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Assessing the Effectiveness of a Stochastic Regression Imputation Method for Ordered Categorical Data

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  • I. Sulis
  • M. Porcu

Abstract

The main aim of this paper is to describe a workable method based on stochastic regression and multiple imputation analysis (MISR) to recover for missingness in surveys where multi-item Likert-type scale are used to measure a latent attribute (namely, the quality of university teaching). A simulation analysis has been carried out and results have been compared in terms of bias and efficiency with other missing data handling methods, specifically - Complete Cases Analysis (CCA) and Multiple Imputation by Chained Equations (MICE). The authors provide also functions (implemented in R language) to apply the procedure to a matrix of ordered categorical items. Functions described allow - (i) to simulate missing data at random and completely at random; (ii) to replicate the simulation study presented in this work in order to assess the accuracy in distribution and in estimation of a multiple imputation procedure.

Suggested Citation

  • 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.
    • Handle: RePEc:cns:cnscwp:200804
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    mice; multiple imputation analysis; validation process; mar; mcar;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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