IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v16y1988i3p379-405.html
   My bibliography  Save this article

Latent Structure Models with Direct Effects between Indicators

Author

Listed:
  • JACQUES A. HAGENAARS

    (Tilburg University)

Abstract

A basic assumption of latent structure models is that of local independence: given the score on the latent variable, the scores on the manifest variables are independent of each other. This basic assumption is violated when test-retest effects, response consistency effects, correlated response errors, and so forth are present. However, it is possible to reformulate the latent class model in such a way that these direct relations between the indicators (manifest variables) can be accounted for. The reformulation proposed here, takes place within the framework of log-linear modeling.

Suggested Citation

  • Jacques A. Hagenaars, 1988. "Latent Structure Models with Direct Effects between Indicators," Sociological Methods & Research, , vol. 16(3), pages 379-405, February.
    • Handle: RePEc:sae:somere:v:16:y:1988:i:3:p:379-405
    DOI: 10.1177/0049124188016003002
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124188016003002
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124188016003002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jacques Hagenaars, 1978. "Latent probability models with direct effects between indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 12(3), pages 205-222, September.
    2. C. Mitchell Dayton & George Macready, 1976. "A probabilistic model for validation of behavioral hierarchies," Psychometrika, Springer;The Psychometric Society, vol. 41(2), pages 189-204, June.
    3. Dean Harper, 1972. "Local dependence latent structure models," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 53-59, March.
    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. Morey, Edward R. & Thiene, Mara, 2017. "Can Personality Traits Explain Where and With Whom You Recreate? A Latent-Class Site-Choice Model Informed by Estimates From Mixed-Mode LC Cluster Models With Latent-Personality Traits," Ecological Economics, Elsevier, vol. 138(C), pages 223-237.
    2. Willem E. Saris & Melanie Revilla, 2016. "Correction for Measurement Errors in Survey Research: Necessary and Possible," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 1005-1020, July.
    3. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2016. "Latent class model with conditional dependency per modes to cluster categorical data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 183-207, June.
    4. Forcina, Antonio, 2017. "A Fisher-scoring algorithm for fitting latent class models with individual covariates," Econometrics and Statistics, Elsevier, vol. 3(C), pages 132-140.
    5. 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.

    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. Yih-Ing Hser & Haikang Shen & Chih-Ping Chou & Stephen C. Messer & M. Douglas Anglin, 2001. "Analytic Approaches for Assessing Long-Term Treatment Effects," Evaluation Review, , vol. 25(2), pages 233-262, April.
    2. David Rindskopf, 1983. "A general framework for using latent class analysis to test hierarchical and nonhierarchical learning models," Psychometrika, Springer;The Psychometric Society, vol. 48(1), pages 85-97, March.
    3. Clifford Clogg & Leo Goodman, 1986. "On scaling models applied to data from several groups," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 123-135, March.
    4. Frank Van De Pol & Jan De Leeuw, 1986. "A Latent Markov Model to Correct for Measurement Error," Sociological Methods & Research, , vol. 15(1-2), pages 118-141, November.
    5. repec:jss:jstsof:37:i02 is not listed on IDEAS
    6. Lewis Price & C. Dayton & George Macready, 1980. "Discovery algorithms for hierarchical relations," Psychometrika, Springer;The Psychometric Society, vol. 45(4), pages 449-465, December.
    7. James W. Shockey, 1988. "Adjusting for Response Error in Panel Surveys," Sociological Methods & Research, , vol. 17(1), pages 65-92, August.
    8. Rand Wilcox, 1983. "An approximation of theK outN reliability of a test, and a scoring procedure for determining which items an examinee knows," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 211-222, June.
    9. Dereje W. Gudicha & Fetene B. Tekle & Jeroen K. Vermunt, 2016. "Power and Sample Size Computation for Wald Tests in Latent Class Models," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 30-51, April.
    10. Edward Ip, 2002. "Locally dependent latent trait model and the dutch identity revisited," Psychometrika, Springer;The Psychometric Society, vol. 67(3), pages 367-386, September.
    11. Hector E. Najera Catalan, 2017. "Multiple Deprivation, Severity and Latent Sub-Groups: Advantages of Factor Mixture Modelling for Analysing Material Deprivation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(2), pages 681-700, March.
    12. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "Generalized multilevel structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 167-190, June.
    13. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    14. 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.
    15. Lin Ting Hsiang, 2006. "A comparison of model selection indices for nested latent class models," Monte Carlo Methods and Applications, De Gruyter, vol. 12(3), pages 239-259, October.
    16. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
    17. Kumar, Sunil & Husain, Zakir & Mukherjee, Diganta, 2017. "Assessing consistency of consumer confidence data using latent class analysis with time factor," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 35-46.
    18. Anton Formann, 1988. "Latent class models for nonmonotone dichotomous items," Psychometrika, Springer;The Psychometric Society, vol. 53(1), pages 45-62, March.
    19. Unlu, Ali & Albert, Dietrich, 2004. "The Correlational Agreement Coefficient CA(D)--a mathematical analysis of a descriptive goodness-of-fit measure," Mathematical Social Sciences, Elsevier, vol. 48(3), pages 281-314, November.
    20. Pankaj Patel & Sherry Thatcher & Katerina Bezrukova, 2013. "Organizationally-relevant configurations: the value of modeling local dependence," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 287-311, 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:sae:somere:v:16:y:1988:i:3:p:379-405. 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: SAGE Publications (email available below). General contact details of provider: .

    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.