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Fundamentals of Regression Modeling
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Fundamentals of Regression Modeling

Four Volume Set
Edited by:


© 2014 | 1 496 pages | SAGE Publications Ltd
This new four-volume major work presents a collection of landmark studies on the topic of regression modeling, identifying the most important, fundamental articles out of thousands of relevant contributions. The social sciences - particularly sociology and political science - have made extensive use of regression models since the 1960s, and regression modeling continues to be the staple method of the field. The collection is framed by an orienting essay which presents to a guide to regression modelling, written with applied practitioners in mind.
 
VOLUME ONE
 
PART ONE: THE MEANING OF P-VALUES
Sanford Labovitz
The Non-Utility of Significance Tests
The Significance of Tests of Significance Reconsidered  
Gerd Gigerenzer
Mindless Statistics
Raymond Hubbard and M.J. Bayarri
Confusion over Measures of Evidence (p's) versus Errors (?'s) in Classical Statistical Testing
Raymond Hubbard and J. Scott Armstrong
Why We Don't Really Know What Statistical Significance Means
Implications for Educators Statistical Significance  
Andrea Schwab et al
Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests
 
PART TWO: CONTROL VARIABLES
James Lee Ray
Explaining Interstate Conflict and War
What Should Be Controlled for?  
Kevin Clarke
The Phantom Menace
Omitted Variable Bias in Econometric Research  
Andrew Hayes
Beyond Baron and Kenny
Statistical Mediation Analysis in the New Millennium  
David Mackinnon, Jennifer Krull and Chondra Lockwood
Equivalence of the Mediation, Confounding and Suppression Effect
Sanford Labovitz
Statistical Usage in Sociology
Sacred Cows and Ritual  
Douglas Henderson and Daniel Denison
Stepwise Regression in Social and Psychological Research
Kevin Clarke
Return of the Phantom Menace
Michael Lewis-Beck
Stepwise Regression
A Caution  
 
PART THREE: OUTLIERS AND INFLUENTIAL POINTS
Frederick Lorenz
Teaching about Influence in Simple Regression
Kenneth Bollen and Robert Jackman
Regression Diagnostics
An Expository Treatment of Outliers and Influential Cases  
Victoria Hodge and Jim Austin
A Survey of Outlier Detection Methodologies
Catherine Dehon, Marjorie Gassner and Vincenzo Verardi
Practitioners' Corner
Sanford Labovitz
Some Observations on Measurement and Statistics
 
PART FOUR: MULTICOLINEARITY AND VARIANCE INFLATION
Robert Gordon
Issues in Multiple Regression
Robert O'Brien
A Caution Regarding Rules of Thumb for Variance Inflation Factors
Kevin Arceneaux and Gregory Huber
What to Do (and Not Do) with Multicolinearity in State Politics Research
Gwowen Shieh
On the Misconception of Multicollinearity in Detection of Moderating Effects
Multicollinearity Is Not Always Detrimental  
H.M. Blalock Jr.
Correlated Independent Variables
The Problem of Multicollinearity  
 
PART FIVE: SAMPLE SELECTION BIASES
Thad Dunning and David Freedman
Modeling Selection Effects
Richard Berk
An Introduction to Sample Selection Bias in Sociological Data
Christopher Winship and Robert Mare
Models for Sample Selection Bias
James Heckman
Sample Selection Bias as a Specification Error
Barbara Geddes
How the Cases You Choose Affect the Answers You Get
Selection Bias in Comparative Politics  
Bernhard Ebbinghaus
When Less Is More
Selection Problems in Large-N and Small-N Cross-National Comparisons  
 
PART SIX: IMPUTATION TECHNIQUES
David Howell
The Treatment of Missing Data
Craig Enders
A Primer on Maximum Likelihood Algorithms Available for Use with Missing Data
James Honaker and Gary King
What to Do about Missing Values in Time-Series Cross-Section Data
Paul Allison
Multiple Imputation for Missing Data
A Cautionary Tale  
Mark Fichman and Jonathon Cummings
Multiple Imputation for Missing Data
Making the Most of What You Know  
Mark Huisman
Imputation of Missing Item Responses
Some Simple Techniques  
Gary King et al
Analyzing Incomplete Political Science Data
An Alternative Algorithm for Multiple Imputation  
Landermanetal-1997  
 
PART SEVEN: INTERACTION MODELS
Paul Allison
Testing for Interaction in Multiple Regression
Thomas Brambor, William Roberts Clark and Matt Golder
Understanding Interaction Models
Improving Empirical Analyses  
Lowell Hargens
Product-Variable Models of Interaction Effects and Causal Mechanisms
Richard Tate
Limitations of Centering for Interactive Models
Kent Smith and M.S. Sasaki
Decreasing Multicollinearity
A Method for Models with Multiplicative Functions  
Dev Dalal and Michael Zickar
Some Common Myths about Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression
 
PART EIGHT: LONGITUDINAL MODELS
Kenneth Bollen and Jennie Brand
A General Panel Model with Random and Fixed Effects
A Structural Equations Approach  
Sven Wilson and Daniel Butler
A Lot More to Do
The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications  
Charles Halaby
Panel Models in Sociological Research
Theory into Practice  
Luke Keele and Nathan Kelly
Dynamic Models for Dynamic Theories
The ins and outs of Lagged Dependent Variables  
Paul D. Allison
Using Panel Data to Estimate the Effects of Events
 
PART NINE: INSTRUMENTAL VARIABLE MODELS
Joshua Angrist and Alan Krueger
Instrumental Variables and the Search for Identification
From Supply and Demand to Natural Experiments  
Thad Dunning
Improving Causal Inference:
Strengths and Limitations of Natural Experiments  
Allison Sovey and Donald Green
Instrumental Variable Estimation in Political Science
A Readers' Guide  
Kenneth Bollen
Instrumental Variables in Sociology and the Social Sciences
John Bound et al
Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak
 
PART TEN: STRUCTURAL MODELS
P.M. Bentler and Chih-Ping Chou
Practical Issues in Structural Modeling
D.A. Freedman
As Others See Us
A Case Study in Path Analysis: Journal of Education and Behavioral Statistics  
Heather Bullock et al
Causation Issues in Structual Equation Modeling Research
James Anderson and David Gerbing
Structural Equation Modeling in Practice
A Review and Recommended Two-Step Approach  
James Anderson
Structural Equation Models in the Social and Behavioral Sciences
Model-Building  
 
PART ELEVEN: CAUSALITY
David Freedman
Statistical Models for Causation
Keith A. Markus
Structural Equations and Causal Explanations
Some Challenges for Causal Structural Equation Modeling  
Christopher Winship and Stephen Morgan
The Estimation of Causal Effects from Observational Data
David Freedman
Statistical Models for Causation
What Inferential Leverage Do They Provide?  
Pearl-2010  

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ISBN: 9781446208281
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