Statistics for Social Understanding: A Problems-Based Approach

Statistics for Social Understanding introduces statistics as it’s used in the social sciences—as a tool for advancing understanding of the social world. The authors provide thorough coverage of social science statistical topics, a balanced approach to calculation, and step-by-step directions on how to use both SPSS and Stata software, giving students the ability to analyze data and explore exciting questions. “In Depth” boxes encourage critical thinking by tackling tricky statistical queries, and each chapter concludes with a chapter summary, a section on using Stata, a section on using SPSS, and practice problems. All problems have been accuracy-checked by an outside panel of reviewers. Readily available datasets for classroom use include material from institutions such as the American National Election Study, General Social Survey, World Values Survey, and the School Survey on Crime and Safety. Statistics for Social Understanding is accompanied by a learning package, written entirely by author Tina Wildhagen, that is designed to enhance the experience of both instructors and students.

Table of Contents:
Chapter 1 Introduction  Why Study Statistics?  Research Questions and the Research Process  Pinning Things Down: Variables and Measurement  Units of Analysis  Measurement Error: Validity and Reliability  Levels of Measurement  Causation: Independent and Dependent Variables  Getting the Data: Sampling and Generalizing Sampling Methods  Sources of Secondary Data: Existing Data Sets, Reports, and “Big Data”  Big Data Growth Mindset and Math Anxiety  Using This Book Statistical Software Chapter Summary Using Stata Using SPSS Practice Problems Chapter 2 Getting to Know Your Data Frequency Distributions Percentages and Proportions Cumulative Percentage and Percentile Percent Change Rates and Ratios Rates Ratios Working with Frequency Distribution Tables Missing Values Simplifying Tables by Collapsing Categories Graphical Displays of a Single Variable: Bar Graphs, Pie Charts, Histograms, Stem-and-Leaf Plots, and Frequency Polygons Bar Graphs and Pie Charts  Histograms Stem-and-Leaf-Plots Frequency Polygons Time Series Charts Comparing Two Groups On the Same Variable Using Tables, Graphs, and Charts Chapter Summary Using Stata Using SPSS Practice Problems Chapter 3 Examining Relationships between Two Variables Cross-Tabulations and Relationships between Variables Independent and Dependent Variables Column, Row, and Total Percentages Interpreting the Strength of Relationships Interpreting the Direction of Relationships Graphical Representations of Bivariate Relationships Chapter Summary Using Stata Using SPSS Practice Problems Chapter 4 Typical Values in a Group What Does It Mean to Describe What Is Typical? Mean Median Mode Finding the Mode, Median, and Mean in Frequency Distributions Choosing the Appropriate Measure of Central Tendency Median Versus Mean Income Chapter Summary Using Stata Using SPSS Practice Problems Chapter 5 The Diversity of Values in a Group Range Interquartile Range Standard Deviation Using the Standard Deviation to Compare Distributions Comparing Apples and Oranges Skewed Versus Symmetric Distributions Chapter Summary Using Stata Using SPSS Practice Problems Chapter 6 Probability and the Normal Distribution The Rules of Probability The Addition Rule The Complement Rule The Multiplication Rule with Independence The Multiplication Rule without Independence Applying the Multiplication Rule with Independence to the “Linda” and “Birth-Order” Probability Problems The Normal Distribution Standardizing Variables and Calculating Z-Scores Chapter Summary Using Stata Using SPSS Practice Problems Chapter 7 From Sample to Population Repeated Sampling, Sample Statistics, and the Population Parameter Sampling Distributions Finding the Probability of Obtaining A Specific Sample Statistic Estimating the Standard Error from a Known Population Standard Deviation Finding and Interpreting the Z-Score for Sample Means Finding and Interpreting the Z-Score for Sample Proportions The Impact of Sample Size On the Standard Error Chapter Summary Using Stata Using SPSS Practice Problems Chapter 8 Estimating Population Parameters Inferential Statistics and the Estimation of Population Parameters Confidence Intervals Manage Uncertainty Through Margins of Error Certainty and Precision of Confidence Intervals Confidence Intervals for Proportions Constructing a Confidence Interval for Proportions: Examples Confidence Intervals for Means The t-Distribution Calculating Confidence Intervals for Means: Examples The Relationship Between Sample Size and Confidence Interval Range The Relationship Between Confidence Level and Confidence Interval Range Interpreting Confidence Intervals How Big a Sample? Assumptions for Confidence Intervals Chapter Summary Using Stata Using SPSS Practice Problems Chapter 9 Differences between Samples and Populations The Logic of Hypothesis Testing Null Hypotheses (H0) and Alternative Hypotheses (Ha) One-Tailed and Two-Tailed Tests Hypothesis Tests for Proportions The Steps of the Hypothesis Test One-Tailed and Two-Tailed Tests Hypothesis Tests for Means Example: Testing a Claim about a Population Mean Error and Limitations: How Do We Know We are Correct? Type I and Type II Errors What Does Statistical Significance Really Tell Us? Statistical and Practical Significance  Chapter Summary Using Stata Using SPSS Practice Problems Chapter 10 Comparing Groups Two-Sample Hypothesis Tests The Logic of the Null Hypothesis and Alternative Hypothesis in Two-Sample Tests Notation for Two-Sample Tests The Sampling Distribution for Two-Sample Tests Hypothesis Tests for Differences Between Means Confidence Intervals for Differences Between Means Hypothesis Tests for Differences Between Proportions Confidence Intervals for Differences Between Proportions Statistical and Practical Significance in Two-Sample Tests Chapter Summary Using Stata Using SPSS Practice Problems Chapter 11 Testing Mean Differences among Multiple Groups Comparing Variation Within and Between Groups Hypothesis Testing Using Anova Analysis of Variance Assumptions Determining Which Means are Different: Post-Hoc Tests Anova Compared to RepeateD t-Tests Chapter Summary  Using Stata Using SPSS Practice Problems Chapter 12 Testing the Statistical Significance of Relationships in Cross-Tabulations The Logic of Hypothesis Testing with Chi-Square The Steps of a Chi-Square Test Size and Direction of Effects: Analysis of Residuals Example: Gender and Perceptions of Health Assumptions of Chi-Square Statistical Significance and Sample Size Chapter Summary Using Stata Using SPSS Practice Problems Chapter 13 Ruling Out Competing Explanations for Relationships between Variables Criteria for Causal Relationships Modeling Spurious Relationships Modeling Non-Spurious Relationships Chapter Summary Using Stata Using SPSS Practice Problems Chapter 14 Describing Linear Relationships between Variables Correlation Coefficients Calculating Correlation Coefficients Scatterplots: Visualizing Correlations Regression: Fitting a Line to a Scatterplot The “Best-Fitting” Line Slope and Intercept Calculating the Slope and Intercept Goodness-of-Fit Measures R-Squared (r2) Standard Error of Prediction Dichotomous (“Dummy”) Independent Variables Multiple Regression Statistical Inference for Regression The F-Statistic Standard Error of the Slope Assumptions of Regression Chapter Summary Using Stata Using SPSS Practice Problems Solutions to Odd Numbered Practice Problems Glossary Appendix A Normal Table Appendix B Table of t-Values Appendix C F-Table, for Alpha = .05 Appendix D Chi-Square Table Appendix E Selected List of Formulas Appendix F Choosing Tests for Bivariate Relationships Index

About the Author :
Nancy E. Whittier is Sophia Smith Professor of Sociology at Smith College. She has taught statistics and research methods for over 30 years, and also teaches classes on gender, sexuality, and social movements. She is the author of Frenemies: Feminists, Conservatives, and Sexual Violence (Oxford University Press, 2018), The Politics of Child Sexual Abuse: Emotions, Social Movements, and the State (Oxford University Press, 2009), Feminist Generations (Temple, 1995), and numerous articles on social movements, gender, and sexual violence, and is co-editor of Feminist Frontiers (10th edition, Rowman & Littlefield, 2020, with Verta Taylor and Leila Rupp). Tina Wildhagen is Associate Professor of Sociology at Smith College. She has taught statistics and quantitative research methods for 15 years, and also teaches courses on privilege and power in American education and inequality in higher education. Her research and teaching focus on social inequality in the American education system and on first-generation college students. Her work appears in various scholarly journals, including The Sociological Quarterly, Sociological Perspectives, The Teachers College Record, The Journal of Negro Education and Sociology Compass. Howard J. Gold is Professor of Government at Smith College. He has taught statistics for over 30 years, and also teaches courses on American elections, public opinion and the media, and political behavior. His research focuses on voting behavior, partisanship, and third parties. He is the co-author (with Donald Baumer) of Parties, Polarization and Democracy in the United States and author of Hollow Mandates: American Public Opinion and the Conservative Shift. His work has also appeared in American Politics Quarterly, Political Research Quarterly, Polity, Public Opinion Quarterly and the Social Science Journal.

Review :
“This text offers an intuitive, thorough introduction to quantitative social science research, grounding theory and mathematics with a practical, do-it-yourself approach that integrates two of the major statistical software packages directly into the text. It uses visualizations and interesting examples to make this material equally appealing to introductory-level undergraduates and graduate students taking their first steps into statistical analysis.” “This is a textbook that works both for a professor teaching the course for the first time and needs a how-to manual, or the experienced professor who uses the textbook to compliment in-class lectures. The textbook never reaches beyond the level of the audience and is perfect for an introductory social science statistics course.” “Statistics for Social Understanding provides an introduction to quantitative research methods that presents detailed information in an intuitive and straightforward manner. This approach emphasizes the importance of application, which should encourage students to see the importance and relevance of content throughout the course.”