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Weighting for item non‐response in attitude scales by using latent variable models with covariates
Author(s) -
Moustaki Irini,
Knott Martin
Publication year - 2000
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/1467-985x.00177
Subject(s) - covariate , statistics , latent variable , latent variable model , missing data , weighting , propensity score matching , polytomous rasch model , context (archaeology) , item response theory , population , econometrics , local independence , mathematics , psychometrics , demography , medicine , geography , radiology , archaeology , sociology
We discuss the use of latent variable models with observed covariates for computing response propensities for sample respondents. A response propensity score is often used to weight item and unit responders to account for item and unit non‐response and to obtain adjusted means and proportions. In the context of attitude scaling, we discuss computing response propensity scores by using latent variable models for binary or nominal polytomous manifest items with covariates. Our models allow the response propensity scores to be found for several different items without refitting. They allow any pattern of missing responses for the items. If one prefers, it is possible to estimate population proportions directly from the latent variable models, so avoiding the use of propensity scores. Artificial data sets and a real data set extracted from the 1996 British Social Attitudes Survey are used to compare the various methods proposed.