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Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis

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  • Isabella Sulis

    (Università degli Studi di Cagliari)

  • Mariano Porcu

    (Università degli Studi di Cagliari)

Abstract

A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on this topic in the framework of item response theory have shown that imputation procedures are in general associated with more accurate estimates of item location and discrimination parameters under several missing data generating mechanisms. This paper proposes a model-based multiple imputation procedure for multiple categorical items (dichotomous, multinomial or Likert-type) which relies on the results of latent class analysis to impute missing item responses. The effectiveness of the proposed technique is assessed in the estimation of item response theory parameters using a range of ad hoc measures. The accuracy of the method is assessed with respect to other single and multiple imputation procedures, under different missing data generating mechanisms and different rate of missingness (5% to 30%). The simulation results indicate that the proposed technique performs satisfactorily under all conditions and has the greatest potential with severe rates of missingness and under non ignorable missing data mechanisms. The method was implemented in R code with a function that calls scripts from a latent class analysis routine.

Suggested Citation

  • Isabella Sulis & Mariano Porcu, 2017. "Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 327-359, July.
    • Handle: RePEc:spr:jclass:v:34:y:2017:i:2:d:10.1007_s00357-017-9220-3
    DOI: 10.1007/s00357-017-9220-3
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    3. I. Sulis & M. Porcu, 2008. "Assessing the Effectiveness of a Stochastic Regression Imputation Method for Ordered Categorical Data," Working Paper CRENoS 200804, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Maurizio Carpita & Marica Manisera, 2011. "On the Imputation of Missing Data in Surveys with Likert-Type Scales," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 93-112, April.
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    Cited by:

    1. Isabella Sulis & Mariano Porcu & Vincenza Capursi, 2019. "On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1305-1331, April.
    2. Pennoni Fulvia & Nakai Miki, 2019. "A latent class analysis towards stability and changes in breadwinning patterns among coupled households," Dependence Modeling, De Gruyter, vol. 7(1), pages 234-246, January.
    3. Douglas L. Steinley, 2018. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 1-4, April.
    4. Lukasz Struski & Marek Śmieja & Jacek Tabor, 2020. "Pointed Subspace Approach to Incomplete Data," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 42-57, April.
    5. Sandip Sinharay, 2022. "Reporting Proficiency Levels for Examinees With Incomplete Data," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 263-296, June.

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