Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS(r) and Excel(r) applicationsThis book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field.The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material.Using Statistics in the Social and Health Sciences with SPSS(r) and Excel(r) includes:* Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings* Inclusion of a data lab section in each chapter that provides relevant, clear examples* Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needsEmphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS(r) and Excel(r) for analyzing data.Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel(r) and SPSS(r), The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.

Preface xvAcknowledgments xix1 INTRODUCTION 1Big Data Analysis, 1Visual Data Analysis, 2Importance of Statistics for the Social and Health Sciences and Medicine, 3Historical Notes: Early Use of Statistics, 4Approach of the Book, 6Cases from Current Research, 7Research Design, 9Focus on Interpretation, 92 DESCRIPTIVE STATISTICS: CENTRAL TENDENCY 13What is the Whole Truth? Research Applications (Spuriousness), 13Descriptive and Inferential Statistics, 16The Nature of Data: Scales of Measurement, 16Descriptive Statistics: Central Tendency, 23Using SPSS and Excel to Understand Central Tendency, 28Distributions, 35Describing the Normal Distribution: Numerical Methods, 37Descriptive Statistics: Using Graphical Methods, 41Terms and Concepts, 47Data Lab and Examples (with Solutions), 49Data Lab: Solutions, 513 DESCRIPTIVE STATISTICS: VARIABILITY 55Range, 55Percentile, 56Scores Based on Percentiles, 57Using SPSS and Excel to Identify Percentiles, 57Standard Deviation and Variance, 60Calculating the Variance and Standard Deviation, 61Population SD and Inferential SD, 66Obtaining SD from Excel and SPSS, 67Terms and Concepts, 70Data Lab and Examples (with Solutions), 71Data Lab: Solutions, 734 THE NORMAL DISTRIBUTION 77The Nature of the Normal Curve, 77The Standard Normal Score: Z Score, 79The Z Score Table of Values, 80Navigating the Z Score Distribution, 81Calculating Percentiles, 83Creating Rules for Locating Z Scores, 84Calculating Z Scores, 87Working with Raw Score Distributions, 90Using SPSS to Create Z Scores and Percentiles, 90Using Excel to Create Z Scores, 94Using Excel and SPSS for Distribution Descriptions, 97Terms and Concepts, 99Data Lab and Examples (with Solutions), 99Data Lab: Solutions, 1015 PROBABILITY AND THE Z DISTRIBUTION 105The Nature of Probability, 106Elements of Probability, 106Combinations and Permutations, 109Conditional Probability: Using Bayes' Theorem, 111Z Score Distribution and Probability, 112Using SPSS and Excel to Transform Scores, 117Using the Attributes of the Normal Curve to Calculate Probability, 119"Exact" Probability, 123From Sample Values to Sample Distributions, 126Terms and Concepts, 127Data Lab and Examples (with Solutions), 128Data Lab: Solutions, 1296 RESEARCH DESIGN AND INFERENTIAL STATISTICS 133Research Design, 133Experiment, 136Non-Experimental or Post Facto Research Designs, 140Inferential Statistics, 143Z Test, 154The Hypothesis Test, 154Statistical Significance, 156Practical Significance: Effect Size, 156Z Test Elements, 156Using SPSS and Excel for the Z Test, 157Terms and Concepts, 158Data Lab and Examples (with Solutions), 161Data Lab: Solutions, 1627 THET TEST FOR SINGLE SAMPLES 165Introduction, 166Z Versus T: Making Accommodations, 166Research Design, 167Parameter Estimation, 169The T Test, 173The T Test: A Research Example, 176Interpreting the Results of the T Test for a Single Mean, 180The T Distribution, 181The Hypothesis Test for the Single Sample T Test, 182Type I and Type II Errors, 183Effect Size, 187Effect Size for the Single Sample T Test, 187Power, Effect Size, and Beta, 188One- and Two-Tailed Tests, 189Point and Interval Estimates, 192Using SPSS and Excel with the Single Sample T Test, 196Terms and Concepts, 201Data Lab and Examples (with Solutions), 201Data Lab: Solutions, 2038 INDEPENDENT SAMPLE T TEST 207A Lot of "Ts", 207Research Design, 208Experimental Designs and the Independent T Test, 208Dependent Sample Designs, 209Between and Within Research Designs, 210Using Different T Tests, 211Independent T Test: The Procedure, 213Creating the Sampling Distribution of Differences, 215The Nature of the Sampling Distribution of Differences, 216Calculating the Estimated Standard Error of Difference with Equal Sample Size, 218Using Unequal Sample Sizes, 219The Independent T Ratio, 221Independent T Test Example, 222Hypothesis Test Elements for the Example, 222Before-After Convention with the Independent T Test, 226Confidence Intervals for the Independent T Test, 227Effect Size, 228The Assumptions for the Independent T Test, 230SPSS Explore for Checking the Normal Distribution Assumption, 231Excel Procedures for Checking the Equal Variance Assumption, 233SPSS Procedure for Checking the Equal Variance Assumption, 237Using SPSS and Excel with the Independent T Test, 239SPSS Procedures for the Independent T Test, 239Excel Procedures for the Independent T Test, 243Effect Size for the Independent T Test Example, 245Parting Comments, 245Nonparametric Statistics: The Mann-Whitney U Test, 246Terms and Concepts, 249Data Lab and Examples (with Solutions), 249Data Lab: Solutions, 251Graphics in the Data Summary, 2549 ANALYSIS OF VARIANCE 255A Hypothetical Example of ANOVA, 255The Nature of ANOVA, 257The Components of Variance, 258The Process of ANOVA, 259Calculating ANOVA, 260Effect Size, 268Post Hoc Analyses, 269Assumptions of ANOVA, 274Additional Considerations with ANOVA, 275The Hypothesis Test: Interpreting ANOVA Results, 276Are the Assumptions Met?, 276Using SPSS and Excel with One-Way ANOVA, 282The Need for Diagnostics, 289Non-Parametric ANOVA Tests: The Kruskal-Wallis Test, 289Terms and Concepts, 292Data Lab and Examples (with Solutions), 293Data Lab: Solutions, 29410 FACTORIAL ANOVA 297Extensions of ANOVA, 297ANCOVA, 298MANOVA, 299MANCOVA, 299Factorial ANOVA, 299Interaction Effects, 299Simple Effects, 3012XANOVA: An Example, 302Calculating Factorial ANOVA, 303The Hypotheses Test: Interpreting Factorial ANOVA Results, 306Effect Size for 2XANOVA: Partial eta 2, 308Discussing the Results, 309Using SPSS to Analyze 2XANOVA, 311Summary Chart for 2XANOVA Procedures, 319Terms and Concepts, 319Data Lab and Examples (with Solutions), 320Data Lab: Solutions, 32011 CORRELATION 329The Nature of Correlation, 330The Correlation Design, 331Pearson's Correlation Coefficient, 332Plotting the Correlation: The Scattergram, 334Using SPSS to Create Scattergrams, 337Using Excel to Create Scattergrams, 339Calculating Pearson's r, 341The Z Score Method, 342The Computation Method, 344The Hypothesis Test for Pearson's r, 345Effect Size: the Coefficient of Determination, 347Diagnostics: Correlation Problems, 349Correlation Using SPSS and Excel, 352Nonparametric Statistics: Spearman's Rank Order Correlation (rs), 358Terms and Concepts, 363Data Lab and Examples (with Solutions), 364Data Lab: Solutions, 36512 BIVARIATE REGRESSION 371The Nature of Regression, 372The Regression Line, 374Calculating Regression, 376Effect Size of Regression, 379The Z Score Formula for Regression, 380Testing the Regression Hypotheses, 382The Standard Error of Estimate, 383Confidence Interval, 385Explaining Variance Through Regression, 386A Numerical Example of Partitioning the Variation, 389Using Excel and SPSS with Bivariate Regression, 390The SPSS Regression Output, 390The Excel Regression Output, 396Complete Example of Bivariate Linear Regression, 398Assumptions of Bivariate Regression, 398The Omnibus Test Results, 404Effect Size, 404The Model Summary, 405The Regression Equation and Individual Predictor Test of Significance, 405Advanced Regression Procedures, 406Detecting Problems in Bivariate Linear Regression, 408Terms and Concepts, 409Data Lab and Examples (with Solutions), 410Data Lab: Solutions, 41113 INTRODUCTION TO MULTIPLE LINEAR REGRESSION 417The Elements of Multiple Linear Regression, 417Same Process as Bivariate Regression, 418Some Differences between Bivariate Linear Regression and Multiple Linear Regression, 419Stuff not Covered, 420Assumptions of Multiple Linear Regression, 421Analyzing Residuals to Check MLR Assumptions, 422Diagnostics for MLR: Cleaning and Checking Data, 423Extreme Scores, 424Distance Statistics, 428Influence Statistics, 429MLR Extended Example Data, 430Assumptions Met?, 431Analyzing Residuals: Are Assumptions Met?, 433Interpreting the SPSS Findings for MLR, 436Entering Predictors Together as a Block, 437Entering Predictors Separately, 442Additional Entry Methods for MLR Analyses, 447Example Study Conclusion, 448Terms and Concepts, 448Data Lab and Example (with Solution), 450Data Lab: Solution, 45014 CHI-SQUARE AND CONTINGENCY TABLE ANALYSIS 455Contingency Tables, 455The Chi-square Procedure and Research Design, 456Chi-square Design One: Goodness of Fit, 457A Hypothetical Example: Goodness of Fit, 458Effect Size: Goodness of Fit, 462Chi-square Design Two: The Test of Independence, 463A Hypothetical Example: Test of Independence, 464Special 2 × 2 Chi-square, 468Effect Size in 2 × 2 Tables: PHI, 470Cramer's V: Effect Size for the Chi-square Test of Independence, 471Repeated Measures Chi-square: Mcnemar Test, 472Using SPSS and Excel with Chi-square, 474Using SPSS for the Chi-square Test of Independence, 475Using Excel for Chi-square Analyses, 481Terms and Concepts, 483Data Lab and Examples (with Solutions), 483Data Lab: Solutions, 48415 REPEATED MEASURES PROCEDURES: Tdep AND ANOVAWS 489Independent and Dependent Samples in Research Designs, 490Using Different T Tests, 491The Dependent T Test Calculation: The "Long" Formula, 491Example: The Long Formula, 492The Dependent T Test Calculation: The "Difference" Formula, 494Tdep and Power, 496Conducting The Tdep Analysis Using SPSS, 496Conducting The Tdep Analysis Using Excel, 498Within-Subject ANOVA (ANOVAWS), 498Experimental Designs, 499Post Facto Designs, 500Within-Subject Example, 501Using SPSS for Within-Subject Data, 501The SPSS Procedure, 502The SPSS Output, 504Nonparametric Statistics, 508Terms and Concepts, 508APPENDICESAppendix A SPSS BASICS 509Using SPSS, 509General Features, 510Management Functions, 513Additional Management Functions, 517Appendix B EXCEL BASICS 531Data Management, 531The Excel Menus, 533Using Statistical Functions, 541Data Analysis Procedures, 543Missing Values and "0" Values in Excel Analyses, 544Using Excel with "Real Data", 544Appendix C STATISTICAL TABLES 545Table C.1: Z-Score Table (Values Shown are Percentages - %), 545Table C.2: Exclusion Values for the T-Distribution, 547Table C.3: Critical (Exclusion) Values for the Distribution of F, 548Table C.4: Tukey's Range Test (Upper 5% Points), 551Table C.5: Critical (Exclusion) Values for Pearson's Correlation Coefficient, r, 552Table C.6: Critical Values of the chi2 (Chi-Square) Distribution, 553REFERENCES 555Index 557