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Elementary Regression Modeling
A Discrete Approach

- Roger A. Wojtkiewicz - Ball State University, USA

May 2016 | 240 pages | SAGE Publications, Inc

**Elementary Regression Modeling**builds on simple differences between groups to explain regression and regression modeling. User-friendly and immediately accessible, this book gives readers a thorough understanding of control modeling, interaction modeling, modeling linearity with spline variables, and creating research hypotheses that serve as a conceptual basis for many of the processes and procedures quantitative researchers follow when conducting regression analyses.

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Chapter 1: Introductory Ideas

Regression Modeling

Control Modeling

Modeling Interactions

Modeling Linearity With Splines

Testing Research Hypotheses

Classical Approach to Regression

Disadvantages of Classical Approach

Discrete Approach to Regression

Summary

Key Concepts

Notes

Chapter 2: Basic Statistical Procedures

Individual Units and Groups

Measurement

Level of Measurement

Examples for Level of Measurement

Count, Sum, and Transformations

Mean

Proportion and Percentage

Odds and Log odds

Examples of Means and Log Odds

Differences

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 3: Regression Modeling Basics

Difference between Means: The t-test

Linear Regression With a Two-Category Independent Variable

Logistic Regression With a Two-Category Independent Variable

Linear Regression With a Four-Category Independent Variable

Logistic Regression With a Four-Category Independent Variable

Modeling Linear Effect With Dummy Variables

Linear Coefficient in Linear Regression

Linear Coefficient in Logistic Regression

Using Dummy Variables for a Continuous Variable

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 4: Key Regression Modeling Concepts

Unit Vector: Estimating the Intercept

Nestedness

Higher-Order Differences

Constraints

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 5: Control Modeling

Elementary Control Modeling

Elaboration for Controlling

Demographic Standardization for Controlling

Small and Big Models

Allocating Influence With Multiple Control Variables

One-at-a-Time Without Controls

Step Approach

One-at-a-Time With Controls

Hybrid Approach

Nestedness and Constraints

Example Using Logistic Regression

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 6: Modeling Interactions

Interactions as Conditional Differences

Interactions Between Dummy Variables

Interactions Between Dummy Variables and an Interval Variable

Three-Way Interactions

Estimating Separate Models

Example Using Logistic Regression

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 7: Modeling Linearity With Splines

Dummy Variables Nested in an Interval Variable

Introduction to Knotted Spline Variables

Spline Variables Nested in an Interval Variable

Regression Modeling Using Spline Variables

Working With a Continuous Independent Variable

Example Using Logistic Regression

Summary

Key Concepts

Chapter Exercises

Notes

Chapter 8: Conclusion: Testing Research Hypotheses

Bivariate Hypothesis/No Controls

Bivariate Hypothesis/Unanalyzed Controls

Bivariate Hypothesis/Analyzed Controls

Hypothesis Involving Interactions

Hypothesis Involving Nonlinearity

Final Comments

Key Concepts

Summary

Chapter exercises

Notes

### Supplements

Student Resource Site

An open-access companion website features tables and figures from the book, data sets, output files, and a syntax file to accompany the exercises in the book.

An open-access companion website features tables and figures from the book, data sets, output files, and a syntax file to accompany the exercises in the book.