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Estimating latent class model parameters for filter questions with skip patterns

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Abstract

Filter questions with skip patterns have been widely used in survey research, and latent class models (LCM) are often used to analyze this type of categorical data. The LCM parameters are usually estimated by means of an EM (expectation maximization) algorithm. When the pattern is present, the non-response of the skip pattern cannot be treated as random missingness. We thus propose a modified algorithm to estimate the latent class parameters when non-response is present, and the approach is attractive for two reasons. First, the latent class model with the algorithm is very flexible in the sense that it can model the association of variables with the skip patterns under study. Secondly, the algorithm can be easily implemented using any computer language. An empirical example is used to demonstrate the usefulness of the algorithm. The algorithm may also be flexibly generalized to more complex surveys, for example, polytomous responses.

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References

  • Akaike H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csake, F. (eds) Second international symposium on information theory, pp. 267–281. Budapest, Akademiai Kiado (1973)

    Google Scholar 

  • Bozdogan H.: Model-selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987)

    Article  Google Scholar 

  • Dayton C.M., Macready G.B.: A probabilistic model for validation of behavioral hierarchies. Psychometrika 41, 189–204 (1976)

    Article  Google Scholar 

  • Dayton C.M., Macready G.B.: A scaling model with response errors and intrinsically unscalable respondents. Psychometrika 45, 343–356 (1980)

    Article  Google Scholar 

  • Dayton C.M., Macready G.B.: Concomitant-variable latent-class models. J. Am. Stat. Assoc. 83, 173–178 (1988)

    Article  Google Scholar 

  • Dempster A.P., Laird N.M., Rubin D.B.: Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Stat. Soc. B. 39, 1–38 (1977)

    Google Scholar 

  • Everitt B.S.: A Monte Carlo investigation of the likelihood ratio test for number of latent classes in latent class analysis. Multivariate Behav. Res. 23, 531–538 (1988)

    Article  Google Scholar 

  • Goodman L.A.: The analysis of multidimensional contingency tables: stepwise procedures and direct estimation methods for building models for multiple classifications. Technometrics 13, 33–61 (1971)

    Article  Google Scholar 

  • Goodman L.A.: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215–231 (1974)

    Article  Google Scholar 

  • Goodman L.A.: A new model for scaling response patterns: an application for quasi-independence concept. J. Am. Stat. Assoc. 70, 755–768 (1975)

    Article  Google Scholar 

  • Goodman L.A.: Analyzing qualitative/categorical data: log-linear models and latent structure analysis. Abt Books, Cambridge (1978)

    Google Scholar 

  • Hagenaars, J.A., McCutcheon, A.L. (eds): Applied Latent Class Analysis Models. Cambridge University Press, London (2002)

    Google Scholar 

  • Lazarsfeld P.F., Henry N.W.: Latent Structure Analysis. Houghton Mifflin, Boston (1968)

    Google Scholar 

  • Lin T.H., Dayton C.M.: Model selection information criteria for non-nested latent class analysis. J. Educ. Behav. Stat. 22, 249–264 (1997)

    Google Scholar 

  • Magidson J., Vermunt J.K.: Latent Class Factor and Cluster Models, Bi-plots and Related Graphical Displays. Sociological Sociol. Methodol. 31, 223–264 (2001)

    Article  Google Scholar 

  • National Center for Health Statistics: National Health Interview Survey. ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Survey_Questionnaires/NHIS/2004/english/QADULT.pdf (2004). Accessed 1 May 2007

  • Proctor C.H.: A probabilistic formation and statistical analysis of Guttman scaling. Psychometrika 35, 73–76 (1970)

    Article  Google Scholar 

  • Schafer J.L., Graham J.W.: Missing data: our view of the state of the art. Psychological Methods 7, 147–177 (2002)

    Article  Google Scholar 

  • Schwarz G.: Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978)

    Article  Google Scholar 

  • Ware J.E.J., Snow K.K., Kosinski M., Gandek B.: SF-36 Health Survey Manual and Interpretation Guide. Nimrod Press, Boston (1993)

    Google Scholar 

Download references

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  1. Department of Statistics, National Taipei University, Taipei, Taiwan, ROC

    Ting Hsiang Lin

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  1. Ting Hsiang Lin
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Correspondence to Ting Hsiang Lin.

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Lin, T.H. Estimating latent class model parameters for filter questions with skip patterns. Qual Quant 46, 545–552 (2012). https://doi.org/10.1007/s11135-010-9385-x

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