## 4 Results  
More than 500 surveys were distributed to launch vehicle organizations throughout the US. A total of 203 valid survey responses were received, which yielded a response rate of approximately 40%. Furthermore, 27.7% of the respondents identified as systems engineers, which made up the largest percentage of systems engineering roles. The remaining 72.3% of respondents were distributed across other roles in the systems engineering process, thus addressing any sampling bias concerns. Approximately 40.9% of respondents had more than 20 years of experience. The number of projects worked on by the respondents varied, and the largest proportion of respondents (31%), by a small margin, had worked on 30 or more projects. Most of the respondents' systems engineering experience (71.4%) was in the launch vehicle industry. Survey participants were from small, medium, and large organizations, and 60.1% came from large organizations. Participants had a range of levels of government involvement, and most (71.4%) came from government agencies. The details of the profiles of respondents are shown in Table 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristics** & & **Frequency** & **\%** \\ \hline Role & Engineering support & 7 & 3.4 \\  & Manager & 19 & 9.4 \\  & Design Engineer & 5 & 2.5 \\  & Test Engineer & 4 & 2 \\  & Manufacturing Engineer & 3 & 1.5 \\  & Operations Engineer & 7 & 3.4 \\  & Component Engineer & 13 & 6.4 \\  & Analyst & 25 & 12.3 \\  & Integration Engineer & 14 & 6.9 \\  & Project Manager & 28 & 13.8 \\  & Systems Engineer & 78 & 38.4 \\ Years of Experience & 1–5 years & 31 & 15.3 \\  & 5–10 years & 29 & 14.3 \\  & 10–15 years & 30 & 14.8 \\  & 15–20 years & 30 & 14.8 \\  & 20 years or more & 83 & 40.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data Set (\(n=203\)).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model Fit Index** & **Criteria** \\ \hline Chi – square (\(\chi^{2}\)) & Low \\ Degrees of freedom (df) & \textgreater{}0 \\ Probability value (\(p\)) & \textgreater{}0.05 \\ \(\frac{\chi^{2}}{df}\) & \textless{}5 \\ Goodness-of-fit index (GFI) & \textgreater{}0.90 \\ Tucker–Lewis index (TLI) & \textgreater{}0.90 \\ Comparative fit index (CFI) & \textgreater{}0.90 \\ Root mean square error approximation (RMSE) & \textless{}0.08 \\
90\% Confidence interval (lo90—hi90) & \textless{}0.05–0.08 \\ Probability of closeness of fit (P\({}_{\text{close}}\)) & \textgreater{}0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Goodness of Fit Indices.  
Before CFA and SEM, multivariate assumptions of normality, linearity, and homoscedasticity were verified. When residuals were plotted and examined, the data were found to be in violation of the assumptions. Bootstrapping is a widely accepted technique to use for SEM when assumptions are violated [60,64]. Although research by [PERSON] [63] has suggested that validity can still be achieved when the data do not follow a normal distribution, we nevertheless used the bootstrapping technique to bolster analysis results.  
Multicollinearity was the next assumption tested. This test identifies one or more observed variables that are strongly correlated. When two observed variables are highly correlated, they are essentially measuring the same thing and could cause problems later in the data analysis process. SEM researchers have suggested that a correlation above 0.90 between variables is considered evidence of multicollinearity [66]. No observed variables were found to have a correlation greater than 0.90; therefore, no observed variables were recommended for removal on the basis of multicollinearity.