Introduction to Ibm Spss® 3\u003c\/p\u003e \u003cp\u003eChapter 2 Entering Data in Ibm Spss® 5\u003c\/p\u003e \u003cp\u003eChapter 3 Importing Data From Excel to Ibm Spss® 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Obtaining, Editing, and Saving Statistical Output 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 4 Performing Statistical Procedures In Ibm Spss® 21\u003c\/p\u003e \u003cp\u003eChapter 5 Editing Output 27\u003c\/p\u003e \u003cp\u003eChapter 6 Saving and Copying Output 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3 Manipulating Data 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 7 Sorting and Selecting Cases 39\u003c\/p\u003e \u003cp\u003eChapter 8 Splitting Data Files 45\u003c\/p\u003e \u003cp\u003eChapter 9 Merging Data From Separate Files 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4 Descriptive Statistics Procedures 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 10 Frequencies 59\u003c\/p\u003e \u003cp\u003eChapter 11 Descriptives 67\u003c\/p\u003e \u003cp\u003eChapter 12 Explore 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5 Simple Data Transformations 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 13 Standardizing Variables to Z Scores 79\u003c\/p\u003e \u003cp\u003eChapter 14 Recoding Variables 83\u003c\/p\u003e \u003cp\u003eChapter 15 Visual Binning 97\u003c\/p\u003e \u003cp\u003eChapter 16 Computing New Variables 103\u003c\/p\u003e \u003cp\u003eChapter 17 Transforming Dates to Age 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6 Evaluating Score Distribution Assumptions 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 18 Detecting Univariate Outliers 123\u003c\/p\u003e \u003cp\u003eChapter 19 Detecting Multivariate Outliers 131\u003c\/p\u003e \u003cp\u003eChapter 20 Assessing Distribution Shape: Normality, Skewness, and Kurtosis 139\u003c\/p\u003e \u003cp\u003eChapter 21 Transforming Data to Remedy Statistical Assumption Violations 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 7 Bivariate Correlation 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 22 Pearson Correlation 159\u003c\/p\u003e \u003cp\u003eChapter 23 Spearman Rho and Kendall Tau-b Rank-order Correlations 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 8 Regressing (predicting) Quantitative Variables 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 24 Simple Linear Regression 173\u003c\/p\u003e \u003cp\u003eChapter 25 Centering the Predictor Variable in Simple Linear Regression 181\u003c\/p\u003e \u003cp\u003eChapter 26 Multiple Linear Regression 191\u003c\/p\u003e \u003cp\u003eChapter 27 Hierarchical Linear Regression 211\u003c\/p\u003e \u003cp\u003eChapter 28 Polynomial Regression 217\u003c\/p\u003e \u003cp\u003eChapter 29 Multilevel Modeling 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 9 Regressing (predicting) Categorical Variables 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 30 Binary Logistic Regression 255\u003c\/p\u003e \u003cp\u003eChapter 31 Roc Analysis 265\u003c\/p\u003e \u003cp\u003eChapter 32 Multinominal Logistic Regression 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 10 Survival Analysis 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 33 Survival Analysis: Life Tables 283\u003c\/p\u003e \u003cp\u003eChapter 34 The Kaplan–Meier Survival Analysis 289\u003c\/p\u003e \u003cp\u003eChapter 35 Cox Regression 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 11 Reliability as a Gauge of Measurement Quality 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 36 Reliability Analysis: Internal Consistency 311\u003c\/p\u003e \u003cp\u003eChapter 37 Reliability Analysis: Assessing Rater Consistency 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 12 Analysis of Structure 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 38 Principal Components and Factor Analysis 331\u003c\/p\u003e \u003cp\u003eChapter 39 Confirmatory Factor Analysis 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 13 Evaluating Causal (predictive) Models 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 40 Simple Mediation 381\u003c\/p\u003e \u003cp\u003eChapter 41 Path Analysis Using Multiple Regression 389\u003c\/p\u003e \u003cp\u003eChapter 42 Path Analysis Using Structural Equation Modeling 397\u003c\/p\u003e \u003cp\u003eChapter 43 Structural Equation Modeling 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 14 t TEST 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 44 One-Sample t Test 459\u003c\/p\u003e \u003cp\u003eChapter 45 Independent-Samples t Test 463\u003c\/p\u003e \u003cp\u003eChapter 46 Paired-Samples t Test 471\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 15 Univariate Group Differences: Anova and Ancova 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 47 One-way Between-subjects Anova 477\u003c\/p\u003e \u003cp\u003eChapter 48 Polynomial Trend Analysis 485\u003c\/p\u003e \u003cp\u003eChapter 49 One-way Between-subjects Ancova 493\u003c\/p\u003e \u003cp\u003eChapter 50 Two-way Between-subjects Anova 507\u003c\/p\u003e \u003cp\u003eChapter 51 One-way Within-subjects Anova 521\u003c\/p\u003e \u003cp\u003eChapter 52 Repeated Measures Using Linear Mixed Models 531\u003c\/p\u003e \u003cp\u003eChapter 53 Two-way Mixed Anova 555\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 16 Multivariate Group Differences: Manova and Discriminant Function Analysis 567\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 54 One-way Between-subjects Manova 569\u003c\/p\u003e \u003cp\u003eChapter 55 Discriminant Function Analysis 579\u003c\/p\u003e \u003cp\u003eChapter 56 Two-way Between-subjects Manova 591\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 17 Multidimensional Scaling 603\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 57 Multidimensional Scaling: Classical Metric 605\u003c\/p\u003e \u003cp\u003eChapter 58 Multidimensional Scaling: Metric Weighted 613\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 18 Cluster Analysis 621\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 59 Hierarchical Cluster Analysis 623\u003c\/p\u003e \u003cp\u003eChapter 60 K-means Cluster Analysis 631\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 19 Nonparametric Procedures for Analyzing Frequency Data 643\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 61 Single-sample Binomial and Chi-square Tests: Binary Categories 645\u003c\/p\u003e \u003cp\u003eChapter 62 Single-sample (one-way) Multinominal Chi-square Tests 655\u003c\/p\u003e \u003cp\u003eChapter 63 Two-way Chi-square Test of Independence 665\u003c\/p\u003e \u003cp\u003eChapter 64 Risk Analysis 675\u003c\/p\u003e \u003cp\u003eChapter 65 Chi-square Layers 681\u003c\/p\u003e \u003cp\u003eChapter 66 Hierarchical Loglinear Analysis 689\u003c\/p\u003e \u003cp\u003eAppendix Statistics Tables 699\u003c\/p\u003e \u003cp\u003eReferences 703\u003c\/p\u003e \u003cp\u003eAuthor Index 713\u003c\/p\u003e \u003cp\u003eSubject Index 715 \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eLAWRENCE S. MEYERS, PhD,\u003c\/b\u003e is Professor in the Depart-ment of Psychology at California State University, Sacramento. The author of numerous books, Dr. Meyers is a member of the Association for Psychological Science and the Society for Industrial and Organiza-tional Psychology.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGLENN C. GAMST, PhD,\u003c\/b\u003e is Chair and Professor in the Department of Psychology at the University of La Verne. His research interests include univariate and multivariate statistics as well as multicultural community mental health outcome research.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA. J. Guarino, PhD,\u003c\/b\u003e is Professor of Biostatistics at Massachusetts General Hospital, Institute of Health Professions, where he serves as the methodologist for capstones and dissertations as well as teaching advanced Biostatistics courses. Dr. Guarino is also the statistician on numerous National Institutes of Health grants and coauthor of several statistical textbooks.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eFeatures easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS®\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output.\u003c\/p\u003e \u003cp\u003eDesigned as a user's guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of \u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis.\u003c\/p\u003e \u003cp\u003eThe book provides in-depth chapter coverage of:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIBM SPSS statistical output\u003c\/li\u003e \u003cli\u003eDescriptive statistics procedures\u003c\/li\u003e \u003cli\u003eScore distribution assumption evaluations\u003c\/li\u003e \u003cli\u003eBivariate correlation\u003c\/li\u003e \u003cli\u003eRegressing (predicting) quantitative and categorical variables\u003c\/li\u003e \u003cli\u003eSurvival analysis\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003et\u003c\/i\u003e Test\u003c\/li\u003e \u003cli\u003eANOVA and ANCOVA\u003c\/li\u003e \u003cli\u003eMultivariate group differences\u003c\/li\u003e \u003cli\u003eMultidimensional scaling\u003c\/li\u003e \u003cli\u003eCluster analysis\u003c\/li\u003e \u003cli\u003eNonparametric procedures for frequency data\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePerforming Data Analysis Using IBM SPSS\u003c\/i\u003e is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989765406949,"sku":"NP9781118357019","price":91.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118357019.jpg?v=1761785392","url":"https:\/\/k12savings.com\/es\/products\/performing-data-analysis-using-ibm-spss-isbn-9781118357019","provider":"K12savings","version":"1.0","type":"link"}