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