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Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions
Author(s) -
Kreuter Frauke,
Yan Ting,
Tourangeau Roger
Publication year - 2008
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/j.1467-985x.2007.00530.x
Subject(s) - latent class model , class (philosophy) , context (archaeology) , econometrics , survey data collection , statistics , computer science , mathematics , artificial intelligence , geography , archaeology
Summary. Latent class analysis has been used to model measurement error, to identify flawed survey questions and to estimate mode effects. Using data from a survey of University of Maryland alumni together with alumni records, we evaluate this technique to determine its usefulness for detecting bad questions in the survey context. Two sets of latent class analysis models are applied in this evaluation: latent class models with three indicators and latent class models with two indicators under different assumptions about prevalence and error rates. Our results indicated that the latent class analysis approach produced good qualitative results for the latent class models—the item that the model deemed the worst was the worst according to the true scores. However, the approach yielded weaker quantitative estimates of the error rates for a given item.