Intermediate Social Statistics: A Conceptual and Graphic Approach

Intermediate Social Statistics expertly guides students through a broad range of statistical methods, starting with an overview of fundamental concepts such as basic descriptive statistics and inferential statistics, before moving on to more advanced techniques, including two- and three-way crosstabulations, multiple regression and logistic regression, path analysis, and more. Taking a conceptual approach, the text emphasizes teaching students why we use particular statistics, what statistics can tell us, how statistics could mislead us, and what to do to prevent being misled or misleading others. This approach allows students to learn not only how to apply statistical techniques but also, importantly, the rationale behind the various methods and formulas they'll use in social statistics. Offering a rich variety of illustrative examples and graphics, helpful review exercises, and easy-to-follow instruction, Intermediate Social Statistics helps students master concepts and sharpen their practical skills, allowing them to navigate the world of statistics with confidence.

Table of Contents:
Part One: Descriptive Statistics for One Variable 1: Levels of Measurement Learning Objectives The Stevens Classification Two Related Classifications Graphing Summary Review Questions on Levels of Measurement Notes 2: Measures of Central Tendency Learning Objectives Definitions and Notation Links to Levels of Measurement Potentially Unstable Results Measures of Central Tendency as Averages Summary Review Questions on Central Tendency Notes 3: Measures of Dispersion Learning Objectives Measures for Nominal Variables Quantile-Based Measures of Dispersion Summary Review Questions on Measures of Dispersion Notes 4: Describing the Shape of a Distribution Learning Objectives Modes Skewness and Kurtosis Formulae for Skewness and Kurtosis Summary Review Questions on Shapes of Distributions Notes 5: Summarizing a Distribution Learning Objectives The Five-Number Summary Graphing a Distribution Creating a Mean and Standard Deviation Table Summary Review Questions on Summarizing a Distribution Part Two: Statistical Inference 6: Sampling Distributions Learning Objectives The Normal Distribution The t Distribution The Chi-Square Distribution Relations Among the Normal, t, and Chi-square Distributions The Effect of Sample Size Summary Review Questions on Sampling Distributions Notes 7: The Standard Model of Statistical Inference Learning Objectives Central Ideas Summary Review Questions on the Standard Model of Statistical Inference Note 8: The Bayesian Alternative Learning Objectives Frequentist and Personal Probabilities Bayes' Theorem Estimating a Proportion Credible Intervals Numerical Equivalence to Standard Results Summary Review Questions on the Bayesian Alternative Notes Part Three: Measures of Association Association and Independence 9: Measures for Nominal and Ordinal Variables Learning Objectives PRE Measures The Odds Ratio Summary Review Questions on Measures for Nominal and Ordinal Variables Notes 10: Pearson's r Learning Objectives Explaining the Formula Correlation Matrices Graphic Displays for Interval or Ratio Data Summary Review Questions on Pearson's r Notex Part Four: Examining Crosstabulations 11: Two-Way Tables Learning Objectives Reading a Crosstabulation Heavy and Light Cells Setting Up a Crosstabulation for Presentation Further Graphic Methods of Clarifying a Crosstabulation Summary Review Questions on Heavy and Light Cells and Graphic Methods Notes 12: Conditional Tables Learning Objectives The Columbia Approach Specification Causal Chains Spurious Association Distortion Conditional Probabilities An Illustration with Polytomies Summary Review Questions on Conditional Tables Notes Part Five: Regression 13: Bivariate Regression Learning Outcomes Origins The Principle of Least Squares The Form of the Equation Interpreting b Graphic Display for Bivariate Regression Non-linear Trends Summary Review Questions on Bivariate Regression Notes 14: Multiple Regression Learning Objectives Why Multiple Regression (MR)? Obtaining b's in Multiple Regression Interpreting b's in Multiple Regression Getting the Right Variables into the Equation Interpreting a Table of Regression Results Interaction Terms Setting Up a Regression Table Graphs Presenting Regression Results The Special Case of Analysis of Variance (ANOVA) Summary Review Questions on Multiple Regression and ANOVA Notes 15: Path Analysis Learning Objectives A Famous Path Model Setting Up Path Diagrams Equations Decomposing a Correlation Steps in Decomposing a Correlation A Further Example Summary Review Questions on Path Analysis Notes 16: Logistic Regression Learning Objectives Why Logistic Regression? Logits Interpreting the b's A Sample Logistic Regression Table An Extension of the Logistic Model: Multinomial Regression Summary Review Questions on Logistic Regression Notes Appendix A: Going a Step Further A1: Minimizing the Sum of Squared Deviations A2: The Mean and Standard Deviation of Z-Scores A3: The Standard Deviation of a Proportion Appendix B: Some Additional Explanations B1: Basic Notes on Logarithms B2: Obtaining Expected Values for Chi-Square B3: Another Form of Bayesian Hypothesis Testing Glossary of Statistical Terms Credits References Index

About the Author :
Robert Arnold worked for 13 years in applied research before becoming a university professor. He is currently an associate professor in the Department of Sociology, Anthropology, and Criminology at the University of Windsor. His primary area of interest is quantitative methodology and he has taught methods and statistics from the undergraduate to the doctoral level for the last 25 years. He has also taught introductory sociology, social psychology, and sociology of the contemporary family, which he has offered at the University of Windsor for the past six years. He has served as chair of the Advisory Committee for the University of Windsor's M.A. in Social Data Analysis (MASDA) and has co-authored academic papers in criminology and sociology of health as well as in program evaluation.

Review :
"Intermediate Social Statistics approaches concepts in a way that emphasizes the rationale behind statistical methods. The instruction is great, and the concepts are supported by good examples and well-represented graphically. Students will get everything they need from this text." --Tom Buchanan, Mount Royal University "I was impressed with the thorough approach. . . . Few books have as many examples completely worked out as this book does." --Kenneth MacKenzie, McGill University