Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Smock cited in story on how low marriage rates may exacerbate marriage-status economic inequality

Shapiro says Americans' seemingly volatile spending pattern linked to 'sensible cash management'

Work of Cigolle, Ofstedal et al. cited in Forbes story on frailty risk among the elderly

Highlights

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

ISR's program in Society, Population, and Environment (SPE) focuses on social change and social issues worldwide.

McEniry and Schoeni host Conference on Long-run Impacts of Early Life Events

Next Brown Bag


PSC Brown Bags will return in the fall

Good item or bad-can latent class analysis tell? the utility of latent class analysis for the evaluation of survey questions

Archived Abstract of Former PSC Researcher

Kreuter, F., T. Yan, and Roger Tourangeau. 2008. "Good item or bad-can latent class analysis tell? the utility of latent class analysis for the evaluation of survey questions." Journal of the Royal Statistical Society Series a-Statistics in Society, 171:723-738.

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.

DOI:10.1111/j.1467-985X.2007.00530.x (Full Text)

Browse | Search : All Pubs | Next