a set of techniques and procedures for collection and analysis (Punch 2014 ). As the
analysis progressed, key themes were identified and refined—adding some and
deleting or merging others—through“constant comparison”with the interview
transcripts.
For data analysis NVivo 9 was used, going through a number of steps:
i. Initial coding of the transcripts was fairly broad, leading to 100
- nodes/themes. Some arose simply as answers to interview questions (e.g.
background experience as a classroom teacher) while others emerged unex-
pectedly (e.g. fell into doing a PhD).
ii. After two rounds of coding the 100+ nodes/themes were collapsed into 40
nodes; however, within these there were sub-nodes (e.g. gaps in knowledge
had sub-nodes of knowledge of research, knowledge of schools). NVivo
allows for double- and triple-code certain content (e.g. the same material might
relate to influence on practice, classroom teacher experience, and pedagogy).
iii. As the quotes, annotations, and memos were analyzed summaryfindings in
three key areas were identified: influences on practice; goals for courses; and
impact of politics. Given the sophistication of NVivo, queries were conducted
to see relationships between the biographical data and other data (e.g. PhD
area of study and current research activities). With NVivo both qualitative and
quantitative data can be drawn upon.
Experience as a classroom teacher 0 years = 1
1-5 years = 3
6-10 years = 12
11-20 years= 6
21+ years = 6
Rank at the university Assistant Professor (Lecturer in UK and AU) = 6
Associate Professor =5
Senior Lecturer = 7
Full Professor = 5
Other =1
Contract = 4
Experience as a teacher educator 1-5 years = 7
6 -10 years = 10
11-15 years = 2
16 -20 years= 5
21+ years = 4
Countries Canada - 7
US - 11
England - 5
Australia -5Fig. 9.2 Background of participants (as of 2013)
140 C. Kosnik et al.