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could see that they talked about having an expectation of success regarding their
performance in this 2
nd
interview and that they were frustrated with the way they
performed. In order to make sense of this we then looked at the data coded at
satisfaction at m1 (1
st
interview) and found a concept we called “despite difficulties”,
which refereed to participants acknowledging satisfaction for doing something despite
encountering difficulties while doing it («
Despite not being prepared, I think the
interview wasn't bad
»). Interestingly enough, this concept was absent at Satisfaction
at m4 m1 (2
nd
interview).
Table 1.
First level categories (themes) in six moments of the program in year one (n=6)
First level themes M1 M2 M3 M4 M5 M6
Satisfaction
24,97% 21,63% 22,03% 16,84% 24,51% 25,74%
Dissatisfaction
16,11% 14,94% 18,64% 25,32% 15,49% 15,33%
Surprise
18,38% 20,2% 16,19% 18,14% 19,35% 14,01%
Discoveries
15,4% 14,12% 14,11% 15,46% 13,97% 19,96%
Learnings
13,98% 13,31% 13,66% 12,27% 11,84% 10,99%
Transpositions
11,17% 15,8% 15,36% 11,97% 14,84% 13,97%
Total
100% 100% 100% 100% 100% 100%
The percentages inform the number of words in our first level themes (categories).
M1=1
st
interview; M2=1
st
debriefing; M3=lecture; M4=2
nd
interview; M5=2
nd
debriefing; M6=3
rd
interview.
These analyses informed us that participants seem to begin the process (1
st
interview) with a self-justifying speech and also that the context wasn't felt as safe
enough for them to explore their difficulties at that moment. The fact that this concept
(i.e., despite difficulties) disappeared in the 2
nd
interview, as participants stressed their
frustration (Dissatisfaction) suggests that at this time the context was already felt as
being safe. Supporting this interpretation is the fact that at m5 (2
nd
debriefing)
participants' satisfaction reflected the normalization of their difficulties («
it was very
useful to find that most of my questions or considerations are transversal to most
colleagues
»). To summarize, this process of integrating a deductive and inductive
approach to the data enabled us to identify emergent concepts in the data and
understand the participants' experience. We took advantage of this possibility of
integrating qualitative and quantitative analysis using NVivo in different stages of the
process, according to our aims.
Searching for Patterns in the Data.
As authors [8] stress, searching for patterns in the
data is «crucially dependent on the analyst's creativity and ability to recognize
[them]» (p. 10). The role of the software here is to support the researcher searching
and querying the data. In our work this was done using NVivo in an interactive way:
coding, memoing and using queries. In the first stages of analyses we used mainly
coding and memos of ideas that emerged while exploring and coding the first group
of participants (n=6). The quantification of data, using matrix queries also supported
the identification of patterns, signaling the themes most explored by participants at
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