Analysis of subjective thermal comfort data: A statistical point of view

Thermal comfort research aims to determine the relationship between the thermal environment and the human sense of warmth. This is usually achieved by measuring the subjective human thermal response to different thermal environments. However, it is common practice to use simple linear regression to analyse data collected using ordinal scales. This practice may lead to severe errors in inference. This study first set the methodological foundations to analyse subjective thermal comfort data from a statistical perspective. Subsequently, we show the practical consequences of fallacious assumptions by utilising a Bayesian approach and show, through an illustrative example, that a linear regression model applied to ordinal data suggests results different from those obtained using ordinal regression. Specifically, linear regression found no difference in means and effect size between genders, while the ordinal regression model led to the opposite conclusion. In addition, the linear regression model distorts the estimated regression coefficient for air temperature compared to the ordinal model. Finally, the ordinal model shows that the distance between adjacent response categories of the ASHRAE 7-point thermal sensation scale is not equidistant. Given the abovementioned issues, we advocate utilising ordinal models instead of metric models to analyse ordinal data. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Thermal comfort research aims to study the relationship between thermal environment and human sense of warmth, usually through subjective thermal response. However, using linear regression to analyze ordinal data may result in severe errors. This study establishes methodological foundations for analyzing subjective thermal comfort data and demonstrates the practical consequences of fallacious assumptions through a Bayesian approach. The findings highlight the importance of using ordinal models instead of metric models for analyzing ordinal data.