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Basic Statistics for Social Research

ISBN: 978-1-118-23415-0
560 pages
November 2012, Jossey-Bass
Basic Statistics for Social Research (1118234154) cover image


A core statistics text that emphasizes logical inquiry, not math

Basic Statistics for Social Research teaches core general statistical concepts and methods that all social science majors must master to understand (and do) social research. Its use of mathematics and theory are deliberately limited, as the authors focus on the use of concepts and tools of statistics in the analysis of social science data, rather than on the mathematical and computational aspects. Research questions and applications are taken from a wide variety of subfields in sociology, and each chapter is organized around one or more general ideas that are explained at its beginning and then applied in increasing detail in the body of the text.

Each chapter contains instructive features to aid students in understanding and mastering the various statistical approaches presented in the book, including:

  • Learning objectives
  • Check quizzes after many sections and an answer key at the end of the chapter
  • Summary
  • Key terms
  • End-of-chapter exercises
  • SPSS exercises (in select chapters)

Ancillary materials for both the student and the instructor are available and include a test bank for instructors and downloadable video tutorials for students.

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Table of Contents

Tables and Figures ix

Preface xv

About the Authors xix


Chapter 1 Using Statistics 3

Why Study Statistics? 4

Tasks for Statistics: Describing, Inferring, Testing, Predicting 4

Statistics in the Research Process 9

Basic Elements of Research: Units of Analysis and Variables 14

Chapter 2 Displaying One Distribution 25

Summarizing Variation in One Variable 26

Frequency Distributions for Nominal Variables 26

Frequency Distributions for Ordinal Variables 32

Frequency Distributions for Interval/Ratio Variables 38

Summarizing Data Using Excel 43

Chapter 3 Central Tendency 81

The Basic Idea of Central Tendency 82

The Mode 83

The Median 88

The Mean 95

Chapter 4 Dispersion 113

The Basic Idea of Dispersion 114

Dispersion of Categorical Data 115

Dispersion of Interval/Ratio Data 121

Chapter 5 Describing the Shape of a Distribution 149

The Basic Ideas of Distributional Shape 150

The Shape of Nominal and Ordinal Distributions 152

Unimodality 158

Skewness 163

Kurtosis 169

Some Common Distributional Shapes 175

Chapter 6 The Normal Distribution 187

Introduction to the Normal Distribution 188

Properties of Normal Distributions 189

The Standard Normal, or Z, Distribution 192

Working with Standard Normal (Z) Scores 194

Finding Areas “Under the Curve” 197


Chapter 7 Basic Ideas of Statistical Inference 211

Introduction to Statistical Inference 212

Sampling Concepts 214

Central Tendency Estimates 219

Assessing Confidence in Point Estimates 229

Chapter 8 Hypothesis Testing for One Sample 247

Hypothesis Testing 248

The Testing Process 250

Tests about One Mean 258

Tests about One Proportion 267

Chapter 9 Hypothesis Testing for Two Samples 279

Comparing Two Groups 280

Comparing Two Groups’ Means 280

Comparing Two Groups’ Proportions 289

Nonindependent Samples 296

Using Excel for Two-Sample Tests 301

Interpreting Group Differences 302

Chapter 10 Multiple Sample Tests of Proportions: Chi-Squared 313

Comparing Proportions across Several Groups 314

Testing for Multiple Group Differences 315

Describing Group Differences 327

Chapter 11 Multiple Sample Tests for Means: One-Way ANOVA 337

Comparing Several Group Means with Analysis of Variance 338

Analyzing Variance and the F-Test 339

Analyzing Variance 342

The F-Test 350

Comparing Means 356


Chapter 12 Association with Categorical Variables 371

The Concept of Statistical Association 372

Association with Nominal Variables 375

Association with Ordinal Variables 391

Chapter 13 Association of Interval/Ratio Variables 425

Visualizing Interval/Ratio Association 426

Significance Testing for Interval/Ratio Association 434

Chapter 14 Regression Analysis 453

Predicting Outcomes with Regression 454

Simple Linear Regression 454

Applying Simple Regression Analysis 465

Multiple Regression 469

Applying Multiple Regression 474

Chapter 15 Logistic Regression Analysis 489

Predicting with Nonlinear Relationships 490

Logistic Regression 492

The Logistic Regression Model 492

Interpreting Effects in Logistic Regression 493

Estimating Logistic Regression Models with Maximum Likelihood 495

Applying Logistic Regression 496

Assessing Partial Effects 498

Extending Logistic Regression 499


Chi-Squared Distribution: Critical Values for Commonly Used Alpha50.05 and Alpha50.01 505

F-Distribution: Critical Values for Commonly Used Alpha50.05 and Alpha50.01 507

Standard Normal Scores (Z-Scores), and Cumulative Probabilities (Proportion of Cases Having Scores below Z) 511

Student’s t-Distribution: Critical Values for Commonly Used Alpha Levels 517

Index 519

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Author Information

ROBERT A. HANNEMAN is a professor of sociology at the University of California, Riverside.

AUGUSTINE J. KPOSOWA is a professor of sociology at the University of California, Riverside.

MARK D. RIDDLE is the Director of Institutional Research at Antioch University Los Angeles.

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