Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It

Name: Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It
ISBN: 9781446286340

Salvatore Babones

doi:10.4135/9781446286340

Major Works

Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It

Edited by:Salvatore Babones
Publisher: SAGE Publications Ltd
Series: SAGE Benchmarks in Social Research Methods
- Publication year: 2013
- Online pub date: September 28, 2015
Discipline: Quantitative/Statistical Research
DOI: https://doi.org/10.4135/9781446286340
Keywords:
statistical models
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Online ISBN: 9781446286340

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‘Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It’, CarinaMoodEuropean Sociological Review, 26(1) (2010): 67–82. Reprinted by permission of Oxford University Press.

Chronology of Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It

Volume I
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- 1. An Orientation to Applied Statistical Modeling in the Social Sciences
  
  Salvatore Babones
- Variables and Colinearity
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  - 2. Explaining Interstate Conflict and War: What Should Be Controlled For?
    
    James Ray
    2003
    Conflict Management and Peace Science
  - 3. The Moderator–Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations
    
    Reuben Baron | David Kenny
    1986
    Journal of Personality and Social Psychology
  - 4. Understanding and Using Mediators and Moderators
    
    Amery Wu | Bruno Zumbo
    2008
    Social Indicators Research
  - 5. Collinearity, Power, and Interpretation of Multiple Regression Analysis
    
    Charlotte Mason | William Perreault
    1991
    Journal of Marketing Research
  - 6. A Caution Regarding Rules of Thumb for Variance Inflation Factors
    
    Robert O'Brien
    2007
    Quality & Quantity
  - 7. What to Do (and Not Do) with Multicollinearity in State Politics Research
    
    Kevin Arceneaux | Gregory Huber
    2007
    State Politics & Policy Quarterly
- Interaction Models
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  - 8. Theory Building and the Statistical Concept of Interaction
    
    Hubert Blalock
    1965
    American Sociological Review
  - 9. Testing for Interaction in Multiple Regression
    
    Paul Allison
    1977
    American Journal of Sociology
  - 10. In Defense of Multiplicative Terms in Multiple Regression Equations
    
    Robert Friedrich
    1982
    American Journal of Political Science
  - 11. Hypothesis Testing and Multiplicative Interaction Terms
    
    Bear Braumoeller
    2004
    International Organization
  - 12. Understanding Interaction Models: Improving Empirical Analyses
    
    Thomas Brambor | William Clark | Matt Golder
    2006
    Political Analysis
- Multilevel Models
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  - 13. Modeling Multilevel Data Structures
    
    Marco Steenbergen | Bradford Jones
    2002
    American Journal of Political Science
  - 14. Multilevel Models: Methods and Substance
    
    Thomas DiPrete | Jerry Forristal
    1994
    Annual Review of Sociology
  - 15. Multilevel Analysis in Public Health Research
    
    Ana Diez-Roux
    2000
    Annual Review of Public Health
  - 16. Multilevel Modeling: A Review of Methodological Issues and Applications
    
    Robert Dedrick | John Ferron | Melinda Hess | Kristine Hogarty | Jeffrey Kromrey | Thomas Lang | John Niles | Reginald Lee
    2009
    Review of Educational Research
  - 17. Sufficient Sample Sizes for Multilevel Modeling
    
    Cora Maas | Joop Hox
    2005
    Methodology
Volume II
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- Models for Panel Data
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  - 18. Panel Models in Sociological Research: Theory into Practice
    
    Charles Halaby
    2004
    Annual Review of Sociology
  - 19. Problems with Repeated Measures Analysis: Demonstration with a Study of the Diversification and Performance Relationship
    
    Donald Bergh
    1995
    Academy of Management Journal
  - 20. Modeling Error in Quantitative Macro-comparative Research
    
    Salvatore Babones
    2009
    Journal of World-Systems Research
  - 21. Advances in Analysis of Longitudinal Data
    
    Robert Gibbons | Donald Hedeker | Stephen DuToit
    2010
    Annual Review of Clinical Psychology
- Time Series Cross-Sectional Analysis
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  - 22. What to Do (and Not to Do) with Time-Series Cross-Section Data
    
    Nathaniel Beck | Jonathan Katz
    1995
    American Political Science Review
  - 23. Sense and Sensitivity in Pooled Analysis of Political Data
    
    Bernhard Kittel
    1999
    European Journal of Political Research
  - 24. Dirty Pool
    
    Donald Green | Soo Kim | David Yoon
    2001
    International Organization
  - 25. Time-Series–Cross-Section Data: What Have We Learned in the Past Few Years?
    
    Nathaniel Beck
    2001
    Annual Review of Political Science
  - 26. A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications
    
    Sven Wilson | Daniel Butler
    2007
    Political Analysis
- Spatial Models
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  - 27. Spatial Autocorrelation: Trouble or New Paradigm?
    
    Pierre Legendre
    1993
    Ecology
  - 28. “The Problem of Spatial Autocorrelation” and Local Spatial Statistics
    
    A. Fotheringham
    2009
    Geographical Analysis
  - 29. Under the Hood: Issues in the Specification and Interpretation of Spatial Regression Models
    
    Luc Anselin
    2002
    Agricultural Economics
  - 30. Spatial Regression Models for Demographic Analysis
    
    Guangqing Chi | Jun Zhu
    2008
    Population Research and Policy Review
  - 31. Space Is More Than Geography: Using Spatial Econometrics in the Study of Political Economy
    
    Nathaniel Beck | Kristian Gleditsch | Kyle Beardsley
    2006
    International Studies Quarterly
Volume III
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- Logistic Regression
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  - 32. An Introduction to Logistic Regression Analysis and Reporting
    
    Chao-Ying Peng | Kuk Lee | Gary Ingersoll
    2002
    The Journal of Educational Research
  - 33. A Tutorial in Logistic Regression
    
    Alfred DeMaris
    1995
    Journal of Marriage and Family
  - 34. Logistic Regression: Description, Examples, and Comparisons
    
    S. Morgan | Jay Teachman
    1988
    Journal of Marriage and Family
  - 35. Binary Response Models: Logits, Probits and Semiparametrics
    
    Joel Horowitz | N. Savin
    2001
    Journal of Economic Perspectives
  - 36. Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It
    
    Carina Mood
    2010
    European Sociological Review
- Multinomial Logit
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  - 37. A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression
    
    Carrie Petrucci
    2009
    Journal of Social Service Research
  - 38. Multinomial Probit and Multinomial Logit: A Comparison of Choice Models for Voting Research
    
    Jay Dow | James Endersby
    2004
    Electoral Studies
  - 39. A Conceptual Framework for Ordered Logistic Regression Models
    
    Andrew Fullerton
    2009
    Sociological Methods & Research
- Poisson Regression
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  - 40. Analysis of Count Data Using Poisson Regression
    
    M. Hutchinson | Matthew Holtman
    2005
    Research in Nursing & Health
  - 41. The Analysis of Count Data: Overdispersion and Autocorrelation
    
    David Barron
    1992
    Sociological Methodology
  - 42. Negative Multinomial Regression Models for Clustered Event Counts
    
    Guang Guo
    1996
    Sociological Methodology
  - 43. A Comparison of Poisson, Negative Binomial, and Semiparametric Mixed Poisson Regression Models: With Empirical Applications to Criminal Careers Data
    
    Kenneth Land | Patricia McCall | Daniel Nagin
    1996
    Sociological Methods & Research
- Instrumental Variables
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  - 44. Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments
    
    Joshua Angrist | Alan Krueger
    2001
    Journal of Economic Perspectives
  - 45. Controlling for Endogeneity with Instrumental Variables in Strategic Management Research
    
    Guilhem Bascle
    2008
    Strategic Organization
  - 46. Model Specification in Instrumental-Variables Regression
    
    Thad Dunning
    2008
    Political Analysis
  - 47. That Instrument Is Lousy! In Search of Agreement When Using Instrumental Variables Estimation in Substance Use Research
    
    Michael French | Ioana Popovici
    2011
    Health Economics
Volume IV
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- Structural Equation Modeling
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  - 48. Path Analysis: Sociological Examples
    
    Otis Duncan
    1966
    American Journal of Sociology
  - 49. Structural Equation Models
    
    William Bielby | Robert Hauser
    1977
    Annual Review of Sociology
  - 50. Practical Issues in Structural Modeling
    
    P. Bentler | Chih-Ping Chou
    1987
    Sociological Methods & Research
  - 51. Total, Direct, and Indirect Effects in Structural Equation Models
    
    Kenneth Bollen
    1987
    Sociological Methodology
  - 52. Principles and Practice in Reporting Structural Equation Analyses
    
    Roderick McDonald | Moon-Ho Ho
    2002
    Psychological Methods
  - 53. Instrumental Variables in Sociology and the Social Sciences
    
    Kenneth Bollen
    2012
    Annual Review of Sociology
- Latent Variable Models
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  - 54. Confirmatory Factor-Analytic Structures and the Theory Construction Process
    
    Ronald Burt
    1973
    Sociological Methods & Research
  - 55. Latent Variables in Psychology and the Social Sciences
    
    Kenneth Bollen
    2002
    Annual Review of Psychology
  - 56. Specification, Evaluation, and Interpretation of Structural Equation Models
    
    Richard Bagozzi | Youjae Yi
    2012
    Journal of the Academy of Marketing Science
  - 57. Latent Variable Models under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators
    
    Kenneth Bollen | James Kirby | Patrick Curran | Pamela Paxton | Feinian Chen
    2007
    Sociological Methods & Research
  - 58. The Fallacy of Formative Measurement
    
    Jeffrey Edwards
    2011
    Organizational Research Methods

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Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It

Key Reading

All Readings

Entry

Table of Contents

Volume I

Variables and Colinearity

Interaction Models

Multilevel Models

Volume II

Models for Panel Data

Time Series Cross-Sectional Analysis

Spatial Models

Volume III

Logistic Regression

Multinomial Logit

Poisson Regression

Instrumental Variables

Volume IV

Structural Equation Modeling

Latent Variable Models

Sign in to access this content

Get a 30 day FREE TRIAL

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Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do about It

Key Reading

All Readings

Entry

Table of Contents

Volume I

Variables and Colinearity

Interaction Models

Multilevel Models

Volume II

Models for Panel Data

Time Series Cross-Sectional Analysis

Spatial Models

Volume III

Logistic Regression

Multinomial Logit

Poisson Regression

Instrumental Variables

Volume IV

Structural Equation Modeling

Latent Variable Models

Sign in to access this content

Get a 30 day FREE TRIAL

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