Show simple item record

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

dc.contributor.authorKreuter, Fraukeen_US
dc.contributor.authorYan, Tingen_US
dc.contributor.authorTourangeau, Rogeren_US
dc.date.accessioned2010-06-01T18:26:59Z
dc.date.available2010-06-01T18:26:59Z
dc.date.issued2008-06en_US
dc.identifier.citationKreuter, Frauke; Yan, Ting; Tourangeau, Roger (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(3): 723-738. <http://hdl.handle.net/2027.42/71657>en_US
dc.identifier.issn0964-1998en_US
dc.identifier.issn1467-985Xen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/71657
dc.format.extent594983 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights© 2008 The Royal Statistical Society and Blackwell Publishing Ltden_US
dc.subject.otherItem Developmenten_US
dc.subject.otherLatent Class Analysisen_US
dc.subject.otherQuestionnaire Designen_US
dc.titleGood item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questionsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, and University of Maryland, College Park, USAen_US
dc.contributor.affiliationotherUniversity of Maryland, College Park, USAen_US
dc.contributor.affiliationotherNational Opinion Research Center, Chicago, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/71657/1/j.1467-985X.2007.00530.x.pdf
dc.identifier.doi10.1111/j.1467-985X.2007.00530.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series A (Statistics in Society)en_US
dc.identifier.citedreferenceAmerican Association for Public Opinion Research ( 2000 ) Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. Lenexa: American Association for Public Opinion Research.en_US
dc.identifier.citedreferenceBiemer, P. P. ( 2004 ) Modeling measurement error to identify flawed questions. In Methods for Testing and Evaluating Survey Questionnaires ( eds S. Presser, J. Rothgeb, M. Couper, J. Lessler, E. Martin, J. Martin and E. Singer ), pp. 225 – 246. New York: Wiley.en_US
dc.identifier.citedreferenceBiemer, P. P. and Wiesen, C. ( 2002 ) Measurement error evaluation of self-reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse. J. R. Statist. Soc. A, 165, 97 – 119.en_US
dc.identifier.citedreferenceBiemer, P. P. and Witt, M. ( 1996 ) Estimation of measurement bias in self-reports of drug use with applications to the national household survey on drug abuse. J. Off. Statist., 12, 275 – 300.en_US
dc.identifier.citedreferenceClogg, C. C. and Goodman, L. A. ( 1984 ) Latent structure analysis of a set of multidimensional contingency tables. J. Am. Statist. Ass., 79, 762 – 771.en_US
dc.identifier.citedreferenceConrad, F. and Blair, J. ( 2004 ) Data quality in cognitive interviews: the case of verbal reports. In Methods for Testing and Evaluating Survey Questionnaires ( eds S. Presser, J. Rothgeb, M. Couper, J. Lessler, E. Martin, J. Martin and E. Singer ), pp. 67 – 87. New York: Wiley.en_US
dc.identifier.citedreferenceHui, S. L. and Walter, S. D. ( 1980 ) Estimating the error rates of diagnostic tests. Biometrics, 36, 167 – 171.en_US
dc.identifier.citedreferenceLazarsfeld, P. F. and Henry, N. W. ( 1968 ) Latent Structure Analysis. Boston: Houghton Mifflin.en_US
dc.identifier.citedreferenceMcCutcheon, A. L. ( 1987 ) Latent Class Analysis. Beverly Hills: Sage.en_US
dc.identifier.citedreferenceMuthÉn, B. ( 2004 ) Mplus: Statistical Analysis with Latent Variables, technical appendices. Los Angeles: MuthÉn and MuthÉn.en_US
dc.identifier.citedreferenceMuthÉn, L. K. and MuthÉn, B. ( 2004 ) Mplus User's Guide. Los Angeles: MuthÉn and MuthÉn.en_US
dc.identifier.citedreferencePresser, S., Rothgeb, J., Couper, M., Lessler, J., Martin, E., Martin, J. and Singer, E. ( eds ) ( 2004 ) Methods for Testing and Evaluating Survey Questionnaires. New York: Wiley.en_US
dc.identifier.citedreferenceRamaswamy, V., DeSarbo, W., Reibstein, D. and Robinson, W. ( 1993 ) An empirical pooling approach for estimating marketing mix elasticities with pims data. Marktng Sci., 12, 103 – 124.en_US
dc.identifier.citedreferenceSchwarz, G. ( 1978 ) Estimating the dimension of a model. Ann. Statist., 6, 461 – 464.en_US
dc.identifier.citedreferenceSclove, L. S. ( 1987 ) Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333 – 343.en_US
dc.identifier.citedreferenceTourangeau, R., Rips, L. and Rasinski, K. ( 2000 ) The Psychology of Survey Response. Cambridge: Cambridge University Press.en_US
dc.identifier.citedreferenceYang, C. C. ( 1998 ) Finite mixture model selection with psychometrics application. PhD Dissertation. University of California, Los Angeles.en_US
dc.identifier.citedreferenceYang, C. C. ( 2006 ) Evaluating latent class analysis models in qualitative phenotype identification. Computnl Statist. Data Anal., 50, 1090 – 1104.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.