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fear-system was activated («
I was so worried about what would be correct to say and
I blocked completely, not remembering anything
»). These punitive appraisals
decreased with debriefings and it seems that these moments allowed participants to
reflect on their feelings and experiences («
debriefing (…) made me think and
challenge what I wrote in the last SISDAT (…) it made me reflect on what I really felt
during the experience
»). Debriefings also appeared to be associated with the
expression of the activity as a learning moment (
learning source
), with not having
dissatisfactions and with normalizing dissatisfaction. This analysis enabled us to
understand the impact of the interview on the participants' self-defense system and
the importance of debriefing in reducing it and activating their exploratory system.
The analysis of the two years also informed us that a third interview and debriefing
(absent in year two) seem to enhance the activation of the participants' exploratory
system (see patterns in
learning source
,
self-fulfillment
, and
without dissatisfactions
).
This analysis considering
sets
represents comparative work, since we compare what
happens in different moments. It also enables identifying patterns in the data. This
work is fundamental in qualitative research [7], in the sense that we try to understand
phenomenon (specified in the proposition, for instance), considering specific criteria.
In this case the criteria were the moment and the year of the program, using
sets
.
Team Work and Reporting.
One of the publicized features of NVivo is its tools for
boosting teamwork. These can be used in any stage of the research - when first
having contact with the data and thinking about coding schemes and concepts and
when coding and observing the reliability of the coding process. In our research the
process of coding was done simultaneously by the authors, in person or using Skype.
The queries were operationalized by one of the authors and later discussed by both.
This happened for two reasons: the fact that one of the authors had an easier access to
the software and that this way we were able to think about the data together, building
a more robust coding scheme and contributing to the coding process's reliability.
Despite representing a worse use of time resources, since this required that both of us
worked on the project at the same time, we feel this strategy was important in the first
phases of our research as we guaranteed that the most complex work was done in a
way that both researchers had access to the data at the same time and could discuss it.
Sometimes the indexing of data proposed by one of the researchers was questioned by
the other, requiring that we discussed it in a way that we both agreed on the coding.
Meanwhile, both researchers gained easy access to NVivo and feel comfortable
enough to code separately. This requires, however, defining rules regarding the work,
considering the technical features of NVivo, such as the impossibility of editing the
data, in order we can later import data from one NVivo project to the other. Other
rules have been set in a coding manual, which has been built as the research
progressed, and kept in NVivo. Qualitative analysis requires that the researcher
deeply understands the data and analyses it, if not all of the data, at least part of it. In
this way, we will have a deep understanding of what the data conveys and the
concepts it entails. Both the authors stress the importance of this for them.
The results of this research have been presented to the scientific community (e.g.,
[12]). NVivo has supported us in the process of reporting by presenting outputs that
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