Statistical Analysis for the Social Sciences: An Interactive Approach Book

All chapters begin with “Overview” and conclude with “Problems,” “References,” and “Putting It All Together.”

1. Introduction.

Statistics.

Variables and Variability.

Preparing Data for Analysis.

2. Research Methodology: A Primer.

The Importance of Good Research Design.

Basics of Research Design.

Pre-Experimental Designs.

True Experimental Designs.

Quasi-Experimental Designs.

Measurement Issues.

Reliability and Validity.

3. Organizing and Displaying Data.

Why Organize and Display Data?

Organizing the Data.

Grouping the Data.

Crosstabulation.

Displaying the Data.

4. Descriptive Statistics.

Why Summarize the Data?

Measures of Central Tendency.

Measures of Dispersion.

Moments about the Mean.

Measures of Bivariate Relationship.

5. Building Blocks of Inferential Statistics: Probability, Chance, Variability, and Distributions.

Probability: The Foundation of Inferential Statistics.

The Role of Probability Theory in Inferential Statistics.

The Shape of Chance Variability: More Pieces of the Puzzle.

Properties of the Normal Distribution.

T-Scores.

6. Sampling Distributions.

Basic Concepts in Statistical Inference.

Using Sample Statistics to Estimate Population Parameters.

Sampling Distributions.

Hypothesis Testing.

7. Statistical Issues in Hypothesis Testing.

Steps in Hypothesis Testing.

Z-Test Interval Estimation.

Statistical Power.

Interpretation and Guidelines for Acceptable Statistical Power.

Effect Size and Practical Importance.

Guidelines for Using Statistics.

8. Testing the Difference between Two Independent Groups: The T-Test.

What's Wrong with the Z-Test?

The Separate Variance Model T-Test.

The Pooled Variance Model T-Test.

Underlying Assumptions.

Choosing between the Separate and Pooled Variance Models.

9. Testing the Difference between Two or More Independent Groups: The Oneway between Groups Analysis of Variance.

Omnibus Tests of Significance.

Specific or General Hypotheses?

The Simple Mathematics of Variance Partitioning.

Between-Groups Variability.

The F-Test.

Underlying Assumptions.

Effect Size Calculations and Power.

Appendix 9.1

10. Testing the Difference between Two or More Independent Groups: Multiple Comparisons.

Overview of Multiple Comparisons.

Planned Comparisons.

Post Hoc Comparisons.

11. Analyzing More Than a Single Independent Variable: Factorial Between Groups Analysis of Variance.

Factorial Designs.

Factorial Analysis of Variance.

Multiple Comparison and Simple Effect Tests.

12. Within Group Designs: Analyzing Repeated Measures.

Basics of Within Group Designs.

Correlated or Dependent Samples T-Test.

Oneway Within-Groups ANOVA.

Mixed Designs.

13. Determining the Relationship between Two Variables: Correlation.

Correlation Basics.

Correlation: Scatterplots.

The Pearson Product-Moment Correlation.

Inferential Uses.

Confidence Intervals.

Strength of Association.

Derivatives of Pearson's Product-Moment Correlation.

Some Cautions and Limitations.

14. Determining the Relationship between Two Variables: Simple Linear Regression.

Some Simple Regression Basics.

Imperfect Prediction.

Statistical Tests for Simple Regression.

Other Issues in Simple Linear Regression.

15. Dealing with More Than a Single Predictor Variable: Multiple Linear Regression.

The Logic of Multiple Linear Regression.

The Multiple Regression Equation and Tests of Significance.

The Incremental Approach to Multiple Linear Regression.

Special Issues in Multiple Linear Regression.

16. Nonparametric Statistical Tests.

Why Nonparametric Statistical Tests?

Chi-Square and the Analysis of Nominal Data.

The Analysis of Ordinal Data.