In view of the limitations of univariate statistics for studying construction accidents, a multivariate approach was undertaken using crosstabulation analysis and logistic regression.
Heavy construction equipment accidents related data for four type of equipment; backhoe, bulldozer, excavator and scraper were incorporated in the study using categorical variables. Degree of injury indicating the severity of accident outcome (fatal vs. nonfatal) was selected as the dependent variable, and a variety of factors potentially affecting the outcome comprised the independent variables. Cross tabulation results enabled the understanding and evaluation of associations between the research variables, while logistic regression yielded predictive models that helped describe accident severity in terms of the contributing factors. Factors increasing or decreasing the odds of accident severity (degree of injury) in the presence or absence of various factors were identified and quantified. It was concluded that multivariate analysis serves as a much more powerful tool than univariate methods in eliciting information from construction accident data. Union status of workers and the safety training they were provided according to OSHA guidelines vastly affect the degree of injury and lessen the odds of fatality.
","abstract_html":"<p>In view of the limitations of univariate statistics for studying construction accidents, a multivariate approach was undertaken using crosstabulation analysis and logistic regression. </p> <p>Heavy construction equipment accidents related data for four type of equipment; backhoe, bulldozer, excavator and scraper were incorporated in the study using categorical variables. Degree of injury indicating the severity of accident outcome (fatal vs. nonfatal) was selected as the dependent variable, and a variety of factors potentially affecting the outcome comprised the independent variables. Cross tabulation results enabled the understanding and evaluation of associations between the research variables, while logistic regression yielded predictive models that helped describe accident severity in terms of the contributing factors. Factors increasing or decreasing the odds of accident severity (degree of injury) in the presence or absence of various factors were identified and quantified. It was concluded that multivariate analysis serves as a much more powerful tool than univariate methods in eliciting information from construction accident data. Union status of workers and the safety training they were provided according to OSHA guidelines vastly affect the degree of injury and lessen the odds of fatality.</p>","abstract_has_math":false,"creators":["Kazan, Esref Emrah"],"institution":null,"degree_name":"Ph.D.","degree_level":"Open Access Dissertation","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":["Mumtaz A. Usmen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T05:59:26Z","subjects":["Accident Analysis","Construction Safety","Crosstabulation","Heavy Construction Equipment Operator","Logistic Regression","Predictive Model","Civil Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wayne.edu/oa_dissertations/731","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mumtaz A. Usmen"]},{"key":"dc:creator","label":"Author","values":["Kazan, Esref Emrah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Accident Analysis","Construction Safety","Crosstabulation","Heavy Construction Equipment Operator","Logistic Regression","Predictive Model","Civil Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wayne.edu/oa_dissertations/731"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In view of the limitations of univariate statistics for studying construction accidents, a multivariate approach was undertaken using crosstabulation analysis and logistic regression.
Heavy construction equipment accidents related data for four type of equipment; backhoe, bulldozer, excavator and scraper were incorporated in the study using categorical variables. Degree of injury indicating the severity of accident outcome (fatal vs. nonfatal) was selected as the dependent variable, and a variety of factors potentially affecting the outcome comprised the independent variables. Cross tabulation results enabled the understanding and evaluation of associations between the research variables, while logistic regression yielded predictive models that helped describe accident severity in terms of the contributing factors. Factors increasing or decreasing the odds of accident severity (degree of injury) in the presence or absence of various factors were identified and quantified. It was concluded that multivariate analysis serves as a much more powerful tool than univariate methods in eliciting information from construction accident data. Union status of workers and the safety training they were provided according to OSHA guidelines vastly affect the degree of injury and lessen the odds of fatality.
"]},{"key":"dc:title","label":"Title","values":["Analysis Of Fatal And Nonfatal Accidents Involving Earthmoving Equipment Operators And On-Foot Workers"]}]}],"canonical_facts":{"dc:contributor":["Mumtaz A. Usmen"],"dc:creator":["Kazan, Esref Emrah"],"dc:date.available":["2013-01-01T08:00:00Z"],"dc:description.abstract":["In view of the limitations of univariate statistics for studying construction accidents, a multivariate approach was undertaken using crosstabulation analysis and logistic regression.
Heavy construction equipment accidents related data for four type of equipment; backhoe, bulldozer, excavator and scraper were incorporated in the study using categorical variables. Degree of injury indicating the severity of accident outcome (fatal vs. nonfatal) was selected as the dependent variable, and a variety of factors potentially affecting the outcome comprised the independent variables. Cross tabulation results enabled the understanding and evaluation of associations between the research variables, while logistic regression yielded predictive models that helped describe accident severity in terms of the contributing factors. Factors increasing or decreasing the odds of accident severity (degree of injury) in the presence or absence of various factors were identified and quantified. It was concluded that multivariate analysis serves as a much more powerful tool than univariate methods in eliciting information from construction accident data. Union status of workers and the safety training they were provided according to OSHA guidelines vastly affect the degree of injury and lessen the odds of fatality.
"],"dc:identifier":["https://digitalcommons.wayne.edu/oa_dissertations/731"],"dc:subject":["Accident Analysis","Construction Safety","Crosstabulation","Heavy Construction Equipment Operator","Logistic Regression","Predictive Model","Civil Engineering"],"dc:title":["Analysis Of Fatal And Nonfatal Accidents Involving Earthmoving Equipment Operators And On-Foot Workers"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T05:59:26Z"}