Essentials of Behavioral and Social Science Statistics

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.

Table of Contents:
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 11.1 The Research Context 259 11.2 Hypotheses 261 11.3 The Conceptual Basis: Sources of Variation 262 11.4 The Assumptions 265 11.5 Computing the F Ratio 265 11.6 Testing Null Hypotheses 271 11.7 The ANOVA Summary Table 274 11.8 Measuring Effect Size 274 11.9 Locating the Source(s) of Significance 275 11.10 Formal Presentation of Findings 279 Summary 279 Microsoft® Excel and SPSS® 280 Exercises 281 Work Problems 283 12 Two-Way Analysis of Variance 289 12.1 The Research Context 289 12.2 The Logic of the Two-Way ANOVA 300 12.3 Definitional and Computational Formulas 303 12.4 The ANOVA Summary Table 306 12.5 Using the F Ratios to Test Null Hypotheses 307 12.6 The Assumptions 308 12.7 Measuring Effect Sizes 308 12.8 Multiple Comparisons 309 12.9 Interpreting the Factors in a Two-Way ANOVA 314 12.10 Formal Presentation of Findings 315 Summary 315 Microsoft® Excel and SPSS® 316 Exercises 317 Work Problems 320 13 Repeated-Measures Analysis of Variance 325 13.1 The Research Context 325 13.2 The Logic of the Repeated-Measures ANOVA 328 13.3 The Formulas 331 13.4 The ANOVA Summary Table 334 13.5 Using the F Ratio to Test the Null Hypothesis 335 13.6 Interpreting the Findings 335 13.7 The Assumptions 335 13.8 Measuring Effect Size 336 13.9 Locating the Source(s) of Statistical Evidence 337 13.10 Formal Presentation of Findings 338 Summary 339 Microsoft® Excel and SPSS® 339 Exercises 340 Work Problems 341 Part 4 Review Analyses of Variance 347 Part 5 Inferential Statistics: Bivariate Data and Chi-Square Tests 351 14 Linear Correlation 353 14.1 The Research Context 353 14.2 The Correlation Coefficient and Scatter Diagrams 356 14.3 The Coefficient of Determination, r2 362 14.4 Using the Pearson r for Hypothesis Testing 365 14.5 Misleading Correlation Coefficients 368 14.6 Formal Presentation of Findings 372 Summary 372 Microsoft® Excel and SPSS® 373 Exercises 374 Work Problems 376 15 Linear Regression 381 15.1 The Research Context 381 15.2 Overview of Regression 382 15.3 Establishing the Regression Line 386 15.4 Putting It All Together: A Worked Problem 396 15.5 The Pitfalls of Linear Regression 398 15.6 Formal Presentation of Findings 401 Summary 401 Microsoft® Excel and SPSS® 402 Exercises 403 Work Problems 404 16 Chi-Square Tests and Other Nonparametrics 409 16.1 The Research Context 409 16.2 The Goodness-of-Fit Chi-Square Test 410 16.3 The Chi-Square Sampling Distribution 416 16.4 The Chi-Square Test for Independence 418 16.5 The Chi-Square Test for a 2 × 2 Contingency Table 422 16.6 A Measure of Effect Size for Chi-Square Tests 423 16.7 Major Contributors to a Significant Chi-Square 424 16.8 Using the Chi-Square Test with Quantitative Variables 425 16.9 The Assumptions 426 16.10 Formal Presentation of Findings 426 16.11 Other Nonparametric Tests 426 Summary 429 Microsoft® Excel and SPSS® 430 Exercises 431 Work Problems 432 Part 5 Review Bivariate Data and Chi-Square Tests 437 Appendix A Statistical Tables 433 Appendix B Answers to Exercises and Work Problems 461 Appendix c Instructions for Microsoft® Excel and SPSS® 533 References 565 Glossary 573 List of Selected Formulas 583 List of Symbols 589 Index 593