Claudio Quintano (Author), Rosalia Castellano (Author), Antonella Rocca (Author)

Abstract

In the field of data quality, imputation is the most used method for handling missing data. The performance of imputation techniques is influenced by various factors, especially when data represent only a sample of population, for example the survey design characteristics. In this paper, we compare the results of different multiple imputation methods in terms of final estimates when outliers occur in a dataset. Consequently, in order to evaluate the influence of outliers on the performance of these methods, the procedure is applied before and after that we have identified and removed them. For this purpose, missing data were simulated on data coming from sample ISTAT annual survey on Small and Medium Enterprises. MAR mechanism is assumed for missing data. The methods are based on the multiple imputation through the Markov Chain Monte Carlo (MCMC), the propensity score and the mixture models. The results highlight the strong influence of data characteristics on final estimates.

Keywords

Ankete;Metodologija;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FDV - Faculty of Social Sciences
Publisher: Fakulteta za družbene vede
UDC: 303
COBISS: 29643869 Link will open in a new window
ISSN: 1854-0023
Views: 590
Downloads: 171
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Unknown
Secondary keywords: Surveys;Methodology;
URN: URN:NBN:SI
Type (COBISS): Not categorized
Pages: str. 1-16
Volume: ǂVol. ǂ7
Issue: ǂno. ǂ1
Chronology: 2010
Keywords (UDC): social sciences;družbene vede;methods of the social sciences;metode družbenih ved;
ID: 1470504
Recommended works:
, no subtitle data available
, magistrsko delo
, no subtitle data available
, znanje, o katerem se razpravlja
, no subtitle data available