Performing Data Analysis Using IBM SPSS

Häftad, Engelska, 2013

1 868 kr

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Beskrivning

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® 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.Designed 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 Performing Data Analysis Using IBM SPSS 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.The book provides in-depth chapter coverage of: IBM SPSS statistical outputDescriptive statistics proceduresScore distribution assumption evaluationsBivariate correlationRegressing (predicting) quantitative and categorical variablesSurvival analysist TestANOVA and ANCOVAMultivariate group differencesMultidimensional scalingCluster analysisNonparametric procedures for frequency dataPerforming Data Analysis Using IBM SPSS 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.

Produktinformation

Utgivningsdatum:2013-09-27
Mått:218 x 281 x 39 mm
Vikt:1 683 g
Format:Häftad
Språk:Engelska
Antal sidor:736
Förlag:John Wiley & Sons Inc
ISBN:9781118357019

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Mer om författaren

LAWRENCE S. MEYERS, PhD, 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.GLENN C. GAMST, PhD, 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.A. J. Guarino, PhD, 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.

Innehållsförteckning

Preface ixPart 1 Getting Started with Ibm Spss® 1Chapter 1 Introduction to Ibm Spss® 3Chapter 2 Entering Data in Ibm Spss® 5Chapter 3 Importing Data From Excel to Ibm Spss® 15Part 2 Obtaining, Editing, and Saving Statistical Output 19Chapter 4 Performing Statistical Procedures In Ibm Spss® 21Chapter 5 Editing Output 27Chapter 6 Saving and Copying Output 31Part 3 Manipulating Data 37Chapter 7 Sorting and Selecting Cases 39Chapter 8 Splitting Data Files 45Chapter 9 Merging Data From Separate Files 51Part 4 Descriptive Statistics Procedures 57Chapter 10 Frequencies 59Chapter 11 Descriptives 67Chapter 12 Explore 71Part 5 Simple Data Transformations 77Chapter 13 Standardizing Variables to Z Scores 79Chapter 14 Recoding Variables 83Chapter 15 Visual Binning 97Chapter 16 Computing New Variables 103Chapter 17 Transforming Dates to Age 111Part 6 Evaluating Score Distribution Assumptions 121Chapter 18 Detecting Univariate Outliers 123Chapter 19 Detecting Multivariate Outliers 131Chapter 20 Assessing Distribution Shape: Normality, Skewness, and Kurtosis 139Chapter 21 Transforming Data to Remedy Statistical Assumption Violations 147Part 7 Bivariate Correlation 157Chapter 22 Pearson Correlation 159Chapter 23 Spearman Rho and Kendall Tau-b Rank-order Correlations 165Part 8 Regressing (predicting) Quantitative Variables 171Chapter 24 Simple Linear Regression 173Chapter 25 Centering the Predictor Variable in Simple Linear Regression 181Chapter 26 Multiple Linear Regression 191Chapter 27 Hierarchical Linear Regression 211Chapter 28 Polynomial Regression 217Chapter 29 Multilevel Modeling 225Part 9 Regressing (predicting) Categorical Variables 253Chapter 30 Binary Logistic Regression 255Chapter 31 Roc Analysis 265Chapter 32 Multinominal Logistic Regression 273Part 10 Survival Analysis 281Chapter 33 Survival Analysis: Life Tables 283Chapter 34 The Kaplan–Meier Survival Analysis 289Chapter 35 Cox Regression 301Part 11 Reliability as a Gauge of Measurement Quality 309Chapter 36 Reliability Analysis: Internal Consistency 311Chapter 37 Reliability Analysis: Assessing Rater Consistency 319Part 12 Analysis of Structure 329Chapter 38 Principal Components and Factor Analysis 331Chapter 39 Confirmatory Factor Analysis 353Part 13 Evaluating Causal (predictive) Models 379Chapter 40 Simple Mediation 381Chapter 41 Path Analysis Using Multiple Regression 389Chapter 42 Path Analysis Using Structural Equation Modeling 397Chapter 43 Structural Equation Modeling 419Part 14 t TEST 457Chapter 44 One-Sample t Test 459Chapter 45 Independent-Samples t Test 463Chapter 46 Paired-Samples t Test 471Part 15 Univariate Group Differences: Anova and Ancova 475Chapter 47 One-way Between-subjects Anova 477Chapter 48 Polynomial Trend Analysis 485Chapter 49 One-way Between-subjects Ancova 493Chapter 50 Two-way Between-subjects Anova 507Chapter 51 One-way Within-subjects Anova 521Chapter 52 Repeated Measures Using Linear Mixed Models 531Chapter 53 Two-way Mixed Anova 555Part 16 Multivariate Group Differences: Manova and Discriminant Function Analysis 567Chapter 54 One-way Between-subjects Manova 569Chapter 55 Discriminant Function Analysis 579Chapter 56 Two-way Between-subjects Manova 591Part 17 Multidimensional Scaling 603Chapter 57 Multidimensional Scaling: Classical Metric 605Chapter 58 Multidimensional Scaling: Metric Weighted 613Part 18 Cluster Analysis 621Chapter 59 Hierarchical Cluster Analysis 623Chapter 60 K-means Cluster Analysis 631Part 19 Nonparametric Procedures for Analyzing Frequency Data 643Chapter 61 Single-sample Binomial and Chi-square Tests: Binary Categories 645Chapter 62 Single-sample (one-way) Multinominal Chi-square Tests 655Chapter 63 Two-way Chi-square Test of Independence 665Chapter 64 Risk Analysis 675Chapter 65 Chi-square Layers 681Chapter 66 Hierarchical Loglinear Analysis 689Appendix Statistics Tables 699References 703Author Index 713Subject Index 715