Modeling association between two or more categorical variables that allow for multiple category choices

Christopher R Bilder, Thomas M. Loughin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Multiple-response (or pick any/c) categorical variables summarize responses to survey questions that ask "pick any" from a set of item responses. Extensions to loglinear model methodology are proposed to model associations between these variables across all their items simultaneously. Because individual item responses to a multiple-response categorical variable are likely to be correlated, the usual chi-square distributional approximations for model-comparison statistics are not appropriate. Adjusted statistics and a new bootstrap procedure are developed to facilitate distributional approximations. Odds ratio and standardized Pearson residual measures are also developed to estimate specific associations and examine deviations from a specified model.

Original languageEnglish (US)
Pages (from-to)433-451
Number of pages19
JournalCommunications in Statistics - Theory and Methods
Volume36
Issue number2
DOIs
StatePublished - Jan 1 2007

Fingerprint

Categorical variable
Multiple Responses
Modeling
Association Model
Statistics
Model Comparison
Log-linear Models
Chi-square
Odds Ratio
Approximation
Bootstrap
Deviation
Likely
Methodology
Estimate
Model

Keywords

  • Bootstrap
  • Correlated binary data
  • Generalized loglinear model
  • Marginal model
  • Multiple-response categorical variable
  • Pick any/c

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Modeling association between two or more categorical variables that allow for multiple category choices. / Bilder, Christopher R; Loughin, Thomas M.

In: Communications in Statistics - Theory and Methods, Vol. 36, No. 2, 01.01.2007, p. 433-451.

Research output: Contribution to journalArticle

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