{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T02:59:17Z","timestamp":1792205957854,"version":"4.4.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2013,10,17]],"date-time":"2013-10-17T00:00:00Z","timestamp":1381968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Sociological Methods &amp; Research"],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>For survey methodologists, latent class analysis (LCA) is a powerful tool for assessing the measurement error in survey questions, evaluating survey methods, and estimating the bias in estimates of population prevalence. LCA can be used when gold standard measurements are not available and applied to essentially any set of indicators that meet certain criteria for identifiability. LCA offers quality inference, provided the key threat to model validity\u2014namely, local dependence\u2014can be appropriately addressed either in the study design or in the model-building process. Three potential causes threaten local independence: bivocality, behaviorally correlated error, and latent heterogeneity. In this article, these threats are examined separately to obtain insights regarding (a) questionnaire designs that reduce local dependence, (b) the effects of local dependence on parameter estimation, and (c) modeling strategies to mitigate these effects in statistical inference. The article focuses primarily on the analysis of rare and sensitivity outcomes and proposes a practical approach for diagnosing and mitigating model failures. The proposed approach is empirically tested using real data from a national survey of inmate sexual abuse where measurement errors are a serious concern. Our findings suggest that the proposed modeling strategy was successful in reducing local dependence bias in the estimates, but its success varied by the quality of the indicators available for analysis. With only three indicators, the biasing effects of local dependence can usually be reduced but not always to acceptable levels.<\/jats:p>","DOI":"10.1177\/0049124113506407","type":"journal-article","created":{"date-parts":[[2013,10,17]],"date-time":"2013-10-17T21:53:03Z","timestamp":1382046783000},"page":"137-170","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Local Dependence in Latent Class Analysis of Rare and Sensitive Events"],"prefix":"10.1177","volume":"43","author":[{"given":"Marcus E.","family":"Berzofsky","sequence":"first","affiliation":[{"name":"RTI International, Research Triangle Park, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul P.","family":"Biemer","sequence":"additional","affiliation":[{"name":"RTI International, Research Triangle Park, NC, USA"},{"name":"The University of North Carolina, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William D.","family":"Kalsbeek","sequence":"additional","affiliation":[{"name":"The University of North Carolina, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2013,10,17]]},"reference":[{"key":"bibr1-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/9780470146316"},{"key":"bibr2-0049124113506407","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/01621459.1968.11009221","volume":"63","author":"Bailar Barbara A.","year":"1968","journal-title":"Journal of the American Statistical Association"},{"key":"bibr3-0049124113506407","doi-asserted-by":"crossref","unstructured":"Beck Allen J., Harrison Paige M. 2007. \u201cSexual Victimization in State and Federal Prisons Reported by Inmates, 2007.\u201d Bureau of Justice Special Rpt. No. NCJ 219414, U.S. Department of Justice, Office of Justice Programs. Washington, DC. Retrieved December 16, 2009 (http:\/\/www.ojp.usdoj.gov\/bjs\/pub\/pdf\/svsfpri07.pdf).","DOI":"10.1037\/e432632008-001"},{"key":"bibr4-0049124113506407","first-page":"417","volume":"20","author":"Biemer Paul P.","year":"2004","journal-title":"Journal of Official Statistics"},{"key":"bibr5-0049124113506407","volume-title":"Latent Class Analysis of Survey Error","author":"Biemer Paul P.","year":"2011"},{"key":"bibr6-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/0471458740"},{"key":"bibr7-0049124113506407","doi-asserted-by":"publisher","DOI":"10.2307\/2531499"},{"key":"bibr8-0049124113506407","doi-asserted-by":"crossref","unstructured":"Garrett Elizabeth S., Zeger Scott L. 2000. \u201cLatent Class Model Diagnosis.\u201d Biometrics 56:1055\u201367.","DOI":"10.1111\/j.0006-341X.2000.01055.x"},{"key":"bibr9-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/0471725277"},{"key":"bibr10-0049124113506407","volume-title":"Ensuring the Quality, Credibility, and Relevance of U.S. Justice Statistics. Panel to Review the Programs of the Bureau of Justice Statistics. Committee on National Statistics and Committee on Law and Justice, Division of Behavioral and Social Sciences and Education","author":"Groves Robert M.","year":"2008"},{"key":"bibr11-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1177\/0049124188016003002"},{"key":"bibr12-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/9781118596869"},{"key":"bibr13-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/acp.2350050305"},{"key":"bibr14-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1086\/269029"},{"key":"bibr15-0049124113506407","volume-title":"Latent Structure Analysis","author":"Lazarsfeld Paul F.","year":"1968"},{"key":"bibr16-0049124113506407","first-page":"175","volume-title":"Sage Handbook of Quantitative Methodology for Social Sciences,","author":"Magidson Jay","year":"2004"},{"key":"bibr17-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxl038"},{"key":"bibr18-0049124113506407","doi-asserted-by":"publisher","DOI":"10.2307\/2533043"},{"key":"bibr19-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3194"},{"key":"bibr20-0049124113506407","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511819322"},{"key":"bibr21-0049124113506407","volume-title":"LEM: A General Program for the Analysis of Categorical Data","author":"Vermunt Jeroen K","year":"1997"},{"key":"bibr22-0049124113506407","volume-title":"Panel Analysis, Latent Probability Models for Attitude and Behavior Processing","author":"Wiggins L. 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