Statistical methods for the social sciences 

 - 4ª ed 
 -  Pearson Education,  2009 
 - xi + 609 p. ; 25 cm 
<br/><br/>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-- 
<br/><br/> 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. ;  ;  
<br/><br/><label>ISBN: </label>9780137131501 
<br/><br/><label>Subjects--Uniform Titles: </label><br/>QA MATHEMATICS
<br/><br/><label>Subjects--Topical Terms: </label><br/>Statistics<br/>Mathematical statistics<br/>Social sciences<br/>