Understanding Statistics in the Behavioral Sciences

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 straightforward wa...

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Bibliographic Details
Main Authors Bakeman, Roger, Robinson, Byron F.
Format eBook Book
LanguageEnglish
Published Mahwah, N.J Lawrence Erlbaum Associates, Inc 2005
Routledge
Taylor and Francis
Lawrence Erlbaum Associates
Taylor & Francis Group
Psychology Press
Edition1
Subjects
Online AccessGet full text
ISBN9780805849448
0805849440
113801284X
9781138012844
DOI10.4324/9781410612625

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Abstract 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 straightforward 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 concrete, recurring examples throughout, which allow the 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 the formal, mathematical properties of statistical theory. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with various statistical designs and tests to answer research questions. This contrasts with the order found in most statistics texts, which begin with descriptive statistics, probability, and other topics before explaining hypothesis testing. 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 considerably more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion, such as degrees of freedom, repeated measures designs, and the analysis of covariance. Understanding Statistics in the Behavioral Sciences features helpful tools to aid learning: 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 an end-of-text 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 basic or supplemental text in an introductory course in behavioral science statistics. It is expected to appeal to instructors who want a relatively brief text that students can master in one semester. The book's active approach to learning statistics works well both in the classroom and for individual self-study. Understanding Statistics in the Behavioral Sciences reflects the comments of the students at Georgia State University who used and tested it over several semesters.
AbstractList This book is designed to develop your conceptual and practical understanding of basic data analysis. The primary audience is beginning graduate students.
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. Contents: Preface. Preliminaries: How to Use This Book. Getting Started: The Logic of Hypothesis Testing. Inferring From a Sample: The Binomial Distribution. Measuring Variables: Some Basic Vocabulary. Describing a Sample: Basic Descriptive Statistics. Describing a Sample: Graphical Techniques. Inferring From a Sample: The Normal and t Distributions. Accounting for Variance: A Single Predictor. Bivariate Relations: The Regression and Correlation Coefficients. Inferring From a Sample: The F Distribution. Accounting for Variance: Multiple Predictors. Single-Factor Between-Subjects Studies. Planned Comparisons, Post Hoc Tests, and Adjusted Means. Studies With Multiple Between-Subjects Factor. Single-Factor Within-Subjects Studies. Two-Factor Studies With Repeated Measures. Power, Pitfalls, and Practical Matters.
Understanding Statistics in the Behavioral Sciencesis 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 Sciencesfeatures:*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 symbolsare 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.
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 straightforward 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 concrete, recurring examples throughout, which allow the 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 the formal, mathematical properties of statistical theory. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with various statistical designs and tests to answer research questions. This contrasts with the order found in most statistics texts, which begin with descriptive statistics, probability, and other topics before explaining hypothesis testing. 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 considerably more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion, such as degrees of freedom, repeated measures designs, and the analysis of covariance. Understanding Statistics in the Behavioral Sciences features helpful tools to aid learning: 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 an end-of-text 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 basic or supplemental text in an introductory course in behavioral science statistics. It is expected to appeal to instructors who want a relatively brief text that students can master in one semester. The book's active approach to learning statistics works well both in the classroom and for individual self-study. Understanding Statistics in the Behavioral Sciences reflects the comments of the students at Georgia State University who used and tested it over several semesters.
Author Bakeman, Roger
Robinson, Byron F.
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Notes Includes bibliographical references (p. 301-302) and indexes
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Snippet Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the...
This book is designed to develop your conceptual and practical understanding of basic data analysis. The primary audience is beginning graduate students.
Understanding Statistics in the Behavioral Sciencesis designed to help readers understand research reports, analyze data, and familiarize themselves with the...
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SubjectTerms Introductory & Intermediate Statistics
Psychology
Psychology -- Statistical methods -- Textbooks
Psychometrics
Psychometrics -- Textbooks
Social sciences
Social sciences -- Statistical methods -- Textbooks
Statistical methods
Textbooks
TableOfContents 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
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgements -- 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
Cover -- Title -- Copyright -- Contents -- Preface -- 1 Preliminaries: How to Use This Book -- 2 Getting Started: The Logic of Hypothesis Testing -- 3 Inferring From a Sample: The Binomial Distribution -- 4 Measuring Variables: Some Basic Vocabulary -- 5 Describing a Sample: Basic Descriptive Statistics -- 6 Describing a Sample: Graphical Techniques -- 7 Inferring From a Sample: The Normal and t Distributions -- 8 Accounting for Variance: A Single Predictor -- 9 Bivariate Relations: The Regression and Correlation Coefficients -- 10 Inferring From a Sample: The F Distribution -- 11 Accounting for Variance: Multiple Predictors -- 12 Single-Factor Between-Subjects Studies -- 13 Planned Comparisons, Post Hoc Tests, and Adjusted Means -- 14 Studies With Multiple Between-Subjects Factors -- 15 Single-Factor Within-Subjects Studies -- 16 Two-Factor Studies With Repeated Measures -- 17 Power, Pitfalls, and Practical Matters -- References -- Glossary of Symbols and Key Terms -- Appendix A: SAS Exercises -- Appendix B: Answers to Selected Exercises -- Appendix C: Statistical Tables -- Author Index -- Subject Index
Title Understanding Statistics in the Behavioral Sciences
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