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Babones

Fundamentals of Regression Modeling

Medium: Buch
ISBN: 978-1-4462-0828-1
Verlag: Sage Publications
Erscheinungstermin: 16.10.2013
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9781446208281
  • Medium: Buch
  • ISBN: 978-1-4462-0828-1
  • Verlag: Sage Publications
  • Erscheinungstermin: 16.10.2013
  • Sprache(n): Englisch
  • Auflage: Four-Volume Set Auflage
  • Serie: SAGE Benchmarks in Social Research Methods
  • Produktform: Kartoniert
  • Gewicht: 2790 g
  • Seiten: 1496
  • Format (B x H x T): 163 x 241 x 104 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

Salvatore J. Babones is a senior lecturer in sociology and social policy at the University of Sydney and an associate fellow at the Institute for Policy Studies (IPS). Previously, he was an assistant professor of sociology, public health, and public and international affairs at the University of Pittsburgh. He holds both a PhD in sociology and an MSE in mathematical sciences from the Johns Hopkins University. Dr. Babones is the author or editor of eight books and more than thirty academic papers. He is the editor of Applied Statistical Modeling and Fundamentals of Regression Modeling, both published by SAGE as part of the Benchmarks in Social Research Methods reference series. His academic research focuses on globalization, economic development, and statistical methods for comparative social science research.

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