Identifying Factors that Contribute to Severity of Construction Injuries using Logistic Regression Model

Year 2020, Volume: 31 Issue: 2, 9919 - 9940, 01.03.2020

Özge Akboğa Kale , Selim Baradan

https://doi.org/10.18400/tekderg.470633

Cited By: 16

Abstract

Consequently, majority
of studies in occupational safety leaned towards describing accidents with the
aid of surveys and descriptive statistics. This
study intends to fill this gap by using inferential statistics to identify the
factors that contribute to severity of injuries. Subsequently, cooperation with Social Security
Institute of Turkey was achieved and an extensive archival study was performed.
The information acquired from open-ended questions in forms were reorganized to
be identified as variables. Categorically identified data set were analyzed statistically
by using binary logistic regression analyses. The findings of the study showed
that work experience, accident type, unsafe condition, unsafe act have
statistically significant influence on Injury Severity Score. 

Keywords

Occupational Safety and Health, Logistic Regression Analysis, Injury Severity Score, Construction Accidents, Data Mining

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