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Assessing the effectiveness of a sthocastic regression imputation method for ordered categorical data

Sulis , Isabella - Porcu, Mariano (2008) Assessing the effectiveness of a sthocastic regression imputation method for ordered categorical data. Working Paper. CRENoS.

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Official URL: http://www.crenos.it/working/pdf/08-04.pdf

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.

Item Type:Technical Report / Working Paper / Project Report (Working Paper)
Publisher:CRENoS
Date:March 2008
Institution:Universita' degli Studi di Cagliari
Divisions:Centri > CRENoS Centro Ricerche Economiche Nord Sud
Subjects:Area 13 - Scienze economiche e statistiche > SECS-P/06 Economia applicata
Area 13 - Scienze economiche e statistiche > SECS-S/05 Statistica sociale
Uncontrolled Keywords:Multiple Imputation Analysis, Validation Process, MAR, MCAR, MICE
ID Code:195
Deposited On:07 Nov 2008 10:38

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