# An Assessment of the Factors Impacting Project Success in the Engineering Sector
## 5 Results
### Confirmatory Factor Analysis  
The data were analysed using JASP to conduct Confirmatory Factor Analysis (CFA), replicating the EQS package. Table 5 below is a chi-square test of the data to determine the model fit. The \(p\)-value of the chi-square test is 0.190, which is greater than the significance level of 0.05 (30). A \(p\)-value greater than the significance level means that we cannot reject the null hypothesis. This indicates that there is no significant difference between the observed and expected values. The non-significant \(p\)-value indicates that the model adequately fits the data, as the goodness-of-fit is assessed using the chi-square test. When the chi-square test returns a non-significant result, it means that the differences between the observed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Component**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline CC1 & 0.019 & 0.703 & 0.018 & \(-\)0.015 & 0.118 & 0.254 \\ \hline CC2 & 0.042 & \(-\)0.182 & 0.012 & 0.259 & 0.767 & 0.153 \\ \hline CC3 & 0.039 & 0.498 & 0.074 & \(-\)0.020 & 0.680 & \(-\)0.092 \\ \hline CC4 & 0.173 & 0.274 & \(-\)0.018 & \(-\)0.124 & 0.644 & 0.108 \\ \hline CC5 & \(-\)0.121 & 0.558 & \(-\)0.030 & 0.319 & 0.235 & 0.066 \\ \hline CC6 & \(-\)0.149 & 0.720 & \(-\)0.137 & 0.166 & \(-\)0.140 & \(-\)0.238 \\ \hline CC7 & \(-\)0.105 & 0.627 & \(-\)0.081 & \(-\)0.002 & 0.216 & 0.393 \\ \hline CC8 & \(-\)0.075 & 0.210 & \(-\)0.109 & 0.026 & 0.130 & 0.796 \\ \hline EM1 & 0.052 & 0.114 & 0.809 & \(-\)0.077 & \(-\)0.131 & 0.054 \\ \hline EM2 & \(-\)0.042 & \(-\)0.134 & 0.668 & 0.173 & \(-\)0.013 & 0.384 \\ \hline EM3 & \(-\)0.144 & 0.028 & 0.651 & \(-\)0.020 & 0.206 & \(-\)0.283 \\ \hline EM4 & \(-\)0.081 & \(-\)0.219 & 0.760 & 0.028 & 0.027 & \(-\)0.202 \\ \hline EE1 & 0.047 & 0.202 & \(-\)0.020 & 0.803 & \(-\)0.027 & 0.032 \\ \hline EE2 & \(-\)0.022 & 0.216 & \(-\)0.093 & 0.768 & 0.329 & \(-\)0.101 \\ \hline EE3 & \(-\)0.072 & \(-\)0.118 & 0.132 & 0.783 & \(-\)0.096 & 0.075 \\ \hline EP1 & 0.739 & 0.163 & 0.020 & 0.081 & 0.041 & \(-\)0.079 \\ \hline EP2 & 0.786 & \(-\)0.130 & 0.141 & \(-\)0.017 & 0.049 & 0.047 \\ \hline EP3 & 0.772 & 0.013 & \(-\)0.231 & \(-\)0.043 & 0.169 & 0.042 \\ \hline EP4 & 0.560 & \(-\)0.294 & \(-\)0.265 & \(-\)0.018 & 0.067 & \(-\)0.102 \\ \hline EP5 & 0.826 & \(-\)0.136 & \(-\)0.009 & \(-\)0.090 & \(-\)0.073 & \(-\)0.029 \\ \hline \hline \end{tabular} Extraction Method: Principal Component Analysis.  
Rotation Method: Varimax with Kaiser Normalisation.  
\end{table} 
Table 4: Rotated component matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{X}^{2}\) & **df** & \(\mathbf{p}\) \\ \hline Baseline model & 1353.419 & 253 & \\ \hline Factor model & 238.266 & 220 & 0.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CFA chi-square test. data and what the model predicts are insufficient to be considered statistically significant. Thus, the model is regarded as a good fit for the data [32]. The factor loadings of the data are shown in Table 6 below. The factor loadings from the analysis reveal that all indicators have \(p\)-values lower than 0.05, indicating that they are statistically significant. This suggests that these indicators are meaningfully associated with the underlying construct. The factor covariances are shown in Table 7 below. The factor covariances suggest a statistically significant relationship between individual factors and the dependent variable, project success. The covariance between company culture and project success is shown to be statistically significant, with a \(p\)-value of 0.004. This indicates a strong relationship between the two factors, suggesting that company