## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction? Usually, weighting schemes were applied in the previous studies to combine the individual index into an overall IEQ satisfaction model. Table 1 lists a summary of the weighting schemes used in the existing IEQ models. To ensure comparability, the weightings were normalized into a new array with a sum equal to one (those with the highest weightings were marked in bold). Among them, the most straightforward weighting scheme is a set of equal weights, namely, 0.25 for each factor [54,55,76]. This weighting scheme was officially proposed and justified by [PERSON] et al. [54]. However, IEQ is a complex system, wherein multiple interaction effects among the individual IEQ aspects might occur [82,86], and the contributions of these aspects on the overall IEQ satisfaction might not equal (e.g., [14,60]). For example, [PERSON] et al. [61] conducted a laboratory study on the occupants' perceptions of four IEQ aspects under different environmental configurations. They found that different background sound types could significantly affect the occupants' perception of acoustic quality and IAQ. Therefore, to accurately predict the occupants' IEQ satisfaction, the four IEQ aspects identified earlier should be investigated together. An integrated analysis approach that considers the combined effect of the individual IEQ aspects on the overall IEQ satisfaction should be applied. The most commonly used integrated analysis methods were linear regression analysis, logistic regression analysis, the analytical hierarchy process (AHP), and structural equation modeling (SEM) to determine the weightings of the individual indexes. [PERSON] et al. [17] compared three different weighting schemes and found that different analytical approaches resulted in the different weightings of individual IEQ indexes. For example, the importance ranking of acoustic comfort and visual comfort on the overall IEQ performance obtained by linear regression analysis was opposite to that obtained by AHP. The difference in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Building Type** & **Thermal** & **Air** & **Acoustic** & **Visual** \\ \hline [PERSON] and [PERSON] (2005) [60] & Office & **0.53** & 0.11 & 0.35 & / \\ [PERSON] et al. (2008) [14] & Office & **0.31** & 0.25 & 0.24 & 0.19 \\ [PERSON] (2005) [51] & Office & **0.42** & 0.39 & 0.14 & 0.05 \\ [PERSON] and [PERSON] (2009) [39] & Residents & **0.34** & 0.22 & 0.22 & 0.22 \\ [PERSON] et al. (2009) [38] & Residents & **0.38** & 0.03 & 0.38 & 0.21 \\ [PERSON] et al. (2012) [13] & Office and education & **0.39** & 0.14 & 0.27 & 0.20 \\ [PERSON] (2012) [15] & Office & 0.30 & **0.36** & 0.18 & 0.16 \\ [PERSON] and [PERSON] (2012) [76] & Education & 0.25 & 0.25 & 0.25 & 0.25 \\ [PERSON] et al. (2013) [8] & Commercial & 0.12 & 0.20 & **0.39** & 0.29 \\ [PERSON] et al. (2014) [16] & Education & **0.31** & 0.25 & 0.24 & 0.19 \\ [PERSON] and [PERSON] (2016) [10] & Education & 0.25 & **0.26** & 0.24 & 0.24 \\ [PERSON] et al. (2017) [54] & Office & 0.25 & 0.25 & 0.25 & 0.25 \\ [PERSON] (2018) [72] & Hospital & **0.31** & 0.21 & 0.25 & 0.24 \\ [PERSON] (2018) [42] & Education & **0.31** & 0.16 & 0.24 & 0.29 \\ [PERSON] et al. (2018) [56] & Education & **0.35** & / & 0.35 & 0.30 \\ [PERSON] et al. (2019) [85] & Residents & 0.17 & 0.01 & 0.27 & **0.56** \\ [PERSON] (2020) [50] & Education & **0.32** & 0.16 & 0.28 & 0.24 \\ [PERSON] et al. (2020) [55] & Education & 0.25 & 0.25 & 0.25 & 0.25 \\ [PERSON] et al. (2021) [48] & Office & 0.29 & **0.35** & 0.20 & 0.17 \\ [PERSON] (2022) [71] & Education & **0.37** & 0.30 & 0.16 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of normalized weighting schemes used in the existing models. the weighting schemes obtained between linear and logistic regression methods was also identified by [PERSON] et al. [16]; they concluded that logistic regression analysis tended to generate more stable results than linear regression analysis.