eprintid: 691
rev_number: 9
eprint_status: archive
userid: 14
dir: disk0/00/00/06/91
datestamp: 2016-05-16 13:20:05
lastmod: 2016-05-16 13:20:05
status_changed: 2016-05-16 13:20:05
type: report
metadata_visibility: show
creators_name: Kolev, V.
creators_name: Noncheva, V.
creators_name: Valkov, V.
creators_name: Ilieva, E.
creators_name: Dobreva, M.
title: Direct Ascription of Missing Categorical Values in Survey Research Data
ispublished: pub
subjects: data
subjects: decision
subjects: retail
studygroups: esgi113
full_text_status: public
abstract: The complete datasets are a prerequisite for sustainable analyses, robust an- alytics and unbiased interpretation of results. Missing values in a survey occur when no data value is stored for the variable in an observation. Missing data can have a significant effect on the conclusions that can be drawn from the data. Direct ascription is the process of replacing missing data with predicted values. The aim of this work is to describe an approach to direct ascription of missing categorical values in survey research data based both on the assumption that values in a data set are missing at random and on the implementation of the correspondence analysis.
date: 2014
citation:   Kolev, V. and Noncheva, V. and Valkov, V. and Ilieva, E. and Dobreva, M.  (2014) Direct Ascription of Missing Categorical Values in Survey Research Data.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/691/1/Direct%20Ascription%20of%20Missing%20Categorical%20Values%20in%20Survey%20Research%20Data.pdf