ISBN: 9781292220345
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Statistical Methods for the Social Sciences, 5e (e-Book VS 12m)

By Alan Agresti


For courses in Statistical Methods for the Social Sciences.

Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition uses examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics—often a stumbling block for students—while avoiding reliance on an overly simplistic recipe-based approach to statistics.


1 Introduction


1.1 Introduction to Statistical Methodology

1.2 Descriptive Statistics and Inferential Statistics

1.3 The Role of Computers and Software 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*

2.5 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 Measures 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 Probability 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: Population, 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 Estimation Methods: Maximum Likelihood and the Bootstrap*

5.6 Chapter Summary


6 Statistical Inference: Significance Tests


6.1 The Five Parts of a Significance Test

6.2 Significance Test for a Mean

6.3 Significance Test for a Proportion

6.4 Decisions and Types of Errors in Tests

6.5 Limitations of Significance Tests

6.6 Finding 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 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 Inferences 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 Inferential Issues in Statistical Control

10.5 Chapter Summary


11 Multiple Regression and Correlation


11.1 The Multiple Regression Model

11.2 Multiple Correlation and R2

11.3 Inferences for Multiple Regression Coefficients

11.4 Modeling Interaction Effects

11.5 Comparing Regression Models

11.6 Partial Correlation*

11.7 Standardized Regression Coefficients*

11.8 Chapter Summary


12 Regression with Categorical Predictors: Analysis of Variance Methods


12.1 Regression Modeling with Dummy Variables for Categories

12.2 Multiple Comparisons of Means

12.3 Comparing Several Means: Analysis of Variance

12.4 Two-Way ANOVA and Regression Modeling

12.5 Repeated-Measures Analysis of Variance*

12.6 Two-Way ANOVA with Repeated Measures on a Factor*

12.7 Chapter Summary


13 Multiple Regression with Quantitative and Categorical Predictors


13.1 Models with Quantitative and Categorical Explanatory Variables

13.2 Inference for Regression with Quantitative and Categorical Predictors

13.3 Case Studies: Using Multiple Regression in Research

13.4 Adjusted Means*

13.5 The Linear Mixed Model*

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 Nonlinear Relationships: Polynomial Regression

14.6 Exponential Regression and Log Transforms*

14.7 Robust Variances and Nonparametric Regression*

14.8 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*

15.8 Chapter Summary


Appendix: R, Stata, SPSS, and SAS for Statistical Analyses