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

I. Sulis and M. Porcu

Working Paper CRENoS from Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia

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.

Keywords: mice; multiple imputation analysis; validation process; mar; mcar (search for similar items in EconPapers)
JEL-codes: C15 (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:cns:cnscwp:200804

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