# Statistical Methods for the Social Sciences / Edition 4

ISBN-10: 0130272957

ISBN-13: 9780130272959

Pub. Date: 01/11/2008

Publisher: Pearson

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

## Overview

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.

## Product Details

ISBN-13:
9780130272959
Publisher:
Pearson
Publication date:
01/11/2008
Edition description:
New Edition
Pages:
624
Sales rank:
118,130
Product dimensions:
8.00(w) x 10.00(h) x 1.00(d)

## Related Subjects

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

13.6 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

16.1 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

Index

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