# The Construction Industry in a War-Ravaged Region: Examination of Challenging Factors
## 3 Methods
### Data Analysis
#### 3.3.2 Development of SEM  
The SEM is a multivariate data analysis method that makes possible the simultaneous modelling and estimation of complex relationships among variables [75]. SEM is used for a better understanding of the multiple observed variables (dependent and independent) in the area of CI research, as the use of a small number of variables for complex phenomena can be limited [76]. The SEM takes measurement error into account while analyzing data and can statistically test theoretical and measurement assumptions against empirical data [77]. There are two methods within the SEM: Covariance-based (CB-SEM) and Partial least square (PLS-SEM). The features of the data, model characteristics, model estimation, and model evaluation are described by [78] as the critical and most relevant issues in PLS-SEM. Using the Smart PLS 4, a piece of software with highly distinctive functionalities was deemed appropriate for this study. The software uses the partial least squares methodology, a beneficial method in situations where the hypothesis analysis was not followed closely enough, the sample size is small, or the data does not meet the distributional assumptions [78; 79]. Furthermore, the software offers a robust estimation and enables the evaluation of the suggested model with high precision. These are important criteria in assuring the accuracy and proper understanding of the research findings. In this study, three major assessments were taken into consideration in the analysis of the PLS-SEM: the common method variance (CMV), the measurement model, and the structural model. According to [80,81], the common method bias (CMB) is an issue in research (e.g., one data source, self-reported data, leniency bias, and social desirability, etc.) that could lead to inaccuracy in results brought about by the fact that data collection could increase the trigger issues. Therefore, it is important to observe these difficulties and to determine whether the CMV is present or not. [PERSON]'s analysis is recommended by [82], a systematic, single-factor analysis used to examine the presence of CMV. This study examined the possible effect of CMV by utilizing online questionnaires, which prevent multiple respondents and the anonymity of the respondents, thereby preventing the effect of social desirability. The measurement model can be tested through the analysis of internal consistency, indicator reliability, convergent validity, and discriminant validity. In PLS SEM, composite reliability (CR) is used for measuring internal consistency because indicators with different loadings are considered [83]. According to [84], an internal consistency of \(\geq\)0.7 is considered satisfactory, whereas, when the value is \(<\)0.6, this shows a lack of reliability measures. Indicator reliability is evaluated when a set of variables is consistent with what it tends to measure. Ref. [77] suggested that the indicator loading should be \(\geq\)0.7 at the 0.05 significant level. Elimination of a factor is complete when the indicator's reliability is low, and this increases the CR of the factor. Next is the evaluation of convergent and discriminant validity. The analysis of convergent validity is the degree to which all measurements agree with one another, which are assessed using the average variance extracted (AVE) [85]. According to [86], the convergent validity of the construct is achieved when the AVE is \(\geq\)0.50. On the other hand, discriminant validity is the measurement model that differentiates the measure of the construct from one another. This study evaluated the discriminant validity using the Heterotrait-Monotrait Ratio (HTMT). Ref. [87] suggested that the values of the HTMT should be lesser than the required threshold of HTMT 0.85 or HTMT 0.90 as suggested by [88]. The structural model can be analyzed after achieving the measurement model. Lateral collinearity, also known as predictor-criterion collinearity, occurs when two hypothesized variables are causally related and measure the same constructs.