Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Behavioral Observations Reduce the Probability of Injury: A replication AUTHORS: Lizzie Smith, Charlie Wills, Ethan Fountain, Elizabeth Arnold, Madalyn Stephens, Drew Sipe, Shawn Bergman, & Tim Ludwig Introduction Occupational safety is a highly effective area for applying behavior analysis in the workplace (Ludwig & Laske, 2022). Behavioral safety programs are designed to reduce workplace injuries by observing employee behavior and identifying potentially risky behaviors. These programs often include behavioral observations, where employees use a checklist to identify unsafe behaviors and provide quick, helpful feedback. Past research has consistently shown that these programs can lower injury rates over time (Ludwig & Laske, 2023). This study will use a statistical model to determine whether behavioral observations made during the past week influence the probability of incidents happening over the next seven days. Methods Safety data from a southern oil refinery company (2022–2024) will be analyzed to replicate findings from a chemical manufacturing plant (2017 - 2019). A rolling sum time-series logistic regression analysis will test whether the number of observations or hazards reported over the past seven days influenced the likelihood of an incident occurring in the subsequent seven-days. In the replication, the control variables included day of the week, actual week, number of responses marked safe/unsafe/NA, questions responded to, people per day, hours worked per day, and employee count. In the new analysis, these variables serve as predictors rather than controls. Analysis We will apply a rolling sum time series logistic regression model to capture 14‑day safety trends. This model should not only allow us to replicate previous findings but to test the time lag for all predictors. Our hypothesis is that increased safety observations reduce incident likelihood within seven days. Previous findings from a chemical manufacturing plant (2017–2019) showed each additional observation lowered incident odds by 23% in Manufacturing and 17% in Maintenance over three days, preventing an estimated four and 16 incidents annually. Replicating and extending these results will strengthen validity and enhance predictive power. Implications The replication of our findings will strengthen evidence that increased safety observations reduce injury probability. With greater dataset granularity, we expect higher statistical power and improved validity of our methodology as a practical tool for reducing workplace injuries. This replication will serve as a foundation for developing a predictive model capable of estimating daily injury probability and assessing the sustained impact of safety observations. As digital monitoring and AI tools advance, our study highlights the critical role human-generated safety observations continue to play in reducing workplace incidents. References Ludwig, T. D., & Laske, M. M. (2022). Behavioral safety: An efficacious application of applied behavior analysis to reduce human suffering. Journal of Organizational Behavioral Management, 43(3), 190-220. https://doi.org/10.1080/01608061.2022.2108536 Ludwig, T. D., & Laske, M. M. (2023). The science and best practices of behavioral safety: The source for reducing injuries on the front line. Routledge.
Subject
Industrial and organizational psychology
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Included in
Behavioral Observations Reduce the Probability of Injury: A replication
Behavioral Observations Reduce the Probability of Injury: A replication AUTHORS: Lizzie Smith, Charlie Wills, Ethan Fountain, Elizabeth Arnold, Madalyn Stephens, Drew Sipe, Shawn Bergman, & Tim Ludwig Introduction Occupational safety is a highly effective area for applying behavior analysis in the workplace (Ludwig & Laske, 2022). Behavioral safety programs are designed to reduce workplace injuries by observing employee behavior and identifying potentially risky behaviors. These programs often include behavioral observations, where employees use a checklist to identify unsafe behaviors and provide quick, helpful feedback. Past research has consistently shown that these programs can lower injury rates over time (Ludwig & Laske, 2023). This study will use a statistical model to determine whether behavioral observations made during the past week influence the probability of incidents happening over the next seven days. Methods Safety data from a southern oil refinery company (2022–2024) will be analyzed to replicate findings from a chemical manufacturing plant (2017 - 2019). A rolling sum time-series logistic regression analysis will test whether the number of observations or hazards reported over the past seven days influenced the likelihood of an incident occurring in the subsequent seven-days. In the replication, the control variables included day of the week, actual week, number of responses marked safe/unsafe/NA, questions responded to, people per day, hours worked per day, and employee count. In the new analysis, these variables serve as predictors rather than controls. Analysis We will apply a rolling sum time series logistic regression model to capture 14‑day safety trends. This model should not only allow us to replicate previous findings but to test the time lag for all predictors. Our hypothesis is that increased safety observations reduce incident likelihood within seven days. Previous findings from a chemical manufacturing plant (2017–2019) showed each additional observation lowered incident odds by 23% in Manufacturing and 17% in Maintenance over three days, preventing an estimated four and 16 incidents annually. Replicating and extending these results will strengthen validity and enhance predictive power. Implications The replication of our findings will strengthen evidence that increased safety observations reduce injury probability. With greater dataset granularity, we expect higher statistical power and improved validity of our methodology as a practical tool for reducing workplace injuries. This replication will serve as a foundation for developing a predictive model capable of estimating daily injury probability and assessing the sustained impact of safety observations. As digital monitoring and AI tools advance, our study highlights the critical role human-generated safety observations continue to play in reducing workplace incidents. References Ludwig, T. D., & Laske, M. M. (2022). Behavioral safety: An efficacious application of applied behavior analysis to reduce human suffering. Journal of Organizational Behavioral Management, 43(3), 190-220. https://doi.org/10.1080/01608061.2022.2108536 Ludwig, T. D., & Laske, M. M. (2023). The science and best practices of behavioral safety: The source for reducing injuries on the front line. Routledge.
Department
University of Tennessee at Chattanooga. Dept. of Psychology