Contents:

Summary: Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the conceptual underpinnings of statistical analyses used in behavioral science literature. The authors review statistics in a way that is intended to reduce anxiety for students who feel intimidated by statistics. Conceptual underpinnings and practical applications are stressed, whereas algebraic derivations and complex formulas are reduced. New ideas are presented in the context of a few recurring examples, which allows readers to focus more on the new statistical concepts than on the details of different studies.The authors' selection and organization of topics is slightly different from the ordinary introductory textbook. It is motivated by the needs of a behavioral science student, or someone in clinical practice, rather than by formal, mathematical properties. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with statistical designs and tests to answer research questions. In addition, this book treats analysis of variance as another application of multiple regression. With this integrated, unified approach, students simultaneously learn about multiple regression and how to analyze data associated with basic analysis of variance and covariance designs. Students confront fewer topics but those they do encounter possess considerable more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion.Understanding Statistics in the Behavioral Sciences features:*Computer-based exercises, many of which rely on spreadsheets, help the reader perform statistical analyses and compare and verify the results using either SPSS or SAS. These exercises also provide an opportunity to exploreSummary: definitional formulas by altering raw data or terms within a formula and immediately see the consequences thus providing a deeper understanding of the basic concepts.*Key terms and symbols are boxed when first introduced and repeated in a glossary to make them easier to find at review time.*Numerous tables and graphs, including spreadsheet printouts and figures, help students visualize the most critical concepts.This book is intended as a text for introductory behavioral science statistics. It will appeal to instructors who want a relatively brief text. The book's active approach to learning, works well both in the classroom and for individual self-study.
Cover -- Half Title -- Title -- Copyright -- Original Title -- Contents -- Preface -- 1 Preliminaries: How to Use This Book -- 1.1 Statistics and the Behavioral Sciences -- 1.2 Computing Statistics by Hand and Computer -- 1.3 An Integrated Approach to Learning Statistics -- 2 Getting Started: The Logic of Hypothesis Testing -- 2.1 Statistics, Samples, and Populations -- 2.2 Hypothesis Testing: An Introduction -- 2.3 False Claims, Real Effects, and Power -- 2.4 Why Discuss Inferential Before Descriptive Statistics? -- 3 Inferring From a Sample: The Binomial Distribution -- 3.1 The Binomial Distribution -- 3.2 The Sign Test -- 4 Measuring Variables: Some Basic Vocabulary -- 4.1 Scales of Measurement -- 4.2 Designing a Study: Independent and Dependent Variables -- 4.3 Matching Study Designs With Statistical Procedures -- 5 Describing a Sample: Basic Descriptive Statistics -- 5.1 The Mean -- 5.2 The Variance -- 5.3 The Standard Deviation -- 5.4 Standard Scores -- 6 Describing a Sample: Graphical Techniques -- 6.1 Principles of good design -- 6.2 Graphical Techniques Explained -- 7 Inferring From a Sample: The Normal and t Distributions -- 7.1 The Normal Approximation for the Binomial -- 7.2 The Normal Distribution -- 7.3 The Central Limit Theorem -- 7.4 The t Distribution -- 7.5 Single-Sample Tests -- 7.6 Ninety-Five Percent Confidence Intervals -- 8 Accounting for Variance: A Single Predictor -- 8.1 Simple Regression and Correlation -- 8.2 What Accounting for Variance Means -- 9 Bivariate Relations: The Regression and Correlation Coefficients -- 9.1 Computing the Slope and the Y Intercept -- 9.2 Computing the Correlation Coefficient -- 9.3 Detecting Group Differences with a Binary Predictor -- 9.4 Graphing the Regression Line -- 10 Inferring From a Sample: The F Distribution -- 10.1 Estimating Population Variance -- 10.2 The F Distribution.

10.3 The F Test -- 10.4 The Analysis of Variance: Two Independent Groups -- 10.5 Assumptions of the F test -- 11 Accounting for Variance: Multiple Predictors -- 11.1 Multiple Regression and Correlation -- 11.2 Significance Testing With Multiple Predictors -- 11.3 Accounting For Unique Additional Variance -- 11.4 Hierarchic MRC and the Analysis of Covariance -- 11.5 More Than Two Predictors -- 12 Single-Factor Between-Subjects Studies -- 12.1 Coding Categorical Predictor Variables -- 12.2 One-Way Analysis of Variance -- 12.3 Trend Analysis -- 13 Planned Comparisons, Post Hoc Tests, and Adjusted Means -- 13.1 Organizing Stepwise Statistics -- 13.2 Planned Comparisons -- 13.3 Post Hoc Tests -- 13.4 Unequal Numbers of Subjects Per Group -- 13.5 Adjusted Means and the Analysis of Covariance -- 14 Studies With Multiple Between-Subjects Factors -- 14.1 Between-Subjects Factorial Studies -- 14.2 Significance Testing for Main Effects And Interactions -- 14.3 Interpreting Significant Main Effects and Interactions -- 14.4 Magnitude of Effects and Partial Eta Squared -- 15 Single-Factor Within-Subjects Studies -- 15.1 Within-Subjects or Repeated-Measures Factors -- 15.2 Controlling Between-Subjects Variability -- 15.3 Modifying the Source Table for Repeated Measures -- 15.4 Assumptions of the Repeated Measure ANOVA -- 16 Two-Factor Studies With Repeated Measures -- 16.1 One Between- and One Within-Subjects Factor -- 16.2 Two Within-Subjects Factors -- 16.3 Explicating Interactions With Repeated Measures -- 16.4 Generalizing to More Complex Designs -- 17 Power, Pitfalls, and Practical Matters -- 17.1 Pretest, Posttest: Repeated Measure Or Covariate? -- 17.2 Power Analysis: How Many Subjects Are Enough? -- References -- Glossary of Symbols and Key Terms -- Appendix A: SAS exercises -- Appendix B: Answers To Selected Exercises -- Appendix C: Statistical Tables.

A. Critical Values for the Binomial Distribution, P = 0.5 -- B. Areas Under the Normal Curve -- C. Critical Values for the t Distribution -- D.1 Critical Values for the F Distribution, α = .05 -- D.2 Critical Values for the F Distribution, α = .01 -- E.1 Distribution of the Studentized Range Statistic, α = .05 -- E.2 Distribution of the Studentized Range Statistic, α = .01 -- F.1 L Values for α = .05 -- F.2 L Values for α = .01 -- Author Index -- Subject Index.

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Cover -- Half Title -- Title -- Copyright -- Original Title -- Contents -- Preface -- 1 Preliminaries: How to Use This Book -- 1.1 Statistics and the Behavioral Sciences -- 1.2 Computing Statistics by Hand and Computer -- 1.3 An Integrated Approach to Learning Statistics -- 2 Getting Started: The Logic of Hypothesis Testing -- 2.1 Statistics, Samples, and Populations -- 2.2 Hypothesis Testing: An Introduction -- 2.3 False Claims, Real Effects, and Power -- 2.4 Why Discuss Inferential Before Descriptive Statistics? -- 3 Inferring From a Sample: The Binomial Distribution -- 3.1 The Binomial Distribution -- 3.2 The Sign Test -- 4 Measuring Variables: Some Basic Vocabulary -- 4.1 Scales of Measurement -- 4.2 Designing a Study: Independent and Dependent Variables -- 4.3 Matching Study Designs With Statistical Procedures -- 5 Describing a Sample: Basic Descriptive Statistics -- 5.1 The Mean -- 5.2 The Variance -- 5.3 The Standard Deviation -- 5.4 Standard Scores -- 6 Describing a Sample: Graphical Techniques -- 6.1 Principles of good design -- 6.2 Graphical Techniques Explained -- 7 Inferring From a Sample: The Normal and t Distributions -- 7.1 The Normal Approximation for the Binomial -- 7.2 The Normal Distribution -- 7.3 The Central Limit Theorem -- 7.4 The t Distribution -- 7.5 Single-Sample Tests -- 7.6 Ninety-Five Percent Confidence Intervals -- 8 Accounting for Variance: A Single Predictor -- 8.1 Simple Regression and Correlation -- 8.2 What Accounting for Variance Means -- 9 Bivariate Relations: The Regression and Correlation Coefficients -- 9.1 Computing the Slope and the Y Intercept -- 9.2 Computing the Correlation Coefficient -- 9.3 Detecting Group Differences with a Binary Predictor -- 9.4 Graphing the Regression Line -- 10 Inferring From a Sample: The F Distribution -- 10.1 Estimating Population Variance -- 10.2 The F Distribution.

10.3 The F Test -- 10.4 The Analysis of Variance: Two Independent Groups -- 10.5 Assumptions of the F test -- 11 Accounting for Variance: Multiple Predictors -- 11.1 Multiple Regression and Correlation -- 11.2 Significance Testing With Multiple Predictors -- 11.3 Accounting For Unique Additional Variance -- 11.4 Hierarchic MRC and the Analysis of Covariance -- 11.5 More Than Two Predictors -- 12 Single-Factor Between-Subjects Studies -- 12.1 Coding Categorical Predictor Variables -- 12.2 One-Way Analysis of Variance -- 12.3 Trend Analysis -- 13 Planned Comparisons, Post Hoc Tests, and Adjusted Means -- 13.1 Organizing Stepwise Statistics -- 13.2 Planned Comparisons -- 13.3 Post Hoc Tests -- 13.4 Unequal Numbers of Subjects Per Group -- 13.5 Adjusted Means and the Analysis of Covariance -- 14 Studies With Multiple Between-Subjects Factors -- 14.1 Between-Subjects Factorial Studies -- 14.2 Significance Testing for Main Effects And Interactions -- 14.3 Interpreting Significant Main Effects and Interactions -- 14.4 Magnitude of Effects and Partial Eta Squared -- 15 Single-Factor Within-Subjects Studies -- 15.1 Within-Subjects or Repeated-Measures Factors -- 15.2 Controlling Between-Subjects Variability -- 15.3 Modifying the Source Table for Repeated Measures -- 15.4 Assumptions of the Repeated Measure ANOVA -- 16 Two-Factor Studies With Repeated Measures -- 16.1 One Between- and One Within-Subjects Factor -- 16.2 Two Within-Subjects Factors -- 16.3 Explicating Interactions With Repeated Measures -- 16.4 Generalizing to More Complex Designs -- 17 Power, Pitfalls, and Practical Matters -- 17.1 Pretest, Posttest: Repeated Measure Or Covariate? -- 17.2 Power Analysis: How Many Subjects Are Enough? -- References -- Glossary of Symbols and Key Terms -- Appendix A: SAS exercises -- Appendix B: Answers To Selected Exercises -- Appendix C: Statistical Tables.

A. Critical Values for the Binomial Distribution, P = 0.5 -- B. Areas Under the Normal Curve -- C. Critical Values for the t Distribution -- D.1 Critical Values for the F Distribution, α = .05 -- D.2 Critical Values for the F Distribution, α = .01 -- E.1 Distribution of the Studentized Range Statistic, α = .05 -- E.2 Distribution of the Studentized Range Statistic, α = .01 -- F.1 L Values for α = .05 -- F.2 L Values for α = .01 -- Author Index -- Subject Index.

Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the conceptual underpinnings of statistical analyses used in behavioral science literature. The authors review statistics in a way that is intended to reduce anxiety for students who feel intimidated by statistics. Conceptual underpinnings and practical applications are stressed, whereas algebraic derivations and complex formulas are reduced. New ideas are presented in the context of a few recurring examples, which allows readers to focus more on the new statistical concepts than on the details of different studies.The authors' selection and organization of topics is slightly different from the ordinary introductory textbook. It is motivated by the needs of a behavioral science student, or someone in clinical practice, rather than by formal, mathematical properties. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with statistical designs and tests to answer research questions. In addition, this book treats analysis of variance as another application of multiple regression. With this integrated, unified approach, students simultaneously learn about multiple regression and how to analyze data associated with basic analysis of variance and covariance designs. Students confront fewer topics but those they do encounter possess considerable more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion.Understanding Statistics in the Behavioral Sciences features:*Computer-based exercises, many of which rely on spreadsheets, help the reader perform statistical analyses and compare and verify the results using either SPSS or SAS. These exercises also provide an opportunity to explore

definitional formulas by altering raw data or terms within a formula and immediately see the consequences thus providing a deeper understanding of the basic concepts.*Key terms and symbols are boxed when first introduced and repeated in a glossary to make them easier to find at review time.*Numerous tables and graphs, including spreadsheet printouts and figures, help students visualize the most critical concepts.This book is intended as a text for introductory behavioral science statistics. It will appeal to instructors who want a relatively brief text. The book's active approach to learning, works well both in the classroom and for individual self-study.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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