Related Experiment Video
Updated: May 9, 2026
08:25
Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
Published on: December 6, 2024
A data-analytics framework for exploring regression associations in multivariate categorical data of firefighters'
1Department of Marketing Bigdata, Mokwon University, Daejeon, South Korea.
Journal of Applied Statistics
|May 8, 2026
Summary
Firefighters
Area of Science:
- Occupational Health
- Psychological Science
- Data Science
Background:
- Post-Traumatic Stress Disorder (PTSD) is a significant concern in high-stress professions like firefighting.
- Understanding the complex interplay of risk factors associated with firefighter PTSD is crucial for targeted interventions.
Purpose of the Study:
- To develop and apply a data-analytics framework for comprehensive analysis of firefighter PTSD associations.
- To investigate joint, marginal, and conditional regression associations between PTSD and categorical risk factors.
Main Methods:
- Integration of Scaled Checkerboard Copula Regression Association Measure (SCCRAM) for identifying key risk factor subsets.
- Application of resampling (bootstrap/permutation) methods to assess statistical significance and credibility of associations.
- Analysis of multi-dimensional contingency tables, addressing sparseness and imbalance.
Main Results:
- Disorder/mental health factors demonstrate a substantial association with firefighter PTSD.
- The relationship between demographic/job-related factors and PTSD is amplified when considering mental health factors.
Conclusions:
- The proposed framework effectively identifies significant risk factors for firefighter PTSD.
- Mental health status is a critical moderator in the relationship between job-related factors and PTSD in firefighters.
Keywords:
62H0562H1762H20Multidimensional contingency tablesPTSDordinal categorical variablesregression associationsresampling methodsMore Related Videos
Related Concept Videos
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:19Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...