Understanding Statistics in the Behavioral Sciences 1st Edition by Roger Bakeman, Byron F Robinson ISBN 0805849440 9780805849448

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ISBN 10: 0805849440 
ISBN 13: 9780805849448
Author: Roger Bakeman, Byron F Robinson

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.

Understanding Statistics in the Behavioral Sciences 1st Table of contents:

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

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Tags: Roger Bakeman, Byron F Robinson, Statistics, Behavioral