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Journals / Teknik Dergi / 2020 / Cilt: 31 - Sayı: 2

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

Özge AKBOĞA KALE
Selim BARADAN
Pages
9919–9940
Publication Date
2020-03-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings of the study showed that work experience, accident type, unsafe condition, unsafe act have statistically significant influence on Injury Severity Score.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Consequently, majority of studies in occupational safety leaned towards describing accidents with the aid of surveys and descriptive statistics.
📊
Key Findings & Data:This study intends to fill this gap by using inferential statistics to identify the factors that contribute to severity of injuries.
💡
Conclusion & Impact:The findings of the study showed that work experience, accident type, unsafe condition, unsafe act have statistically significant influence on Injury Severity Score.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Occupational Safety and Health · Logistic Regression Analysis · Injury Severity Score

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.

Özet

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.

🔖

Cite This Article

KALE, Ö. A., BARADAN, S. (2020). Identifying Factors that Contribute to Severity of Construction Injuries using Logistic Regression Model. Teknik Dergi, Cilt: 31 - Sayı: 2, 9919–9940. https://ajindex.com/articles/51215

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