Imre Fekete: Technology in English teaching • The Hungarian university context
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3. Research Methods
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3.4. Study 4: The Instructors’ Interview Study
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3.4.4. Methods of Data Collection and Analysis

3.4.4. Methods of Data Collection and Analysis

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Interview data was collected through Zoom, Microsoft Teams and Skype calls because these applications allow users to record lengthy conversations for free. The recruitment process for the interviews was continuous from the second half of 2020 and interviews were usually scheduled at the earliest convenience of the participant on the preferred platform. The interviews were recorded between September 2020 and February 2021. Originally, 10 interviews were planned, but more potential candidates were approached. In the end, it was my intention to interview every participant who agreed to participate. Later, data analysis also confirmed that the rich data collected from the 12 participants was enough to reach saturation because no newer themes emerged in the transcripts.

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Each interview was preceded by a negotiation process of the time and platform of the interview, and the interviews started with a few minutes of icebreaker conversations. Before the interview recordings were started, participants were informed once again about the purpose of the interview and were reassured of their anonymity and their right to opt out from the research or skip interview questions they preferred not to answer. Their consent was asked to record the interviews, and when they agreed, the recording started. Once the recording started, the participants were asked to repeat their verbal consent. In the end, each participant agreed to being recorded and none of them revoked their consent. Table 16 provides an overview of the metadata of the interviews: it details the duration of the interviews, the recording date, and the length of the interview transcripts.
 

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Table 16 An Overview of the Instructor Interview Data
Participant
Time of recording
Duration
Length of transcript
Albert
September 2020
37 mins
3,949 words
Dóra
October 2020
41 mins
4,348 words
Erika
September 2020
33 mins
4,052 words
Erzsébet
November 2020
36 mins
4,115 words
Éva
October 2020
54 mins
6,993 words
Evelyn
February 2021
40 mins
4,297 words
Gábor
October 2020
32 mins
4,044 words
Kálmán
February 2021
38 mins
3,786 words
László
February 2021
43 mins
5,759 words
Magdolna
February 2021
50 mins
5,660 words
Richárd
October 2020
56 mins
7,793 words
Zsombor
October 2020
43 mins
4,827 words
 

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Analysing qualitative data requires a well-established system as well as prolonged engagement (Dörnyei, 2007; Friedman, 2012). Interviews were first transcribed as Microsoft Word documents and then the transcripts were checked for typos or missing segments. A content analysis approach was used (Xu & Zammit, 2020) to establish codes and attach segments to the codes based on the utterances of the participants. I also utilised a researcher’s journal to record details of the interviews as well as some emerging codes even before all transcripts were finalised, and open coding began (Friedman, 2012). Systematic coding of the data began with the use of QDA Miner Lite (https://provalisresearch.com/products/qualitative-data-analysis-software), a free qualitative data miner software in which codes can be entered by the researcher and data can be coded by highlighting segments and attaching them to the corresponding codes. In the following, axial coding step (Friedman, 2012), new codes were registered and added to the codebook, other codes were renamed, and already coded segments were revisited and revised according to the new codes.

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To enhance the credibility of the codes, two additional quality control measures were taken. First, in July 2021, a month after the final coding of the transcripts, 10% of the data was co-coded by a fellow PhD student following a debriefing session of the established codes to enhance inter-rater reliability (Creswell, 2009; 2015). In the meantime, another 10% of the data was re-coded by the researcher to enhance intra-rater reliability (Creswell, 2009; 2015). Both processes confirmed that the final code structure was a valid system to organise what the participants had expressed in their interviews. A sample coded segment and some additional label code samples are provided in Appendix G.

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In the end, 6 main and 42 subcodes were established and 579 segments were coded. The codes and coding frequencies are summarized in Table 17.
 

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Table 17 . An Overview of the Final Codes and Coding Frequencies of the Instructors’ Interview Study
Main code
(Sum: 6)
Subcode
(Sum: 42)
Times code was used (Sum: 579)
% of all codes
(Sum: 100%)
Number of participants
(N = 12)
% of all participants
1. Technology in general
1.1 Terminology
2
0.3%
2
16.7%
1.2 Worldwide presence
3
0.5%
3
25%
1.3 Personal attitude
5
0.9%
4
33.3%
1.4 Everyday advantages
16
2.8%
10
83.3%
1.5 Everyday disadvantages
3
0.5%
3
25.0%
1.6 Blending work and private life
7
1.2%
4
33.3%
2. Technological knowledge
2.1 Own level of knowledge
11
1.9%
8
66.7%
2.2 Devices used
4
0.7%
3
25.0%
2.3 Knowledge from university education
5
0.9%
3
25.0%
2.4 Knowledge from training programmes
4
0.7%
4
33.3%
2.5 Knowledge from self-development
18
3.1%
8
66.7%
2.6 Earlier studies in the field of technology
4
0.7%
2
16.7%
2.7 Still wishes to learn
2
0.3%
2
16.7%
2.8 Missed from their education
6
1.0%
3
25.0%
3. Technology in education
3.1 Available technology at work
6
1.0%
3
25.0%
3.2 Guiding principles of inclusion
41
7.1%
12
100%
3.3 Ideal/good use of technology
11
1.9%
7
58.3%
3.4 Bad use of technology
19
3.3%
7
58.3%
3.5 Advantages for learners
14
2.4%
8
66.7%
3.6 Advantages for instructors
10
1.7%
8
66.7%
3.7 Examples for integration (anecdotes)
46
7.9%
11
91.7%
3.8 Examples for apps, websites, devices used (names)
23
4.0%
8
66.7%
3.9 Developing learners’ level of English
12
2.1%
6
50.0%
3.10 Preparation for the world of work
17
2.9%
9
75.0%
3.11 Criticality, awareness
29
5.0%
10
83.3%
3.12 Negative experiences
20
3.5%
9
75.0%
3.13 Feedback from the learners
22
3.8%
8
66.7%
3.14 Invested time
9
1.6%
3
25.0%
3.15 Training learners for technology inclusion
12
2.1%
7
58.3%
4. Technology as specialty
4.1 Relevant subject taught and its syllabus
26
4.5%
11
91.7%
4.2 Own research
8
1.4%
5
41.7%
4.3 Material development
12
2.1%
6
50.0%
5. Teacher education and technology
5.1 Special attention to EFL teacher education students
14
2.4%
8
66.7%
5.2 Instructor as an example
19
3.3%
9
75.0%
5.3 Teaching technological pedagogical knowledge
16
2.8%
7
58.3%
6. Distant education (Covid-19)
6.1 LMS systems
19
3.3%
9
75.0%
6.2 Digital testing
18
3.1%
9
75.0%
6.3 Changes in the instructor
17
2.9%
8
66.7%
6.4 Changes in the learners
6
1.0%
5
41.7%
6.5 Aiding colleagues / the institution
14
2.4%
8
66.7%
6.6 Long-lasting consequences
13
2.2%
4
33.3%
6.7 Desired development of technology
16
2.8%
7
58.3%
 
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