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Elementary Regression Modeling
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Elementary Regression Modeling
A Discrete Approach

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June 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.

Sample Materials & Chapters

Chapter 5

Chapter 6


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ISBN: 9781506303475
£52.00