Related Experiment Video
Updated: Jun 9, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Exploring risk groups workplace bullying with categorical data.
Guy Notelaers1, Jeroen K Vermunt, Elfi Baillien
1Bergen Bullying Research Group, Faculty of Psychology, Bergen University, Norway. guy.notelaers@uib.no
This study identified six workplace bullying exposure groups, with 3.6% experiencing severe bullying. Risk factors include age (35-54), public service, and manufacturing jobs, informing targeted prevention strategies.
Area of Science:
- Occupational Health
- Psychology
- Sociology
Background:
- Workplace bullying is a significant issue affecting employee well-being and organizational productivity.
- Previous research has often treated workplace bullying as a monolithic phenomenon, potentially overlooking diverse experiences and risk factors.
Purpose of the Study:
- To identify distinct exposure groups of workplace bullying within a large, heterogeneous sample.
- To investigate the demographic and occupational risk factors associated with different levels of workplace bullying exposure.
Main Methods:
- A large, heterogeneous sample was utilized to categorize individuals into different workplace bullying exposure groups.
- Multinomial logistic regression analysis was employed to examine the relationship between identified exposure groups and social demographics.
Main Results:
- Six distinct workplace bullying exposure groups were identified, ranging from no bullying (30.5%) to severe bullying (3.6%).
- Elevated risks for workplace bullying were found in employees aged 35-54, public servants, blue-collar workers, and those in the food and manufacturing industries.
- Conversely, younger employees (<25), temporary contract workers, teachers, nurses, and assistant nurses were found to be at lower risk.
Conclusions:
- The study highlights the heterogeneity of workplace bullying experiences, necessitating tailored prevention strategies.
- Identifying specific risk groups allows for targeted interventions to mitigate workplace bullying.
- Findings provide crucial data for policymakers to develop effective national and organizational anti-bullying initiatives.
Related Concept Videos
Bullying
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Stereotype Content Model
Stereotypes, Prejudice, and Discrimination
Contingency Table
Comparing the Survival Analysis of Two or More Groups