IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v41y2003i3-4p549-559.html
   My bibliography  Save this article

Latent class model diagnostics--a review and some proposals

Author

Listed:
  • Formann, Anton K.

Abstract

No abstract is available for this item.

Suggested Citation

  • Formann, Anton K., 2003. "Latent class model diagnostics--a review and some proposals," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 549-559, January.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:549-559
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00186-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mark Reiser, 1996. "Analysis of residuals for the multionmial item response model," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 509-528, September.
    2. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Subtil, Ana & de Oliveira, M. Rosário & Gonçalves, Luzia, 2012. "Conditional dependence diagnostic in the latent class model: A simulation study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1407-1412.

    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. Alberto Maydeu-Olivares & Rosa Montaño, 2013. "How Should We Assess the Fit of Rasch-Type Models? Approximating the Power of Goodness-of-Fit Statistics in Categorical Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 116-133, January.
    2. Vassilis Vasdekis & Silvia Cagnone & Irini Moustaki, 2012. "A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 425-441, July.
    3. 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.
    4. Yan Feng & Erpeng Liu & Zhang Yue & Qilin Zhang & Tiankuo Han, 2019. "The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005–2014," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    5. Korner-Nievergelt, Fränzi & Prévot, Céline & Hahn, Steffen & Jenni, Lukas & Liechti, Felix, 2017. "The integration of mark re-encounter and tracking data to quantify migratory connectivity," Ecological Modelling, Elsevier, vol. 344(C), pages 87-94.
    6. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
    7. Yun Li & Jeremy M.G. Taylor & Michael R. Elliott, 2010. "A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 523-531, June.
    8. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).
    9. 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.
    10. Gioacchino Fazio & Francesca Giambona & Erasmo Vassallo & Elli Vassiliadis, 2018. "A Measure of Trust: The Italian Regional Divide in a Latent Class Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(1), pages 209-242, November.
    11. Subtil, Ana & de Oliveira, M. Rosário & Gonçalves, Luzia, 2012. "Conditional dependence diagnostic in the latent class model: A simulation study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1407-1412.
    12. Wu, Jianmin & Bentler, Peter M., 2013. "Limited information estimation in binary factor analysis: A review and extension," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 392-403.
    13. Kenneth W. Griffin & Lawrence M. Scheier & Bianca Acevedo & Jerry L. Grenard & Gilbert J. Botvin, 2011. "Long-Term Effects of Self-Control on Alcohol Use and Sexual Behavior among Urban Minority Young Women," IJERPH, MDPI, vol. 9(1), pages 1-23, December.
    14. Yanlou Liu & Wei Tian & Tao Xin, 2016. "An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models," Journal of Educational and Behavioral Statistics, , vol. 41(1), pages 3-26, February.
    15. Bei Jiang & Michael R. Elliott & Mary D. Sammel & Naisyin Wang, 2015. "Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances," Biometrics, The International Biometric Society, vol. 71(2), pages 487-497, June.
    16. Chia-Yi Chiu & Yan Sun & Yanhong Bian, 2018. "Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 355-375, June.
    17. Brian Neelon & A. James O'Malley & Sharon-Lise T. Normand, 2011. "A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity," Biometrics, The International Biometric Society, vol. 67(1), pages 280-289, March.
    18. Jing Huang & Ying Yuan & David Wetter, 2019. "Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 1-18, March.
    19. Lee, Jung Wun & Chung, Hwan & Jeon, Saebom, 2021. "Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    20. Beth A. Reboussin & Nicholas S. Ialongo, 2010. "Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 145-164, January.

    More about this item

    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:eee:csdana:v:41:y:2003:i:3-4:p:549-559. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.