The results below show correlations between outcomes and other factors examined in the survey.

A list of all survey questions and frequencies of the various responses is also available.
These lists are auto generated and may require a little interpretation.

E.1 The population surveyed

Figure E.2: Respondent’s university

As shown in Figure E.2, a large number of the responses (15 in total) were from Monash. A further 4 were from Curtin University of Technology. All other universities had 1 or 2 participants.

Figure E.3 shows the respondents primary field of training. As shown, most (21 in total) were primarily trained in Computer Science. A further 4 were training in Computing or Programming and 2 were training in mathematics. Only one person was trained in a science other than computer science. There were 3 people who's training did not at the selection provided. Two of these were varieties of engineering and the other was digital systems, as indicated in the open response question.

Figure E.3: Respondent's field of training

Figure E.4 shows the respondents primary role in their current institution. The break down is as expected for an Australian university.

Figure E.4: Respondent's primary role

Figure E.5 shows how many respondents have taught Software Engineering.

Figure E.5: Has respondent taught Software Engineering?

E.2 Correlations of interest

This section includes graphs and tables of metric on research outcomes. It also includes graphs and tables for a number of matrices related to the attitude of computer scientists to some parts of software engineering.

Outcome metrics reflect both qualitative e.g. `personal satisfaction' and quantitative e.g. `publication output' analysis. The exact questions asked can be seen in appendix [TODO insert reference to correct appendix].

In this section a graph provides an overview and is later followed by a table which provides further detail.

Figure E.6: Lateness of Analysis and Design and significant correlations

Figure E.7: Would document after and significant correlations

Figure E.8: Personal frustration during the project and significant correlations

Figure E.9: Projects funding and significant correlations

Figure E.10: Participants prediction of their future use of Software Engineering (relative to their present use) and significant correlations

Figure E.11: Total value of grants attracted and significant correlations

Figure E.12: Number of higher degree by research students involved in the project and significant correlations

Figure E.13: Average (related) publications per higher degree by research students involved in the project and significant correlations

Figure E.14: Publications from the project and significant correlations

Figure E.15: Participants estimation of the change up to present in their level of software engineering and significant correlations

Figure E.16: Ability to get resources needed and significant correlations

Figure E.17: Size of component reused in this project and significant correlations

Figure E.18: Number of complete rewrites of this project and significant correlations

Figure E.19: Personal satisfaction with project outcome compared to other projects and significant correlations

Figure E.20: Number of spin-off projects form this one and significant correlations

Figure E.21: Willingness to use more software engineering earlier and significant correlations