Statistical Methods for the Social Sciences

Agresti and Finley present statistical methods in a style that emphasizes their concepts and their application to the social sciences rather than the mathematics and computational details behind them.

Statistical Methods for the Social Sciences, 4e presents an introduction to statistical methods for students majoring in social science disciplines. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra).

This text may be used in a one or two course sequence. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

1.Introduction

1.1 Introduction to statistical methodology

1.2 Descriptive statistics and inferential statistics

1.3 The role of computers in statistics

1.4 Chapter summary

2. Sampling and Measurement

2.1 Variables and their measurement

2.2 Randomization

2.3 Sampling variability and potential bias

2.4 other probability sampling methods *

2.4 Chapter summary

3. Descriptive statistics

3.1 Describing data with tables and graphs

3.2 Describing the center of the data

3.3 Describing variability of the data

3.4 Measure of position

3.5 Bivariate descriptive statistics

3.6 Sample statistics and population parameters

3.7 Chapter summary

4. Probability Distributions

4.1 Introduction to probability

4.2 Probablitity distributions for discrete and continuous variables

4.3 The normal probability distribution

4.4 Sampling distributions describe how statistics vary

4.5 Sampling distributions of sample means

4.6 Review: Probability, sample data, and sampling distributions

4.7 Chapter summary

5. Statistical inference: estimation

5.1 Point and interval estimation

5.2 Confidence interval for a proportion

5.3 Confidence interval for a mean

5.4 Choice of sample size

5.5 Confidence intervals for median and other parameters*

5.6 Chapter summary

6. Statistical Inference: Significance Tests

6.1 Steps of a significance test

6.2 Significance test for a eman

6.3 Significance test for a proportion

6.4 Decisions and types of errors in tests

6.5 Limitations of significance tests

6.6 Calculating P (Type II error)*

6.7 Small-sample test for a proportion: the binomial distribution*

6.8 Chapter summary

7. Comparison of Two Groups

7.1 Preliminaries for comparing groups

7.2 Categorical data: comparing two proportions

7.3 Quantitative data: comparing two means

7.4 Comparing means with dependent samples

7.5 Other methods for comparing means*

7.6 Other methods for comparing proportions*

7.7 Nonparametric statistics for comparing groups

7.8 Chapter summary

8. Analyzing Association between Categorical Variables

8.1 Contingency Tables

8.2 Chi-squared test of independence

8.3 Residuals: Detecting the pattern of association

8.4 Measuring association in contingency tables

8.5 Association between ordinal variables*

8.6 Inference for ordinal associations*

8.7 Chapter summary

9. Linear Regression and Correlation

9.1 Linear relationships

9.2 Least squares prediction equation

9.3 The linear regression model

9.4 Measuring linear association - the correlation

9.5 Inference for the slope and correlation

9.6 Model assumptions and violations

9.7 Chapter summary

10. Introduction to multivariate Relationships

10.1 Association and causality

10.2 Controlling for other variables

10.3 Types of multivariate relationships

10.4 Inferenential issus in statistical control

10.5 Chapter summary

11. Multiple Regression and Correlation

11.1 Multiple regression model

11.2 Example with multiple regression computer output

11.3 Multiple correlation and R-squared

11.4 Inference for multiple regression and coefficients

11.5 Interaction between predictors in their effects

11.6 Comparing regression models

11.7 Partial correlation*

11.8 Standardized regression coefficients*

11.9 Chapter summary

12. Comparing groups: Analysis of Variance (ANOVA) methods

12.1 Comparing several means: One way analysis of variance

12.2 Multiple comparisons of means

12.3 Performing ANOVA by regression modeling

12.4 Two-way analysis of variance

12.5 Two way ANOVA and regression

12.6 Repeated measures analysis of variance*

12.7 Two-way ANOVA with repeated measures on one factor*

12.8 Effects of violations of ANOVA assumptions

12.9 Chapter summary

13. Combining regression and ANOVA: Quantitative and Categorical Predictors

13.1 Comparing means and comparing regression lines

13.2 Regression with quantitative and categorical predictors

13.3 Permitting interaction between quantitative and categorical predictors

13.4 Inference for regression with quantitative and categorical predictors

13.5 Adjusted means*

13.6 Chapter summary

14. Model Building with Multiple Regression

14.1 Model selection procedures

14.2 Regression diagnostics

14.3 Effects of multicollinearity

14.4 Generalized linear models

14.5 Nonlinearity: polynomial regression

14.6 Exponential regression and log transforms*

14.7 Chapter summary

15. Logistic Regression: Modeling Categorical Responses

15.1 Logistic regression

15.2 Multiple logistic regression

15.3 Inference for logistic regression models

15.4 Logistic regression models for ordinal variables*

15.5 Logistic models for nominal responses*

15.6 Loglinear models for categorical variables*

15.7 Model goodness of fit tests for contingency tables*

15.9 Chapter summary

16. Introduction to Advanced Topics

16.1 Longitudinal data analysis*

16.2 Multilevel (hierarchical) models*

16.3 Event history analysis*

16.4 Path analysis*

16.5 Factor analysis*

16.6 Structural equation models*

16.7 Markov chains*

Appendix: SAS and SPSS for Statistical Analyses

Tables

Answers to selected odd-numbered problems

Index

The author is successful in his goal of introducing statistical methods in a style that emphasized their concepts and their application to the social sciences rather than the mathematics and computational details behind them.

1. Strong emphasis on regression topics. Moreover, a wide variety of regression models (such as linear regression, ANOVA, logistic

regression) are taught in the same format, essentially as special cases of a generalized linear model.

2. Emphasis on concepts, rather than computing formulas. Advanced topics such as regression and ANOVA emphasize interpreting output from computer packages rather than complex computing formulas.

3. Integration of descriptive and inferential statistics from an early point in the text.

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 30 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed article and four texts including "Statistics: The Art and Science of Learning From Data" (with Christine Franklin, Prentice Hall, 2nd edition 2009) and "Categorical Data Analysis" (Wiley, 2nd edition 2002). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003 he was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in 2004 he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 20 countries worldwide. He has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons.

Expert Reviews

"This text is readable, understandable, and well-organized. It provides good examples with SPSS output." (Robert Wilson, University of Delaware).

"Overall, [Agresti/ Finlay] is a good book for introductory statistics that targets general audiences...it covers most topics you want to cover and allows the instructor to choose which topics to include." (Youqin Huang, State University of New York, Albany)

"I originally started using the Agresti/ Finlay book based on its reputation as "the class of the market", in terms of being unfailingly statistically correct and having a "modern" perspective. By "modern", I mean that it is model rather than test oriented, that it gives heavy emphasis to confidence intervals and p-values rather than using arbitrary levels of significance, and that it eschews computational formulae. It has met those expectations..." (Michael Lacey, Colorado State University)

"..the book has been a good and helpful resource for me in preparing the class notes and assigning homework qustions. The main concepts to be understood by students are sampling distribution, confidence interval, p-value, linear regression. The book helps in this..." (Arne Bathke, University of Kentucky)

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