| Autor: |
RZEPKA, Agnieszka, KIERSZTYN, Adam, MIŚKIEWICZ, Radosław, KIERSZTYN, Krystyna |
| Předmět: |
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| Zdroj: |
Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie; 2023, Issue 184, p425-440, 16p |
| Abstrakt: |
Purpose: Surveys are one of the most popular data acquisition tools used in economics and management sciences. The results of surveys provide a lot of information and allow for fast response to changes in the socio-economic environment. Unfortunately, in many cases there are missing data in surveys, which can be caused by various reasons. Design/methodology/approach: One of the most common reasons are the respondent’s reluctance to provide an answer or distraction while completing the questionnaire. This study presents a novel approach for filling gaps in the survey data. Findings: The main idea of the proposed method is to use the associations between the answers to given sets of questions for different respondents. Originality/value: The obtained association rules were used as input variables and a number of well-known machine learning tools were applied for filling data gaps. The results of numerical experiments confirmed a very high performance of the proposed novel method for filling data gaps in surveys. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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