ISBN-10:
159385191X
ISBN-13:
2901593851919
Pub. Date:
03/20/2007
Publisher:
Guilford Publications, Inc.
Multilevel Analysis for Applied Research: It's Just Regression! / Edition 1

Multilevel Analysis for Applied Research: It's Just Regression! / Edition 1

by Robert Bickel

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Product Details

ISBN-13: 2901593851919
Publisher: Guilford Publications, Inc.
Publication date: 03/20/2007
Series: Methodology in the Social Sciences
Edition description: New Edition
Pages: 355
Product dimensions: 6.00(w) x 1.25(h) x 9.00(d)

About the Author



Robert Bickel, PhD, is Professor of Advanced Educational Studies at Marshall University in Huntington, West Virginia, where he teaches research methods, applied statistics, and the sociology of education. His publications have dealt with a broad range of issues, including high school completion, teen pregnancy, crime on school property, correlates of school size, neighborhood effects on school achievement, and the consequences of the No Child Left Behind Act of 2001 for poor rural schools. Before joining the faculty at Marshall University, Dr. Bickel spent 15 years as a program evaluator and policy analyst, working in a variety of state and local agencies.

Table of Contents



1. Broadening the Scope of Regression Analysis
1.1.Chapter Introduction
1.2. Why Use Multilevel Regression Analysis?
1.3. Limitations of Available Instructional Material
1.4. Multilevel Regression Analysis in Suggestive Historical Context
1.5. It’s Just Regression under Specific Circumstances
1.6. Jumping the Gun to a Multilevel Illustration
1.7. Summing Up
1.8. Useful Resources
2. The Meaning of Nesting
2.1. Chapter Introduction
2.2. Nesting Illustrated: School Achievement and Neighborhood Quality
2.3. Nesting Illustrated: Comparing Public and Private Schools
2.4. Cautionary Comment on Residuals in Multilevel Analysis
2.5. Nesting and Correlated Residuals
2.6. Nesting and Effective Sample Size
2.7. Summing Up
2.8. Useful Resources
3. Contextual Variables
3.1. Chapter Introduction
3.2. Contextual Variables and Analytical Opportunities
3.3. Contextual Variables and Independent Observations
3.4. Contextual Variables and Independent Observations: A Nine-Category Dummy Variable
3.5. Contextual Variables, Intraclass Correlation, and Misspecification
3.6. Contextual Variables and Varying Parameter Estimates
3.7. Contextual Variables and Covariance Structure
3.8. Contextual Variables and Degrees of Freedom
3.9. Summing Up
3.10. Useful Resources
4. From OLS to Random Coefficient to Multilevel Regression
4.1. Chapter Introduction
4.2. Simple Regression Equation
4.3. Simple Regression with an Individual-Level Variable
4.4. Multiple Regression: Adding a Contextual Variable
4.5. Nesting (Again!) with a Contextual Variable
4.6. Is There a Problem with Degrees of Freedom?
4.7. Is There a Problem with Dependent Observations?
4.8. Alternatives to OLS Estimatorspt; FONT-FAMILY: Arial; mso-bidi-font-weight: bold"4.9. The Conceptual Basis of ML Estimators
4.10. Desirable Properties of REML Estimators
4.11. Applying REML Estimators with Random Coefficient Regression Models
4.12. Fixed Components and Random Components
4.13. Interpreting Random Coefficients: Developing a Cautionary Comment
4.14. Subscript Conventions
4.15. Percentage of Variance Explained for Random Coefficient and Multilevel Models
4.16. Grand-Mean Centering
4.17. Grand-Mean Centering, Group-Mean Centering, and Raw Scores Compared
4.18. Summing Up
4.19. Useful Resources
5. Developing the Multilevel Regression Model
5.1. Chapter Introduction
5.2. From Random Coefficient Regression to Multilevel Regression
5.3. Equations for a Random Intercept and Random Slope
5.4. Subscript Conventions for Two-Level Models: Gamma Coefficients
5.5. The Full Equation
5.6. An Implied Cross-Level Interaction Term
5.7. Estimating a Multilevel Model: The Full Equation
5.8. A Multilevel Model with a Random Slope and Fixed Slopes at Level One
5.9. Complexity and Confusion: Too Many Random Components
5.10. Interpreting Multilevel Regression Equations
5.11. Comparing Interpretations of Alternative Specifications
5.12. What Happened to the Error Term?
5.13. Summing Up
5.14. Useful Resources
6. Giving OLS Regression Its Due
6.1. Chapter Introduction
6.2. An Extended Exercise with County-Level Data
6.3. Tentative Specification of an OLS Regression Model
6.4. Preliminary Regression Results
6.5. Surprise Results and Possible Violation of OLS Assumptions
6.6. Curvilinear Relationships: YBUSH by XBLACK, XHISPANIC, XNATIVE
6.7. Quadratic Functional Form
6.8. A Respecified OLS Regression Model
6.9. Interpreting Quadratic Relationships
6.10. Nonadditivity and Interaction Terms
6.11. Further Respecification of the Regression Model
6.12. Clarifying OLS Interaction Effects
6.13. Results for the Respecified OLS Regression Equation for County-Level Data
6.14. Summing Up
6.15. Useful Resources
7. Does Multilevel Regression Have Anything to Contribute?
7.1. Chapter Introduction
7.2. Contextual Effects in OLS Regression
7.3. Respecification and Changing Functional Form
7.4. Addressing the Limitations of OLS
7.5. Counties Nested within States: Intraclass Correlation
7.6. Multilevel Regression Model Specification: Learning from OLS
7.7. Interpreting the Multilevel Regression Equation for County-Level Data
7.8. Knowing When to Stop
7.9. Summing Up
7.10. Useful Resources
8. Multilevel Regression Models with Three Levels
8.1. Chapter Introduction
8.2. Students Nested within Schools and within Districts
8.3. Level One: Students
8.4. Level Two: Schools
8.5. Level Three: Districts
8.6. Notation and Subscript Conventions for Specifying a Three-Level Model
8.7. Estimating a Three-Level Random Coefficient Model
8.8. Adding a Second Level-One Predictor
8.9. Adding a Level-Two Predictor
8.10. Adding a Second Predictor at Level Two and a Predictor at Level Three
8.11. Discretionary Use of Same-Level Interaction Terms
8.12. Ongoing Respecification of a Three-Level Model
8.13. A Level-Two Random Slope at Level Three
8.14. Summing Up
8.15. Useful Resources
9. Familiar Measures Applied to Three-Level Models
9.1. Chapter Introduction
9.2. The Intraclass Correlation Coefficient Revisited
9.3. Percentage of Variance Explained in a Level-One Dependent Variable
9.4. Other Summary Measures Used with Multilevel Regression
9.5. Summing Up
9.6. Useful Resources
10. Determining Sample Sizes for Multilevel Regression
10.1. Chapter Introduction
10.2. Interest in Sample Size in OLS and Multiple Regression
10.3. Sample Size: Rules of Thumb and Data Constraints
10.4. Estimation and Inference for Unstandardized Regression Coefficients
10.5. More Than One Level of Analysis Means More Than One Sample Size
10.6. An Individual-Level OLS Analysis with a Large Sample
10.7. A Group-Level OLS Analysis with a Small Sample
10.8. Standard Errors: Corrected and Uncorrected, Individual and Group Levels
10.9. When Output Is Not Forthcoming!
10.10. Sample Sizes and OLS-Based Commonsense in Multilevel Regression
10.11. Sample Size Generalizations Peculiar to Multilevel Regression
10.12. Level-One Sample Size and Level-Two Statistical Power
10.13. The Importance of Sample Size at Higher Levels
10.14. Summing Up
10.15. Useful Resources
11. Multilevel Regression Growth Models
11.1. Chapter Introduction
11.2. Analyzing Longitudinal Data: Pretest–Posttest
11.3. Nested Measures: Growth in Student Vocabulary Achievement
11.4. Nested Measures: Growth in NCLEX Pass Rates
11.5. Developing Multilevel Regression Growth Models
11.6. Summary Statistics with Growth Models
11.7. Sample Sizes
11.8. The Multilevel Regression Growth Model Respecified
11.9. The Multilevel Regression Growth Model: Further Respecification
11.10. Residual Covariance Structures
11.11. Multilevel Regression Growth Models with Three Levels
11.12. Nonlinear Growth Curves
11.13. NCLEX Pass Rates with a Time-Dependent Predictor
11.14. Summing Up
11.15. Useful Resources

Interviews



Graduate students and instructors in education, psychology, nursing, sociology, management, political science, and other behavioral and social science disciplines, as well as applied researchers, policy analysts, and program evaluators. Will serve as a core text in graduate-level courses on multilevel analysis and advanced data analysis.

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