Using information and communication technologies to support qualitative research

The use of information and communication technologies (ICT) such as computer assisted qualitative data analysis software (CAQDAS) is a relatively neglected topic in the qualitative research literature. Mason (2002b, p. 164) sums up qualitative researchers’ ambivalence towards

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Since the completion of the fieldwork, the HSE ‘Area A’ has implemented a new computerised client record system (RAISE) that will greatly assist the collection and analysis of this type of data in the future.

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technology when she highlights that ‘qualitative researchers have been said to be notoriously, and not always productively, anti-technology’. Technological developments since 2000 in digital audio recording, Internet speeds, computer software and hardware, qualitative data analysis software and the low cost of data storage devices, suggested some potential for these technologies to support and enhance this qualitative research study.

Recent methodology texts are more sanguine about the merits of using technology in qualitative studies, while also expressing some important epistemological and technical caveats (Mason, 2002b; Sarantakos, 2005; Richards and Morse, 2007). Indeed, Richards (2005), who is involved in the development of nVivo, argues that all researchers, irrespective of their methodological orientation, should no longer avoid using computers in their research. This section examines the use of technologies in the collection, management and analysis of qualitative data, and in the production of transcripts. Other innovative uses of the Internet as a method of data collection, such as the use of online focus groups, online participant observation, online social surveys, ethnographies of online communities and online asynchronous interviews, are not discussed as they are outside of the scope of this chapter (see Sarantakos, 2005).

3.5.1 Qualitative data analysis software and grounded theory

Much of the debate around the use of computers in qualitative research centres on their use in the analysis of qualitative data. The design of any research project should attend to the development of a data management plan: the collection of data, data storage and data retrieval, all of which computers can support more readily than pen, paper and card systems. One of the concerns associated with the use of qualitative data analysis software was that earlier versions were limited to text retrieval (Lee and Fielding, 2004). Furthermore, like SPSS (software used for the analysis of statistical data), another concern was that qualitative data analysis programmes would ‘do the analysis’ for the qualitative researcher, thereby divorcing them from the analytical process (Flick, 1998). Other concerns relate to quantitative style ‘counting’ and ‘theory building’ features offered by some programmes. Mason (2002b, p. 164) argues that the latter closely resembles the ‘logic of variable analysis’, which may be incompatible with a researcher’s methodological stance. CAQDAS software applications usually facilitate the indexing and cross-referencing of categories, but this strength can also be a drawback as it opens up more indexing possibilities. Another limitation of CAQDAS applications is the potential to facilitate ‘stepwise’ conversion of qualitative data into quantitative data. This involves assigning uniform numeric values to statements (textual data), which then facilitates a quantitative analysis of this data (Sarantakos, 2005). This type of conversion and analysis is inconsistent with the principles of qualitative research.

Until my introduction to Atlas.ti, the draft analysis of interview transcripts during the pilot study had been undertaken on paper and by using the ‘comment’ function in Microsoft Word. This

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approach was sufficient for this stage as the transcripts were in their first draft, and it assisted the development of the interview guide and some initial categories of analysis. What became apparent was that this approach to indexing and the development of categories made it difficult to keep data on similar categories or concepts together. It was difficult to organise the data in order to undertake more analytical coding and to answer analytical questions such as: When social workers talk about leaving child protection and welfare, is their experience of supervision and support from managers a factor in this decision? Following a one-day training course on Atlas.ti and the purchase of this application by University College Cork, I explored the potential of this application to support my qualitative data analysis. As I saw it, the merits of this application were its coding functions (creating codes, merging codes, linking categories, and attaching memos to codes), quick retrieval of data, memoing, an audit trail to record research decisions, improved data storage and backup over the paper and pen method, the flexibility of carrying the dataset around from place to place, increased security and its capacity to keep quotations within their original context. Criticisms of the traditional scissors, coloured pens and copied abstracts approach to coding and data analysis are that it removes textual data from its original context, which may lead to misinterpretations (Richards, 2005). One key advantage of software applications such as Atlas.ti and nVivo is that they do not remove data from its original context. This facilitates the researcher to interpret the data within the context of the preceding and following transcript text, as the quotation is not ‘cut’ from the transcript.

While there are a number of CAQDAS applications other than Atlas.ti (WinMax, nVivo, and NUD*IST being the most popular), Atlas.ti was chosen for its congruence with data analysis in grounded theory (Richards and Morse, 2007), I had access to the programme and I had also received training on its use. My approach to using the application was that it supported my analysis of a very large data set, I had control over the coding rather than it being done ‘automatically’ for me by the application, it enabled me to keep memos together, undertake analysis across ‘families’ (for example, team, age) and to link them with codes/quotations, and the speed at which you could search and retrieve data was very attractive. A ‘stepwise’ conversion of the data was not appropriate or desirable as it was incongruent with my methodological orientation and the quantitative word count/statistical features were not used in the analysis. Once I had developed a degree of competence in using Atlas.ti’s tools, I returned to the three earlier transcripts coded in Microsoft Word and I recoded them in Atlas.ti. All 45 interview transcripts were ‘assigned’ to Atlas.ti (version 5.2.18) and analysed using grounded theory.

In his text on qualitative methods in organisational research, Lee (1999) highlights the dominance of grounded theory studies in organisational research. Grounded theory provides specific strategies and approaches for the analysis of large amounts of unstructured qualitative data, and the aim of this method is to develop theory from the data (Punch, 1998; Willot and Griffin, 1999). An overview of the historical development of grounded theory and the ‘schism’ between the

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original developers of the method (Glaser and Strauss) is outside the scope of this section (see Charmaz, 2006). Grounded theory has been subject to much criticism over the last 40 years, with critiques focusing on the continual redevelopment of the method, the overemphasis on procedures, eschewing particular ways of making sense of the world while ‘elevating’ a certain kind of thinking, the fracturing of datasets in the pursuit of order, and the ‘dismissal’ of people’s own accounts of their social world (Thomas and James, 2006). The application of grounded theory in its entirety by researchers in research design and the analysis of qualitative data has been questioned by Bryman and Burgess (1994, p. 220) who suggest that it has ‘… alerted qualitative researchers to the desirability of extracting concepts and theory out of data. Second, grounded theory has informed, in general terms, aspects of the analysis of qualitative data, including coding, and the use of different types of codes and their role in concept creation’. Taking into account Bryman and Burgess’s (1994) observations regarding researchers’ selective use of this method and criticisms of the method, the researcher felt there were some merits in using some of the method’s procedures and concepts to support the research design and data analysis. The main features of the method used in this study were a focus on memo writing, coding, and the use of theoretical and purposive sampling in the research design.

3.5.2 Data collection, digital recording of interviews and the production of transcripts The research literature highlights the importance of making audio-recordings of qualitative research interviews: it produces a verbatim account of the interview, it is a method of recording participants’ pauses and tone, and it enables the production of a text/transcript that becomes the basis for a detailed qualitative analysis (Ritchie and Lewis, 2003; Bryman, 2004; Richards and Morse, 2007). In my research, the informed consent process addressed the audio recording of interviews by giving advance notice in the letter of invitation and by discussing its importance during the informed consent process at the beginning of each interview. Participants were given the option to being interviewed without being recorded, but all 43 interviewees agreed to have their interviews recorded. One participant requested that the audio file (permission obtained to retain transcript) of her interview should be destroyed after the PhD was fully completed37_.

The standard technology used to record interviews has been the use of analogue tape recorders. Analogue tape recorders have come under criticism due to the poor audio fidelity of the recordings (Fook, 1996) resulting in transcribers straining to discern what is being said, possibly leading to a lesser accuracy of transcript. It has been argued that the technical quality of recordings and the adequacy of transcripts contributes to the reliability of qualitative research (Peräkylä, 2004). My previous unsatisfactory experience of using an analogue tape recorder in a research study (Burns, 2006), producing poor-quality audio recordings and leading to subsequent problems with transcribing the interviews, suggested that a review of alternatives was necessary.

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In recent times, the use of digital audio recorders such as Sony Mini-disc recorders have become more popular as they improve the technical quality of recordings (Bryman, 2004). A limitation to audio recording interviews is that body language, which may also provide interesting data, is not recorded. 42 out of 43 of my interviews were recorded using a Sony Hi-MD NH700 Mini-disc recorder. This device produced high-quality audio recordings and contributed to the production of more accurate transcripts as there were very few inaudible remarks and fewer transcription errors due to fatigue and ‘guessing’. Mini-disc cartridges were stored securely and digital copies were stored on a computer with a strong password. Having the interviews in the MP3 format on a laptop allowed me to listen back again to interviews on an iPod while travelling, and through the laptop while writing up the dissertation.

What Bryman does not discuss are the technical issues associated with using Mini-disc recorders. Transcribers have complained that the audio quality is ‘too good’ as the recorder and microphone clearly picked up extraneous noises, such as passing pedestrian traffic, which were distracting. Difficulties with transcribing the interviews were caused by Sony imposing a digital rights management system (DRM), which at the time, permitted recorders to use only their niche proprietary audio format (ATRAC). Problems with software to playback these ATRAC files led to difficulties in transcribing the interviews. Sony’s proprietary ATRAC format is limited to playback on SonicStage software which runs on PCs only, SonicStage was unsuitable as a transcription application and transcribers did not have their own Mini-disc recorders. The resolution of these issues consumed a significant amount of time in the pilot study (these issues are described in Appendix F). Furthermore, the recorder malfunctioned on one occasion. For this interview, the interviewer recorded as much as he could remember of the interview in his research journal on the train journey home, and the participant was advised of the malfunction and was sent a copy of these notes, but she made no changes. Since the research interviews were undertaken, technology has progressed and there is now a wider choice of digital recorders that obviate many of the issues experienced in this study.

One interview was undertaken and recorded over the Internet using Skype, a regular phone and AudioHijack (see Appendix F). This participant lived outside Ireland and she agreed to a synchronous Internet interview that was recorded using these technologies. This interview was similar to a regular telephone interview, except that the researcher spoke through a computer which recorded the interaction. This method is different to asynchronous interview methods such as email, where there is a significant time lag between responses. The merits of this approach were the time saved by not travelling to another country, and the ease with which a digital copy of the interview was made and subsequently transcribed. The demerits of using this method were a lower quality of audio recording than the Mini-disc recorder, the lack of a physical presence between the participant and researcher, and some disruption to the ‘flow’ of the interview. All of

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the interviews were transcribed in full, except for the welcome and informed consent discussions in each interview.

3.5.3 Voice-to-text software and the production of transcripts

The production of a transcript from talk and its primacy in qualitative research (Silverman, 2004), led me to examine alternative methods of transcribing the research interviews. Kvale (1996) argues that the production of transcripts is not without its limitations and involves an artificial interpretative construction of a written text from an oral communication. Transcription can be inaccurate, decisions and judgements will need to be made regarding mumbled words, where a sentence ends and whether emotional aspects are included (e.g. tone of voice, crying, long pauses, and so on). The transcription of 43 by approximately 1.5 hour interviews is a significant labour and as I worked full-time while also undertaking this research study, I spent some time examining voice-to-text software as a possible way to reduce the transcription workload. However, the ‘holy grail’ of a computer that ‘listens’ to your digital audio recordings and then produces a ‘faithful text’, is not presently available. As discussed earlier, the quality of the interview transcript is intrinsically linked with the reliability of the study (Peräkylä, 2004) and it was also important to value and respect participants’ contributions by ensuring that the transcripts were of a high quality. The current available method is the ‘journalist’s solution’: ‘teach’ a voice-to-text software application to recognise your voice (for example: IBM ViaVoice, Dragon Naturally Speaking), listen to the interview on headphones and speak what you hear into a microphone connected to the computer, and the computer ‘translates’ your voice into text on the computer screen.

This method was utilised to transcribe one interview (No. 2) and led to the production of a transcript that was approximately 60% accurate. While the method produced a draft transcript quickly, the accuracy of the transcript was inadequate, which led to a lengthy and time-consuming editing process. The researcher transcribed six of the interviews using the regular transcription method and the other 37 were transcribed by professional transcribers who were paid for their services. The transcribers agreed to a contract which included a confidentiality clause, the return of all original audio files/digital media, the deletion of all audio files from their computers, and they were not to discuss the content of the interviews. All of these transcripts were reviewed by the researcher to ensure the accuracy of the transcription, but few changes were made as they were of a very high quality. Interviews were sent for transcription immediately after each interview to facilitate the ongoing data analysis.

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