Multiple Regression and Beyond

ISBN-10: 0205326447
ISBN-13: 9780205326440
Edition: 2006
Authors: Timothy Z. Keith
List price: $196.80 Buy it from $15.92
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Description: Timothy Keith aims to provide a conceptually-oriented introduction to multiple regression. He concentrates on multiple regression analysis in the first half of the book and structural equation modelling in the second half.

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Book details

List price: $196.80
Copyright year: 2006
Publisher: Allyn & Bacon, Incorporated
Publication date: 6/24/2005
Binding: Hardcover
Pages: 552
Size: 7.25" wide x 9.25" long x 1.00" tall
Weight: 2.2
Language: English

Timothy Keith aims to provide a conceptually-oriented introduction to multiple regression. He concentrates on multiple regression analysis in the first half of the book and structural equation modelling in the second half.

Preface
Multiple Regression
Introduction and Simple (Bivariate) Regression
Simple (Bivariate) Regression
Regression in Perspective
Other Issues
Review of Some Basics
Working with Extant Data Sets
Summary
Exercises
Notes
Multiple Regression: Introduction
A New Example: Regressing Grades on Homework and Parent Education
Questions
Direct Calculation of [beta] and R[superscript 2]
Summary
Exercises
Notes
Multiple Regression: More Detail
Why R[superscript 2] '' r[superscript 2]+r[superscript 2]
Predicted Scores and Residuals
Least Squares
Regression Equation = Creating a Composite?
Assumptions of Regression and Regression Diagnostics
Summary
Exercises
Note
Three and More Independent Variables and Related Issues
Three Predictor Variables
Rules of Thumb: Magnitude of Effects
Four Independent Variables
Common Causes and Indirect Effects
The Importance of R[superscript 2]?
Prediction and Explanation
Summary
Exercises
Notes
Three Types of Multiple Regression
Simultaneous Multiple Regression
Sequential Multiple Regression
Stepwise Multiple Regression
The Purpose of the Research
Combining Methods
Summary
Exercises
Notes
Analysis of Categorical Variables
Dummy Variables
Other Methods of Coding Categorical Variables
Unequal Group Sizes
Additional Methods and Issues
Summary
Exercises
Notes
Categorical and Continuous Variables
Sex, Achievement, and Self-Esteem
Interactions
A Statistically Significant Interaction
Specific Types of Interactions Between Categorical and Continuous Variables
Caveats and Additional Information
Summary
Exercises
Notes
Continuous Variables: Interactions and Curves
Interactions Between Continuous Variables
Moderation, Mediation, and Common Cause
Curvilinear Regression
Summary
Exercises
Note
Multiple Regression: Summary, Further Study, and Problems
Summary
Assumptions and Regression Diagnostics
Topics for Additional Study
Problems with Mr?
Exercises
Note
Beyond Multiple Regression
Path Modeling: Structural Equation Modeling with Measured Variables
Introduction to Path Analysis
A More Complex Example
Summary
Exercises
Notes
Path Analysis: Dangers and Assumptions
Assumptions
The Danger of Common Causes
Intervening (Mediating) Variables
Other Possible Dangers
Dealing with Danger
Review: Steps in a Path Analysis
Summary
Exercises
Notes
Analyzing Path Models Using SEM Programs
SEM Programs
Reanalysis of the Parent Involvement Path Model
Advantages of SEM Programs
More Complex Models
Advice: Mr Versus SEM Programs
Summary
Exercises
Notes
Error: The Scourge of Research
Effects of Unreliability
Effects of Invalidity
Latent Variable SEM and Errors of Measurement
Summary
Exercises
Notes
Confirmatory Factor Analysis
Factor Analysis or the Measurement Model
An Example with the Das
Testing Competing Models
Hierarchical Models
Model Fit and Model Modification
Additional uses of CFA
Summary
Exercises
Putting It All Together: Introduction to Latent Variable SEM
Putting the Pieces Together
An Example: Effects of Peer Rejection
Competing Models
Model Modifications
Summary
Exercises
Latent Variable Models: More Advanced Topics
Single Indicators and Correlated Errors
Multisample Models
Replication and Cross-Validation
Dangers, Revisited
Summary
Exercises
Notes
Summary: Path Analysis, CFA, and SEM
Summary
Issues Incompletely or Not Covered
Additional Resources
Data Files
Sample Statistical Programs and Multiple Regression Output
Sample Output from SEM Programs
Partial and Semipartial Correlation
Review of Basic Statistics Concepts
References
Name Index
Subject Index

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