Statistical methods for the social sciences

Description

The book 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). - The book contains sufficient material for a two-semester sequence of courses. 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.

The book 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). The book contains sufficient material for a two-semester sequence of courses. 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.

The fourth edition has an even stronger emphasis on concepts and applications, with greater attention to "real data" both in the examples and exercises. The mathematics is still downplayed, in particular probability, which is all too often a stumbling block for students. On the other hand, the text is not a cookbook. Reliance on an overly simplistic recipe-based approach to statistics is not the route to good statistical practice. Changes in the Fourth Edition: Since the first edition, the increase in computer power coupled with the continued improvement and accessibility of statistical software has had a major impact on the way social scientists analyze data. Because of this, this book does not cover the traditional shortcut hand-computational formulas and approximations. The presentation of computationally complex methods, such as regression, emphasizes interpretation of software output rather than the formulas for performing the analysis. Teh text contains numerous sample printouts, mainly in the style of SPSS and occasionaly SAS, both in chapter text and homework problems. This edition also has an appendix explaining how to apply SPSS and SAS to conduct the methods of each chapter and a website giving links to information about other software.

Content

Latest edition,

1. Introduction - 2. Sampling and Measurement- 3. Descriptive statistics - 4. Probability Distributions - 5. Statistical inference: estimation - 6. Statistical Inference: Significance Tests - 7. Comparison of Two Groups - 8. Analyzing Association between Categorical Variables - 9. Linear Regression and Correlation - 10. Introduction to multivariate Relationships - 11. Multiple Regression and Correlation - 12. Comparing groups: Analysis of Variance (ANOVA) methods - 13. Combining regression and ANOVA: Quantitative and Categorical Predictors - 14. Model Building with Multiple Regression - 15. Logistic Regression: Modeling Categorical Responses ; 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 -