Essentials of Behavioral and Social Science Statistics

Auteur

Dr. K. PAUL NESSELROADE Jr., a social psychologist, has been an educator for more than 25 years. During this time, he has taught a variety of psychology courses including numerous sections of Behavioral Statistics, Social Psychology, The History of Psychology, and The Psychology of the Holocaust. Dr. Nesselroade serves as Professor of Psychology, Psychology Department Chair, and Director of the Honors Program at Asbury University. THE LATE LAURENCE G. GRIMM, PhD, was a clinical psychologist and Emeritus Associate Professor, University of Illinois at Chicago.

Texte du rabat

Comprehensive resource on applying statistical analyses to behavioral and social science research situations, with new examples, methods, and support for computing in Excel and SPSS The Third Edition of Essentials of Behavioral and Social Science Statistics prompts the student to develop a deep understanding of the psychometric principles involved in the research process, as well as a mastery of the particular functionality of the most common statistical tools and an ability to properly select and use them in the real world; this goal is achieved thanks to the organization of the text, the philosophical content interspersed within it, the depth of the exercises and work problems, and the supporting materials provided for the instructor and student. The Third Edition has been thoroughly edited and streamlined to allow for students to move efficiently through the conceptual and mathematical fundamentals and on to the payoff formulas and descriptions of applications. New content includes philosophical issues associated with psychometrics and inferential statistical testing, interpretation, measurement, and the replication crisis in the social sciences. End-of-chapter exercises and work problems have been strengthened and reorganized to further improve comprehension and performance. Section reviews that draw on concepts from all preceding chapters are included to help students develop skills of statistical tool selection and application. Support for instructors includes chapter-based learning objectives, test banks, and PowerPoints. Essentials of Behavioral and Social Science Statistics includes information on:

• Basic concepts in research covering the scientific method, types of variables, controlling extraneous variables, validity issues, and causality and correlation
• Descriptive statistics including scales of measurement, measures of central tendency and variability, transformations, and standardized scores
• The fundamentals of inferential statistics, including probability theory, sampling distributions, the central limit theorem, and the terminology of hypothesis testing
• The logic and application of basic inferential tests including single-sample tests, independent-and dependent-samples t tests, and the basics of power analysis
• The logic and application of three common ANOVA analyses; one-way, two-way, and repeated-measures
• The logic and application of basic bivariate data analysis tools, linear correlation and linear regression
• The logic and application of chi-square analyses, both goodness-of-fit and tests-for-independence Written to facilitate concept mastery and enable practical application of concepts, Essentials of Behavioral and Social Science Statistics offers a survey of basic descriptive and inferential statistical tools and concepts and is highly suitable to support a rigorous undergraduate behavioral science or social science statistics course.

Contenu

Preface xv

Acknowledgments xix

About the Companion Website xxi

Introduction 1

1 Basic Concepts in Research 3

1.1 The Scientific Method 3

1.2 The Goals of the Researcher 5

1.3 Types of Variables 7

1.4 Controlling Extraneous Variables 9

1.5 Validity Issues 16

1.6 Causality and Correlation 21

1.7 The Organization of the Textbook 23

Summary 24

Exercises 25

Part 1 Descriptive Statistics 29

2 The Nature, Scales, and Display of Measurements 31

2.1 The Nature of Measurement 31

2.2 Scales of Measurement 33

2.3 Types of Variables and Their Features 37

2.4 Using Tables to Organize Data 40

2.5 Using Graphs to Display Data 45

2.6 The Shape of Things to Come 52

Summary 55

Microsoft® Excel and SPSS® 57

Exercises 57

Work Problems 58

3 Measures of Central Tendency 61

3.1 Describing a Distribution of Scores 61

3.2 Parameters and Statistics 62

3.3 The Rounding Rule 62

3.4 The Mean 63

3.5 The Median 66

3.6 The Mode 69

3.7 Distribution Shape and Measures of Central Tendency 70

3.8 When to Use the Mean, Median, and Mode 71

Summary 74

Microsoft® Excel and SPSS® 75

Exercises 76

Work Problems 76

4 Measures of Variability 79

4.1 The Importance of Measures of Variability 79

4.2 The Range 79

4.3 The Mean Deviation 82

4.4 The Variance 84

4.5 The Standard Deviation 89

4.6 Simple Transformations of the Mean and Variance 91

4.7 Deciding Which Measure of Variability to Use 92

Summary 95

Microsoft® Excel and SPSS® 96

Exercises 97

Work Problems 98

5 The Normal Curve and Transformations 101

5.1 Percentile Rank 101

5.2 Normal Distributions 102

5.3 Standard Scores (z Scores) 106

Summary 116

Microsoft® Excel and SPSS® 117

Exercises 118

Work Problems 118

Part 2 Inferential Statistics: Theoretical Basis 121

6 Basic Concepts of Probability 123

6.1 Theoretical Support for Inferential Statistics 123

6.2 The Taming of Chance 124

6.3 What Is Probability? 126

6.4 The Addition Rule 130

6.5 The Multiplication Rule 133

6.6 Conditional Probabilities 136

Summary 141

Exercises 142

Work Problems 143

7 Hypothesis Testing and Sampling Distributions 147

7.1 Inferential Statistics 147

7.2 Hypothesis Testing 148

7.3 Sampling Distributions 153

7.4 Estimating the Features of Sampling Distributions 158

Summary 160

Exercises 162

Work Problems 163

Part 3 Inferential Statistics: z Test, t Tests, and Power 165

8 The Single-Sample z and t Tests 167

8.1 The Research Context 167

8.2 The Sampling Distribution for the Single-Sample z Test 168

8.3 Type I and Type II Errors 175

8.4 Is a Significant Finding "Significant?" 180

8.5 The Sampling Distribution for the Single-Sample t Test 182

8.6 Assumptions of the Single-Sample z and t Tests 188

8.7 Interval Estimation of the Population Mean 189

8.8 Formal Presentation of Findings 191

Summary 191

Microsoft® Excel and SPSS® 192

Exercises 193

Work Problems 194

9 The Independent- and Dependent-Samples t Tests 199

9.1 The Research Context for Between-Participants Designs 199

9.2 The Independent-Samples t Test 201

9.3 Assumptions of the Independent-Samples t Test 210

9.4 Interval Estimation for Independent Samples 210

9.5 The Research Context for Within-Participants Designs 211

9.6 The Dependent-Samples t Test 213

9.7 Assumptions of the Dependent-Samples t Test 218

9.8 Interval Estimation for Dependent Samples 218

9.9 Comparing the Two Tests 219

9.10 The Appropriateness of Unidirectional Tests 220

9.11 Formal Presentation of Findings 225

Summary 225

Microsoft® Excel and SPSS® 226

Exercises 228

Work Problems 230

10 Power Analysis and Hypothesis Testing 239

10.1 Decision-Making While Hypothesis Testing 239

10.2 Why Study Power? 240

10.3 The Five Factors that Influence Power 241

10.4 Decision Criteria that Influence Power 243

10.5 Determining Effect Size: The Achilles Heel of Power Analyses 247

10.6 Determining Sample Size for a Single-Sample Test 248

Summary 249

Exercises 250

Work Problems 251

Part 3 Review The z Test, t Tests, and Power Analysis 253

Part 4 Inferential Statistics: Analyses of Variance 257

11 One-Way Analysis of Variance 259 …