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Fundamentals of Regression Modeling
Four Volume Set
Edited by:
October 2013 | 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
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
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
Kevin Clarke
The Phantom Menace
Andrew Hayes
Beyond Baron and Kenny
David Mackinnon, Jennifer Krull and Chondra Lockwood
Equivalence of the Mediation, Confounding and Suppression Effect
Sanford Labovitz
Statistical Usage in Sociology
Douglas Henderson and Daniel Denison
Stepwise Regression in Social and Psychological Research
Kevin Clarke
Return of the Phantom Menace
Michael Lewis-Beck
Stepwise Regression
PART THREE: OUTLIERS AND INFLUENTIAL POINTS
Frederick Lorenz
Teaching about Influence in Simple Regression
Kenneth Bollen and Robert Jackman
Regression Diagnostics
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
H.M. Blalock Jr.
Correlated Independent Variables
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
Bernhard Ebbinghaus
When Less Is More
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
Mark Fichman and Jonathon Cummings
Multiple Imputation for Missing Data
Mark Huisman
Imputation of Missing Item Responses
Gary King et al
Analyzing Incomplete Political Science Data
PART SEVEN: INTERACTION MODELS
Paul Allison
Testing for Interaction in Multiple Regression
Thomas Brambor, William Roberts Clark and Matt Golder
Understanding Interaction Models
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
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
Sven Wilson and Daniel Butler
A Lot More to Do
Charles Halaby
Panel Models in Sociological Research
Luke Keele and Nathan Kelly
Dynamic Models for Dynamic Theories
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
Thad Dunning
Improving Causal Inference:
Allison Sovey and Donald Green
Instrumental Variable Estimation in Political Science
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
Heather Bullock et al
Causation Issues in Structual Equation Modeling Research
James Anderson and David Gerbing
Structural Equation Modeling in Practice
James Anderson
Structural Equation Models in the Social and Behavioral Sciences
PART ELEVEN: CAUSALITY
David Freedman
Statistical Models for Causation
Keith A. Markus
Structural Equations and Causal Explanations
Christopher Winship and Stephen Morgan
The Estimation of Causal Effects from Observational Data
David Freedman
Statistical Models for Causation
