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Fundamentals of regression modeling

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.

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Bibliographic Details
Other Authors: Babones, Salvatore J.
Format: Book
Language:English
Published: London ; Thousand Oaks, California SAGE 2013.
Series:Sage benchmarks in social research methods.
Subjects:
Regression analysis.
Social sciences > Statistical methods.
Social sciences > research > statistical methods.
Statistics.
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Table of Contents:
  • Volume I
  • 1.Regression Fundamentals for the Social Sciences / Salvatore Babones
  • 1.The Meaning of p-Values
  • 2.The Nonutility of Significance Tests: The Significance of Tests of Significance Reconsidered / Sanford Labovitz
  • 3.Mindless Statistics / Gerd Gigerenzer
  • 4.Confusion over Measures of Evidence (p's) versus Errors ([alpha]'s) in Classical Statistical Testing / M.J. Bayarri
  • 5.Why We Don't Really Know What Statistical Significance Means: Implications for Educators / J. Scott Armstrong
  • 6.Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests / Fiona Fidler
  • 2.Control Variables
  • 7.Explaining Interstate Conflict and War: What Should Be Controlled For? / James Lee Ray
  • 8.The Phantom Menace: Omitted Variable Bias in Econometric Research / Kevin A. Clarke
  • 9.Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium / Andrew F. Hayes
  • Contents note continued: 10.Equivalence of the Mediation, Confounding and Suppression Effect / Chondra M. Lockwood
  • 11.Statistical Usage in Sociology: Sacred Cows and Ritual / Sanford Labovitz
  • 12.Stepwise Regression in Social and Psychological Research / Daniel R. Denison
  • 13.Return of the Phantom Menace: Omitted Variable Bias in Political Research / Kevin A. Clarke
  • 14.Stepwise Regression: A Caution / Michael S. Lewis-Beck
  • Volume II
  • 3.Outliers and Influential Points
  • 15.Teaching about Influence in Simple Regression / Frederick O. Lorenz
  • 16.Regression Diagnostics: An Expository Treatment of Outliers and Influential Cases / Robert W. Jackman
  • 17.A Survey of Outlier Detection Methodologies / Jim Austin
  • 18.Practitioners' Corner: Beware of `Good' Outliers and Overoptimistic Conclusions / Vincenzo Verardi
  • 19.Some Observations on Measurement and Statistics / Sanford Labovitz
  • 4.Multicolinearity and Variance Inflation
  • Contents note continued: 20.Issues in Multiple Regression / Robert A. Gordon
  • 21.A Caution Regarding Rules of Thumb for Variance Inflation Factors / Robert M. O'Brien
  • 22.What to Do (and Not Do) with Multicollinearity in State Politics Research / Gregory A. Huber
  • 23.On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental / Gwowen Shieh
  • 24.Correlated Independent Variables: The Problem of Multicollinearity / H.M. Blalock Jr
  • 5.Sample Selection Biases
  • 25.Modeling Selection Effects / David A. Freedman
  • 26.An Introduction to Sample Selection Bias in Sociological Data / Richard A. Berk
  • 27.Models for Sample Selection Bias / Robert D. Mare
  • 28.Sample Selection Bias as a Specification Error / James J. Heckman
  • 29.How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics / Barbara Geddes
  • Contents note continued: 30.When Less Is More: Selection Problems in Large-N and Small-N Cross-National Comparisons / Bernhard Ebbinghaus
  • Volume III
  • 6.Imputation Techniques
  • 31.The Treatment of Missing Data / David C. Howell
  • 32.A Primer on Maximum Likelihood Algorithms Available for Use with Missing Data / Craig K. Enders
  • 33.What to Do about Missing Values in Time-Series Cross-Section Data / Gary King
  • 34.Multiple Imputation for Missing Data: A Cautionary Tale / Paul D. Allison
  • 35.Multiple Imputation for Missing Data: Making the Most of What You Know / Jonathon N. Cummings
  • 36.Imputation of Missing Item Responses: Some Simple Techniques / Mark Huisman
  • 37.Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation / Kenneth Scheve
  • 38.An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values / Carl F. Pieper
  • 7.Interaction Models
  • Contents note continued: 39.Testing for Interaction in Multiple Regression / Paul D. Allison
  • 40.Understanding Interaction Models: Improving Empirical Analyses / Matt Golder
  • 41.Product-Variable Models of Interaction Effects and Causal Mechanisms / Lowell L. Hargens
  • 42.Limitations of Centering for Interactive Models / Richard L. Tate
  • 43.Decreasing Multicollinearity: A Method for Models with Multiplicative Functions / M.S. Sasaki
  • 44.Some Common Myths about Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression / Michael J. Zickar
  • 8.Longitudinal Models
  • 45.A General Panel Model with Random and Fixed Effects: A Structural Equations Approach / Jennie E. Brand
  • 46.A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications / Daniel M. Butler
  • 47.Panel Models in Sociological Research: Theory Into Practice / Charles N. Halaby
  • Contents note continued: 48.Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables / Nathan J. Kelly
  • 49.Using Panel Data to Estimate the Effects of Events / Paul D. Allison
  • Volume IV
  • 9.Instrumental Variable Models
  • 50.Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments / Alan B. Krueger
  • 51.Improving Causal Inference: Strengths and Limitations of Natural Experiments / Thad Dunning
  • 52.Instrumental Variables Estimation in Political Science: A Readers' Guide / Donald P. Green
  • 53.Instrumental Variables in Sociology and the Social Sciences / Kenneth A. Bollen
  • 54.Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak / Regina M. Baker
  • 10.Structural Models
  • 55.Practical Issues in Structural Modeling / Chih-Ping Chou
  • 56.As Others See Us: A Case Study in Path Analysis / D.A. Freedman
  • Contents note continued: 57.Causation Issues in Structural Equation Modeling Research / Stanley A. Mulaik
  • 58.Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach / David W. Gerbing
  • 59.Structural Equation Models in the Social and Behavioral Sciences: Model Building / James G. Anderson
  • 11.Causality
  • 60.Statistical Models for Causation / David A. Freedman
  • 61.Structural Equations and Causal Explanations: Some Challenges for Causal SEM / Keith A. Markus
  • 62.The Estimation of Causal Effects from Observational Data / Stephen L. Morgan
  • 63.Statistical Models for Causation: What Inferential Leverage Do They Provide / David A. Freedman
  • 64.The Foundations of Causal Inference / Judea Pearl.

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