# An Assessment of the Factors Impacting Project Success in the Engineering Sector
## 5 Results
### Exploratory Factor Analysis  
Before conducting an Exploratory Factor Analysis (EFA), two checks need to be carried out to assess the feasibility of the analysis [31]. These tests include [PERSON]'s test of sphericity and the Kaiser-Meyer-Olkin test of sampling adequacy (KMO). These tests were conducted on the independent variables, and the results are presented in Table 2 below.  
If the significance is \(p<0.001\), then it is appropriate to perform a factor analysis of the data [31]. [PERSON]'s test of sphericity for the data indicates a significance of 0.000; hence, it is appropriate to perform a factor analysis. The KMO value indicates whether it is worthwhile to conduct a factor analysis [31]. A KMO value below 0.500 is unacceptable; a value from 0.500 to 0.599 is classified as miserable [31]. A miserable KMO value is acceptable, but it indicates that the data are not robust and have a weak correlation. The KMO value of the data, as indicated in the table above, is 0.565. This is close to the lower limit of 0.500, which is miserable but acceptable. The eigenvalues were computed to assess the variance within the data. This distinction is demonstrated by a particular factor [31]. The eigenvalues of the dataset are presented in Table 3 below.  
Factors 1 to 6 have eigenvalues greater than 1 and explain 64.328% of the variance. According to the Guttman-Kaiser rule, factors with eigenvalues greater than or equal to 1 are considered [31]. This accounts for a significant portion of the data and, given that these factors explain 64.328% of the variance, is an indication that they capture the majority of the dataset's underlying structure. Additionally, this indicates that these factors are the most significant in explaining the relationships between the variables, and they warrant further investigation and analysis. Table 4 below provides a rotated component matrix of the data. This matrix illustrates the factor loadings, which represent the relationships between each specific factor and item within the dataset [31].  
When determining the allocation of an item to a specific factor, a particular criterion is utilised. This criterion is defined by a threshold of 0.3, indicating the level of association required for an item to be linked with a factor [31]. Upon close examination of the matrix, it becomes apparent that each item is distinctly connected to a specific factor based on the established criterion. This process enables a clear and systematic approach to factor-item allocation within the matrix. The questions relating to company culture are scattered  
\begin{table}
\begin{tabular}{l c} \hline Kaiser–Meyer–Olkin Measure of Sampling Adequacy & 0.565 \\ \hline \multirow{3}{*}{Bartlett’s Test of Sphericity} & Approx. Chi-Square & 472.440 \\ \cline{2-3}  & df & 190 \\ \cline{2-3}  & Sig. & 0.000 \\ \hline \end{tabular}
\end{table}
Table 2: KMO and Barlett’s test.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} \\ \cline{2-4}  & **Total** & **\% of Variance** & **Cumulative \%** \\ \hline
1 & 3.435 & 17.173 & 17.173 \\ \hline
2 & 3.042 & 15.211 & 32.383 \\ \hline
3 & 2.238 & 11.190 & 43.573 \\ \hline
4 & 1.816 & 9.082 & 52.655 \\ \hline
5 & 1.211 & 6.054 & 8.709 \\ \hline
6 & 1.124 & 5.620 & 64.328 \\ \hline \end{tabular} Extraction Method: Principal Component Analysis  
\end{table}
Table 3: Eigenvalues.  
between factors 2.5 and 6. Effective management questions are related to factor 3, employee engagement is associated with factor 4, and effective planning is associated with factor 1.