Relationship Between Demographic Characteristics And Psychosocial Working Conditions Of Construction Workers Working On Housing Projects


  • Natee Suriyanon Mahanakorn University of Technology, Thailand.
  • Pitch Sutheerawatthana King Mongkut’s University of Technology Thonburi, Thailand


Construction workers, High strain isolated psychosocial working condition, Logistic regression model, Subdivision housing projects


Data regarding three psychosocial factors of 438 construction workers in eight Bangkok subdivision housing projects, which had a selling price between 2.50 and 5.00 million Baht, were collected in October 2018. The subjects were divided into two groups. The first group consisted of 71 workers who worked under high strain isolated (HSI) psychosocial working condition (workers felt that their work entailed high demand, they possessed a low controllability over their job and low work–related social support). The second group consisted of 367 workers who worked under other conditions. The result from the chi-square test between workers’ demographic characteristics and their psychosocial working conditions revealed that the probability of working under HSI condition for workers with different Gender, Age, Education Level, Work Experience, Work Position, and Average Monthly Income are different. It insisted the heterogeneity of working under HSI condition among construction workers. A logistic regression model for forecasting the probability of working under HSI condition, using the workers’ demographic characteristics, has then been developed in this study. The model consists of five demographic characteristic variables, namely Work Position, Education Level, Average Monthly Income, Work Experience, and Age. The adjusted odds ratio value, which was obtained from the analysis of the model constants, indicates that Work Position, Average Monthly Income, Age, Education Level, and Work Experience have influence on workers’ probability of working under HSI condition in descending order. Finally, an example of applying the model to identify specific demographic characteristics of workers who need special attention is presented at the end of this paper