Statistical methods for the social sciences
Author(s)
Bibliographic Information
Statistical methods for the social sciences
（Pearson new international edition）
Pearson Education, c2014
4th ed
Available at / 4 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes index
Description and Table of Contents
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. Datasets and other resources (where applicable) for this book are available here.
Table of Contents
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
by "Nielsen BookData"
