This study hoped to answer two questions: first, when trying to determine on what days of the week saw an increase in reported injuries within the central region of XYZ Distribution, could a small, random sample be pulled from the region, and proved to be statistically similar to the region as a whole, allowing it to be studied with accuracy; and two, was there a significant difference in the number of injuries when comparing days of the week that could allow the company to focus their resources in an attempt to lower incident rates? In order to answer those questions, 375 random cases were selected from a total of over 17,000 injuries covering a 30 month span, and statistical analysis of variance was applied.
For the first test, I measured to see if the random selection of participants was a valid data set by performing a one way analysis of variance to measure statistical significance. The ANOVA showed a significance level of .277, well above the alpha level set of .05, meaning there is no statistically significant difference between the two data sets. The second analysis of variance was used to measure the difference in injuries reported on different days of the week. The results showed all five days measured falling well within the 95% confidence interval for mean, meaning that there was no significant difference for the amount of injuries reported when looking at days of the week. To conclude, a Scheffe post-hoc test was performed, and confirmed that not only is there no difference when measuring injuries on days of the week, there's not even a statistically significant difference when measuring the day that saw the most injuries reported against the day that saw the least amount of injuries reported.
","abstract_html":"<p>This study hoped to answer two questions: first, when trying to determine on what days of the week saw an increase in reported injuries within the central region of XYZ Distribution, could a small, random sample be pulled from the region, and proved to be statistically similar to the region as a whole, allowing it to be studied with accuracy; and two, was there a significant difference in the number of injuries when comparing days of the week that could allow the company to focus their resources in an attempt to lower incident rates? In order to answer those questions, 375 random cases were selected from a total of over 17,000 injuries covering a 30 month span, and statistical analysis of variance was applied. </p> <p>For the first test, I measured to see if the random selection of participants was a valid data set by performing a one way analysis of variance to measure statistical significance. The ANOVA showed a significance level of .277, well above the alpha level set of .05, meaning there is no statistically significant difference between the two data sets. The second analysis of variance was used to measure the difference in injuries reported on different days of the week. The results showed all five days measured falling well within the 95% confidence interval for mean, meaning that there was no significant difference for the amount of injuries reported when looking at days of the week. To conclude, a Scheffe post-hoc test was performed, and confirmed that not only is there no difference when measuring injuries on days of the week, there's not even a statistically significant difference when measuring the day that saw the most injuries reported against the day that saw the least amount of injuries reported. </p>","abstract_has_math":false,"creators":["Turner, Michael D."],"institution":"Eastern Kentucky University","degree_name":"Master of Science (MS)","degree_level":"Master's","degree_discipline":"Safety, Security, and Emergency Management","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T02:15:25Z","subjects":["Days","Injuries","Rate","Occupational Health and Industrial Hygiene"],"languages":[],"rights":["Copyright 2014 Michael D. Turner"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://encompass.eku.edu/etd/233","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Turner, Michael D."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Encompass Digital Archive, Eastern Kentucky University"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Safety, Security, and Emergency Management"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Eastern Kentucky University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Days","Injuries","Rate","Occupational Health and Industrial Hygiene"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Michael D. Turner"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://encompass.eku.edu/etd/233"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study hoped to answer two questions: first, when trying to determine on what days of the week saw an increase in reported injuries within the central region of XYZ Distribution, could a small, random sample be pulled from the region, and proved to be statistically similar to the region as a whole, allowing it to be studied with accuracy; and two, was there a significant difference in the number of injuries when comparing days of the week that could allow the company to focus their resources in an attempt to lower incident rates? In order to answer those questions, 375 random cases were selected from a total of over 17,000 injuries covering a 30 month span, and statistical analysis of variance was applied.
For the first test, I measured to see if the random selection of participants was a valid data set by performing a one way analysis of variance to measure statistical significance. The ANOVA showed a significance level of .277, well above the alpha level set of .05, meaning there is no statistically significant difference between the two data sets. The second analysis of variance was used to measure the difference in injuries reported on different days of the week. The results showed all five days measured falling well within the 95% confidence interval for mean, meaning that there was no significant difference for the amount of injuries reported when looking at days of the week. To conclude, a Scheffe post-hoc test was performed, and confirmed that not only is there no difference when measuring injuries on days of the week, there's not even a statistically significant difference when measuring the day that saw the most injuries reported against the day that saw the least amount of injuries reported.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:source","label":"Dc Source","values":["Encompass Digital Archive: Online Theses and Dissertations"]},{"key":"dc:title","label":"Title","values":["An Examination of the Day of Week and Sampling on Injury Occurrence"]}]}],"canonical_facts":{"dc:creator":["Turner, Michael D."],"dc:description.abstract":["This study hoped to answer two questions: first, when trying to determine on what days of the week saw an increase in reported injuries within the central region of XYZ Distribution, could a small, random sample be pulled from the region, and proved to be statistically similar to the region as a whole, allowing it to be studied with accuracy; and two, was there a significant difference in the number of injuries when comparing days of the week that could allow the company to focus their resources in an attempt to lower incident rates? In order to answer those questions, 375 random cases were selected from a total of over 17,000 injuries covering a 30 month span, and statistical analysis of variance was applied.
For the first test, I measured to see if the random selection of participants was a valid data set by performing a one way analysis of variance to measure statistical significance. The ANOVA showed a significance level of .277, well above the alpha level set of .05, meaning there is no statistically significant difference between the two data sets. The second analysis of variance was used to measure the difference in injuries reported on different days of the week. The results showed all five days measured falling well within the 95% confidence interval for mean, meaning that there was no significant difference for the amount of injuries reported when looking at days of the week. To conclude, a Scheffe post-hoc test was performed, and confirmed that not only is there no difference when measuring injuries on days of the week, there's not even a statistically significant difference when measuring the day that saw the most injuries reported against the day that saw the least amount of injuries reported.
"],"dc:format":["application/pdf"],"dc:identifier":["https://encompass.eku.edu/etd/233"],"dc:publisher":["Encompass Digital Archive, Eastern Kentucky University"],"dc:rights":["Copyright 2014 Michael D. Turner"],"dc:source":["Encompass Digital Archive: Online Theses and Dissertations"],"dc:subject":["Days","Injuries","Rate","Occupational Health and Industrial Hygiene"],"dc:title":["An Examination of the Day of Week and Sampling on Injury Occurrence"],"dc:type":["Master Thesis"],"thesis:degree_discipline":["Safety, Security, and Emergency Management"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Science (MS)"],"thesis:institution_name":["Eastern Kentucky University"]},"updated_at":"2026-07-24T02:15:25Z"}