Elementary Regression Modeling
A Discrete Approach
If you’re in North America, please visit our Sage College Publishing website to purchase or sample this book:
Go to College Publishing WebsiteDescription
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.
Available with Perusall—an eBook that makes it easier to prepare for class
Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.
Contents
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
Description
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.
Available with Perusall—an eBook that makes it easier to prepare for class
Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.
Contents
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
May 2016 | 240 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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.
Available with Perusall—an eBook that makes it easier to prepare for class
Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.
Table Of Contents:
- 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