IDEAS home Printed from https://ideas.repec.org/a/isa/journl/v12y2010i1p6-16.html
   My bibliography  Save this article

Evaluating reliability of combined responses through latent class models

Author

Listed:
  • Marcello D’Orazio

    (Italian National Institute of Statistics)

Abstract

The evaluation of the potential impact of the response errors on the final survey estimates requires ad hoc studies. Often these studies consist in additional reinterview surveys: a subsample of the respondent units at the main survey is interviewed again. In such cases, the evaluation can be done by means of the theory introduced by Hansen et al.(1964) and further investigated in Biemer and Forsman (1992). More recent studies (Biemer, 2004) present an approach based on the fitting of latent class models. These models allow for a more detailed analysis of the impact of response errors on the final survey estimates but, on the other hand, they require some additional assumptions to hold. In this paper, the usage of latent class models is extended to tackle the case of couples of survey questions involved in a questionnaire skip. An application of such models to the data of the control survey on the 2001 Population and Housing Census in presented.

Suggested Citation

  • Marcello D’Orazio, 2010. "Evaluating reliability of combined responses through latent class models," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(1), pages 6-16, April.
    • Handle: RePEc:isa:journl:v:12:y:2010:i:1:p:6-16
    as

    Download full text from publisher

    File URL: http://www.istat.it/it/files/2011/05/1_2010_01.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guan-Hua Huang & Karen Bandeen-Roche, 2004. "Building an identifiable latent class model with covariate effects on underlying and measured variables," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 5-32, March.
    2. Patterson B.H. & Dayton C.M. & Graubard B.I., 2002. "Latent Class Analysis of Complex Sample Survey Data: Application to Dietary Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 721-741, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kai Hong & Peter A. Savelyev & Kegon T. K. Tan, 2020. "Understanding the Mechanisms Linking College Education with Longevity," Journal of Human Capital, University of Chicago Press, vol. 14(3), pages 371-400.
    2. Daeyoung Kim & Bruce Lindsay, 2015. "Empirical identifiability in finite mixture models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 745-772, August.
    3. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. Forcina, Antonio, 2008. "Identifiability of extended latent class models with individual covariates," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5263-5268, August.
    5. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    6. Brisa N. Sánchez & Shan Kang & Bhramar Mukherjee, 2012. "A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 466-476, June.
    7. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    8. Jeroen K. Vermunt & Jay Magidson, 2007. "Latent Class Analysis With Sampling Weights," Sociological Methods & Research, , vol. 36(1), pages 87-111, August.
    9. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.
    10. Janne Petersen & Karen Bandeen-Roche & Esben Budtz-Jørgensen & Klaus Groes Larsen, 2012. "Predicting Latent Class Scores for Subsequent Analysis," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 244-262, April.
    11. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    12. Bijmolt, T.H.A. & Paas, L.J. & Vermunt, J.K., 2003. "Country and Consumer Segmentation : Multi-Level Latent Class Analysis of Financial Product Ownership," Other publications TiSEM 4fa5ac68-96cf-47ca-bc95-b, Tilburg University, School of Economics and Management.
    13. Li Donni, P., 2010. "Risk Preference Heterogeneity And Multiple Demand For Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 10/17, HEDG, c/o Department of Economics, University of York.
    14. Dardanoni, Valentino & Li Donni, Paolo, 2012. "Incentive and selection effects of Medigap insurance on inpatient care," Journal of Health Economics, Elsevier, vol. 31(3), pages 457-470.
    15. Jia-Chiun Pan & Guan-Hua Huang, 2014. "Bayesian Inferences of Latent Class Models with an Unknown Number of Classes," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 621-646, October.
    16. Guan-Hua Huang & Su-Mei Wang & Chung-Chu Hsu, 2011. "Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 584-611, October.
    17. Marino, Maria & Donni, Paolo Li & Bavetta, Sebastiano & Cellini, Marco, 2020. "The democratization process: An empirical appraisal of the role of political protest," European Journal of Political Economy, Elsevier, vol. 63(C).
    18. Skinner, Chris J., 2018. "Analysis of categorical data for complex surveys," LSE Research Online Documents on Economics 89707, London School of Economics and Political Science, LSE Library.
    19. Matias Iaryczower & Gabriel Katz, 2016. "What does IT Take for Congress to Enact Good Policies? an Analysis of Roll Call Voting in the US Congress," Economics and Politics, Wiley Blackwell, vol. 28(1), pages 79-104, March.
    20. Leonor Costa & José Dias, 2015. "What do Europeans Believe to be the Causes of Poverty? A Multilevel Analysis of Heterogeneity Within and Between Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(1), pages 1-20, May.

    More about this item

    Keywords

    Response Errors; Simple Response Variance; Latent Class Models; Questionnaire Skip;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:isa:journl:v:12:y:2010:i:1:p:6-16. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stefania Rossetti (email available below). General contact details of provider: https://edirc.repec.org/data/istgvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.