Progressive Focusing in Qualitative Research
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* The business and management community increasingly recognises that qualitative research is a ‘messy’, non-linear and often unpredictable undertaking. Yet, a considerable proportion of the qualitative research published in top journals is still presented as the result of a linear, predictable research process, thus wrongly suggesting deductive reasoning. * In this paper, we focus on a particular type of ‘messiness’ where during fieldwork, the research context is revealed to be more complex than anticipated, forcing the researcher to gradually refine/shift their focus to reflect ‘what really matters’. We adopt Stake’s notion of progressive focusing for this gradual approach. * Progressive focusing is well-suited to qualitative research in international business requiring complex iteration between theory and data, and the truthful yet coherent presentation of the research process. We propose that this dual challenge of complexity and trustworthiness may be addressed by using computer-assisted qualitative data analysis software (CAQDAS). * We present conceptual considerations and guidelines and offer a view on a ‘messy’, non-linear doctoral research project conducted using a progressive focusing approach, to demonstrate how CAQDAS can help to develop and re-negotiate insights from theory and interview data, as well as enhance trustworthiness, transparency and publication potential.
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Progressive Focusing in Qualitative Research
- 1. Progressive Focusing and Trustworthiness in Qualitative Research Rudolf R. Sinkovics (Rudolf.Sinkovics@manchester.ac.uk), Professor of International Business, Manchester Business School, UK Eva A. Alfoldi (Eva.Alfoldi@mbs.ac.uk), Lecturer in International Business, Manchester Business School, UK NVivo - Progressive Focusing 1
- 2. Introduction of presenters NVivo - Progressive Focusing 2 1
- 3. NVivo - Progressive Focusing 3 Presenters • Prof Rudolf R. Sinkovics Professor of International Business Rudolf.Sinkovics@mbs.ac.uk www.manchester.ac.uk/research/rudolf.sinkovics • Dr. Eva Alfoldi Lecturer in International Business • Eva.Alfoldi@mbs.ac.uk www.manchester.ac.uk/research/mbs/eva.alfoldi
- 4. Key resource for this eSeminar • Sinkovics, Rudolf R. and Eva A. Alfoldi (2012), "Progressive focusing and trustworthiness in qualitative research: The enabling role of computer-assisted qualitative data analysis software (CAQDAS)," Management International Review, 52 (6), 817-845. (DOI: 10.1007/s11575-012-0140-5). » available: http://www.manchester.ac.uk/escholar/uk-ac-man- scw:137280 • QSR eSeminar audio/video (12th June 2013). » https://www.youtube.com/watch?list=PLNjHMRgHS4FfVcu9S6Ias 4R2mGhv48AKp&v=_-ERqbb-Bqc#t=23 NVivo - Progressive Focusing 4
- 5. Progressive Focusing NVivo - Progressive Focusing 5 2
- 6. NVivo - Progressive Focusing 6 Problem and motivation • Background » Increasing popularity of qualitative IB & IM research (Marschan-Piekkari & Welch 2004; Sinkovics, Penz & Ghauri 2005,2008) » Importance of qualitative data in developing ideas is often downplayed or even concealed (Sutton 1997) • Problem » Depiction and reporting of qualitative work as linear, clean and structured process » Practice of non-linear interaction in qualitative data and theory removed in write-ups » CAQDAS can help but is not a golden bullet, danger of fragmentation and over- simplification of qualitative research (Bryman & Bell 2003, Jack & Westwood 2006) • Motivation » to demonstrate how inherently ‘messy’ process can be made more manageable and transparent through CAQDAS. » Indicate clear transition stages in qualitative work, making reporting transparent (not necessarily replicable) and exemplifying this via NVivo tool.
- 7. Stage 1: “Getting started” Stage 2: Development of research questions and research design Stage 3: Sample and context Stage 4: Data collection and preparation Stage 5: Data analysis Stage 6: Discussion The (well-known) linear model of the qualitative research process ? 7NVivo - Progressive Focusing
- 8. However • While reporting of the process is mostly linear... » actual course different, influenced by accidents, serendipity, on- spot decisions regarding fieldwork (Van Maanen 1998) » raises expectation that credibility, dependability, transferability and confirmability are important (Sinkovics et al. 2008) • while violating principles of integrity and transparency • Amplification of problems to linearity in IB research » Emic-etic tension heightened due to interconnectedness of business landscapes (Dicken 2007) • Emic=subjectivist, qualitative, insider, inductive perspective • Etic=objectivist, quantitative, outsider, deductive perspective » Bias and equivalence issues • Presence of factors that challenge validity NVivo - Progressive Focusing 8
- 9. Sinkovics, Rudolf R., Elfriede Penz, and Pervez N. Ghauri (2008), "Enhancing the trustworthiness of qualitative research in international business," Management International Review, 48 (6), 689- 714. (DOI: 10.1007/s11575-008-0103-z). NVivo - Progressive Focusing 9
- 10. Towards a dynamic, progressive and non-linear process... • Fundamental premise » Flexibility, fluidity of qualitative research is strength » Close interaction between theory development, various foci, data collection and analysis should be acknowledged as fluid and emergent • Researchers may take etic (outide) view but encounter the unexpected (emic, grounded) • Findings evolve through cyclical process of progressive focusing (Parlett and Hamilton 1972, Stake 1981, 1995) • Progressive focusing • ‘Progressive focusing requires that the researcher be well acquainted with the complexities of the problem before going to the field, but not too committed to a study plan. [...]’ (Stake 1981, p.1) • Also termed: cycles of deliberation, abductive approach, zipping, evolution of perspective NVivo - Progressive Focusing 10
- 11. Task 1: Theoretical Basis Choosing a topic, literature review, development of theoretical/conceptual foundations and research questions Task 2: Research Design Choice of research philosophy, design and method(s) Task 3: Sampling & Access Purposive sampling, research context and negotiating access Task 4: Fieldwork Data collection and preparation Task 5: Analysis Analysis, triangulation and constant comparison with theory Task 6: Findings Discussion, conclusions and limitations, writing up Stage 1: “Getting started” Stage 2: Research design and development of research questions Stage 3: Sample and context Stage 4: Data collection and preparation Stage 5: Data analysis Stage 6: Discussion Towards a progressive focusing model of the qualitative research process NVivo - Progressive Focusing 11
- 12. The role of CAQDAS in facilitating and assisting the dynamic and progressive qualitative research process • Model captures the ‘true’ nature of qualitative research » makes a contribution to increasing the legitimacy and acceptance of the ‘messiness’ of qualitative research (Mellor, 2001). » However, inherent danger of flexibility and progressive focusing in qualitative research misinterpreted as call for leniency towards unsystematic research • qualitative research already rife with accusations of lack of rigour (e.g. misuse of grounded theory and opacity in describing research methodology (Jones & Noble, 2007; Suddaby, 2006). • CAQDAS may tackle criticisms » Accommodates non-linear and evolving process » helps in operational management and formalisation of the research NVivo - Progressive Focusing 12
- 13. CAQDAS as ‘mediator’ • Grounded approach » Glaser 1992, Glaser and Strauss 1967 » Inductive » creativity » meaning • Structured approach » Eisenhardt 1998, Yin 2003 » Deductive » Linear qualitative » formalisation » validity NVivo - Progressive Focusing 13 CAQDAS Provides an auditable footprint on the progressive focussing, defines space between opposing views, opens up the dialogue between researcher and data, improves accessiblity with peers and reviewers
- 14. Oct 2003- Oct 2005 Phase 1 Phase 2 Nov 2005- May 2006 Phase 3 Jun 2006- Sep 2006 Phase 4 Oct 2006- Dec 2006 Phase 5 Jan 2007- Aug 2007 Phase 6 Sep 2007- Oct 2007 Phase 7 Nov 2007- Apr 2008 3 pilot interviews (HUN) 14 interviews (HUN) 6 interviews (SLO & CRO) 6 phone interviews (HUN, HQ, CRO, SLO) Conceptualand theoreticalfocus 5 phone interviews HUN, SLO) Fieldworkfocus (country&division) General General / Trade marketing General / Trade marketing General / Trade marketing / Brand marketing Brand marketing Inter-subsidiary knowledge transfer and subsidiary-level knowledge creation HQ-subsidiary knowledge transfer Reverse & secondary KT Regional integration and responsiveness Regional administrative mandates Inter-unit ties and corporate socialisation Intrinsic / extrinsic motivation for knowledge sharing Managerial role stress Choosing topic / Literature review / Access negotiations Ongoing data analysis / Constant comparison of primary data, secondary data and relevant academic literature / ‘Cycles of deliberation’ to refine codes, focus and research questions / Topic coding and analytical coding / Writing Progressive Focusing Approach Member checking / Writing up Interaction and progression: conceptual & fieldwork focus 14NVivo - Progressive Focusing
- 15. NVivo - Progressive Focusing 15 Using CAQDAS during each stage of the research process • Task 1: Choosing a topic, literature review, development of theoretical/conceptual foundations and research questions » “Run a miniature evolutionary system in a head that suffers from bounded rationality” (Weick, 1989, p.529) • How can CAQDAS help? » Document literature searches, title changes, evolving ideas and questions in the form of detailed project memos » Build a literature casebook by systematically extracting information about existing works using “Classifications/ Attributes” function (allows more details than e.g. Endnote, although we recommend using both programmes in conjunction) » Project unity through documenting and analysing literature and fieldwork data within the same CAQDAS file 15
- 16. Example of a literature review casebook 16NVivo - Progressive Focusing
- 17. Using CAQDAS during each stage of the research process (cont’d) • Task 2: Research design » Research design needs to fit the underlying research question(s) and logic, and may need to co-evolve with these • How can CAQDAS help? » Allows source materials on epistemologies, methodological approaches and specific research methods to be systematically catalogued (similarly to literature review items) • Task 3: Sample, context and negotiating access » Largely “hands-on” activities so CAQDAS takes a back seat • How can CAQDAS help? » Memos to document observations (e.g. uncovering and understanding relations between cases, gatekeepers, respondents etc.) 17NVivo - Progressive Focusing 17
- 18. Using CAQDAS during each stage of the research process (cont’d) • Task 4: Data collection and preparation » Requires systematic organisation and easy access • How can CAQDAS help? » Import and organise interviews (audio/video files, transcripts) as well as market reports, company documentation etc. » Use classifications to record and compare information about sources – can create charts to present this effectively • Task 5: Data analysis » Most obvious task to benefit from using CAQDAS » Formally articulate, define and refine themes and codes (nodes) that form the backbone of the analysis and interpretation » Saving multiple versions of evolving coding templates serves as an “audit trail” of the analysis, which helps ensure transparency 18NVivo - Progressive Focusing 18
- 19. Example of charting cases by attribute value 19NVivo - Progressive Focusing
- 20. Topic coding schemes (2006, 2007) and analytical coding scheme (2008) 20NVivo - Progressive Focusing
- 21. Using CAQDAS during each stage of the research process (cont’d) • Task 6: Discussion and final write-up » Central problem of presenting qualitative findings is the lack of access to the interpretation process (Andersen & Skaates, 2004) • How can CAQDAS help? » Use project file as a “protocol document” which traces the procedures followed through each task during the research » Researcher can refer back to the overall project file and use specific details to illustrate the explanation of research methods in the finished write-up » Journal articles are subject to word limits which often make it a challenge to represent the research process truthfully – CAQDAS screenshots such as those on the previous slide can be an efficient way to convey dynamic information in limited space 21NVivo - Progressive Focusing 21
- 22. CAQDAS as ‘golden bullet’? • ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. » software was never intended to replace the researcher’s unique skills in analysing and interpreting complex data (Catterall & Maclaran, 1998; Gordon & Langmaid, 1988; Gummesson, 2005). » Instead, designed to facilitate the organisation and processing of data and enhance the dialogue between researcher and data. » CAQDAS provides a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al., 2002). NVivo - Progressive Focusing 22
- 23. Review NVivo - Progressive Focusing 23 3
- 24. Particular features of NVivo – Usefulness in our discipline? • Most useful features for IB and IM research » Organisation of large and complex qualitative data sets » Facilitates the iteration between theoretical and empirical analysis » Collaboration tool between international researchers » Sharing of codes • Key tips » Familiarise yourself with the key functions and capabilities of CAQDAS at an early stage of your research project » Keep notes and memos throughout the process » Documenting and recording ideas, thoughts, information etc. will become second nature over time, which will be extremely helpful during the writing-up task NVivo - Progressive Focusing 24
- 25. Review NVivo - Progressive Focusing 25
- 26. Resources - further reading NVivo - Progressive Focusing 26
Hinweis der Redaktion
- QSR Seminar resources information http://forums.qsrinternational.com/index.php?showtopic=4848 Seminar 12th June 2013. https://research.mbs.ac.uk/ciber/Portals/0/docs/Free%20eSeminar.pdf YouTube https://www.youtube.com/watch?list=PLNjHMRgHS4FfVcu9S6Ias4R2mGhv48AKp&v=_-ERqbb-Bqc#t=23 http://youtu.be/_-ERqbb-Bqc?list=PLNjHMRgHS4FfVcu9S6Ias4R2mGhv48AKp It is generally acknowledged in the business and management literature that qualitative research tends to be ‘messy’. In contrast to the typical linear structure of the quantitative research task (find or develop a theory, gather empirical data, confirm or disconfirm the theory), qualitative findings often emerge through a complex process of gradual evolution, driven by the interaction between theory and data. This iterative, cyclical process can be considered a hallmark of qualitative research. It lies at the heart of terms such as evolution of perspective (Peshkin, 1985), zipping (Orton, 1997), systematic combining (Dubois & Gadde, 2002), cycles of deliberation (McGaughey, 2004, 2007) and the term we adopt in this chapter, progressive focusing (Parlett & Hamilton, 1972; Stake, 1981, 1995). QSR EXPERT DISCOVERY http://www.qsrinternational.com/nvivo-experts.aspx http://www.youtube.com/watch?v=_-ERqbb-Bqc
- Silvana
- NVivo eSeminar
- Silvana/Rudolf
- NVivo eSeminar
- Rudolf Stage 1, “getting started” refers to the initial preparations for empirical research, such as generating a topic and conducting a literature review. Stage 2 includes the task of developing the underlying research questions and the research design that is deemed most appropriate for investigating these questions. Stage 3 entails choosing a sample and a context (which, in qualitative research, generally means theoretical sampling). Stage 4 is the crucial stage of collecting empirical data and preparing it for further analysis through digitisation and transcription. Preliminary analysis often takes place during this stage. Stage 5 consists of focused, formal analysis of the empirical data and embedding it in the existing theoretical/conceptual background. Finally, Stage 6 involves a discussion of the findings of the research – including the researcher’s interpretations – and articulating the contribution of the research to the wider academic field. The six stages are viewed as following on from one another in an orderly fashion, although the length of each stage may vary considerably. Most qualitative researchers encounter this model early on in their careers, either explicitly in widely used textbooks (e.g. Iacobucci and Churchill 2010; Marschan-Piekkari and Welch 2004; Yin 2003) and articles such as e.g. Eisenhardt (1989), or implicitly in discussions with advisors and peers. The model is commonly absorbed as ‘the right way to do research’ and often results in expectations of a linear research process, with seamless transitions between its stages.
- Rudolf Quality criteria (qual / quant) ... Guba and Lincoln 1989 Credibility = validity Dependability = reliability Transferability = generalisability Confirmability = objectivity Emic-etic tension in international research: With increasing interconnectedness of business landscapes (Dicken 2007), qualitative researchers are challenged to transcend political or cultural boundaries and thus make philosophical decisions about the comparative nature of their investigations. While this theoretical trajectory may be equally valuable in any type of qualitative research, the tension is even bigger regarding international or more specifically cross-cultural research traditions and the fundamental understanding of how to address comparative issues. Berry (1989) points out that some scholars propose to work intensively within a single cultural context in order to discover and comprehend indigenous phenomena, while others advocate research across cultures that produces results that are valid throughout these contexts. This substantive split in research orientations, which is often seen as dichotomous and contrasting view, is referred to as “emic” versus “etic” approach (Headland, Pike and Harris 1990; Pike 1966). Sinkovics et al. (2008) argue that research should take more emic (i.e. subjectivist/ qualitative/ insider) perspectives, which then could be translated into etic (i.e. objectivist/quantitative/outsider) terms and used as valuable input for further studies. Following this proposition, however, further challenges the traditional linear model of reporting research findings, as comparisons become ever more difficult, multiple data collection units are involved and operational challenges related to the philosophical emic or etic positions are more pronounced. In operational terms, the negotiation and re-negotiation of concepts, the interaction between theoretical position and the qualitative process will be fundamentally more difficult. Bias and equivalence issues in international research: A bias is indicated by the presence of factors that challenge the validity of cross-cultural comparisons and investigations (Poortinga 1989). Only when there is no bias, is it possible to establish equivalence in international research. Van de Vijver and Poortinga (1997) discuss biases such as construct bias, method bias and item bias. Sinkovics et al. (2008) provide examples how these challenges related to biased results are pertinent in various stages of the qualitative research process and how CAQDAS may help to deal with these issues.
- Sinkovics, Rudolf R., Elfriede Penz, and Pervez N. Ghauri (2008), "Enhancing the Trustworthiness of Qualitative Research in International Business," Management International Review, 48 (6), 689-714. Abstract and Key Results: Reliability, validity, generalisability and objectivity are fundamental concerns for quantitative researchers. For qualitative research, however, the role of these dimensions is blurred. Some researchers argue that these dimensions are not applicable to qualitative research and a qualitative researcher’s tool chest should be geared towards trustworthiness and encompass issues such as credibility, dependability, transferability and confirmability. This paper advocates the use of formalised and software-based procedures for the analysis and interpretation of qualitative interview data. It is argued that International Business research, with a focus on international datasets, equivalence issues, multiple research environments and multiple researchers, will benefit from formalisation. The use of software programmes is deemed to help to substantiate the analysis and interpretation of textual interview data. http://dx.doi.org/10.1007/s11575-008-0103-z
- Eva For similar concepts, see also cycles of deliberation (McGaughey 2004, 2007), systematic combining/abductive approach (Dubois and Gadde 2002), zipping (Orton 1997) and evolution of perspective (Peshkin 1985).
- Eva Step 1 of the research task encompasses several key tasks facing the researcher at the start of a new research project: choosing a topic and conducting a literature review, in order to build the theoretical and conceptual foundations of the research. This step includes the articulation of basic assumptions, logic and expectations, as well as developing the research questions – a task that is closely intertwined with the literature review, since research questions should be clearly rooted in the theoretical foundations and literature gaps identified through a review of existing research. Step 2 focuses on the logic behind operationalising the research questions. The researcher draws up a ‘blueprint’, seeking a good fit between theoretical foundations, epistemological assumptions and practical feasibility issues. Building sound logic and coherent ideas, with the input of fellow academics, forms an essential part of this step. Building on the first two steps, Step 3 is concerned with moving the research ‘out of theory and into the field’ by carefully choosing a sample and a context. Admittedly, in many cases, sampling and context are influenced by pragmatic issues such as pre-existing contacts or ease and level of access – as a result, Step 3 could involve prolonged negotiations, or alternatively even precede Step 1. Once the first three steps have been taken, it is time for the researcher to enter the field in earnest. Step 4 contains the task of collecting and preparing primary data, whilst Step 5 consists of analysing that data and embedding it in what is known from the literature or other (often secondary) data sources. In our model, these steps are tightly linked not only with each other, but also with Step 1. The arrows linking Steps 1, 4 and 5 highlight the constant comparison of data with literature, a key aspect of a grounded approach (see Glaser & Strauss, 1967; Strauss & Corbin, 1998). We argue that this constant comparison has considerable potential to influence the completion of the research task. If the data – collected in Step 4 and analysed in Step 5 – is found to have sufficient fit with the theoretical and conceptual framework and research questions developed in Step 1, the researcher may move on to Step 6, which involves developing and articulating the key arguments and overall contributions of the research. However, the analysis in Step 5 may reveal that the data does not fit sufficiently with the framework and questions developed in Step 1; or it may expose previously unconsidered emic constructs that are of particular interest to the research and require further investigation. In such cases, it may make sense to return to the field for more data, and/or return to the literature to refine the underlying theoretical and conceptual foundations. Depending on the complexity of the data, the researcher’s interpretations and the emergent theoretical framework, any or all of Steps 1, 4 and 5 may be repeated before finally moving on to Step 6. The goal of theoretical saturation (Glaser & Strauss, 1967; Strauss & Corbin, 1998) and practical constraints (such as interviewees’ availability) mean that researchers may end up alternating between the three steps until such a point where they are satisfied that their theoretical focus, empirical data and potential contribution are in line with one another. The point at which this is achieved – and thus the number of iterations between Steps 1, 4 and 5 that are required – may differ widely across research projects. Whilst the optimal number of iterations cannot be prescribed ex ante, the process can be managed in a systematic manner through the use of CAQDAS. In our terminology, etic refers to constructs that are developed by the researcher and take an outside view of the phenomenon under investigation, whilst emic refers to native constructs emerging from inside the research setting and reflecting the insider's view of reality (see Buckley & Chapman, 1997; Sinkovics et al., 2008).
- Rudolf ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. The use of CAQDAS is suggested to accommodate for the non-linear and evolving process of interaction between qualitative data and the theoretical and conceptual backbones of research, while helping in the operational management and formalisation of the research. To this end, CAQDAS is simply seen as a meritorious tool, that helps in legitimising the acknowledgement of complexity and ‘messiness’ in the reporting of qualitative research, and encourages greater transparency and credibility, otherwise called “trustworthiness” (Ghauri and Firth 2009; Sinkovics, Penz and Ghauri 2005, 2008). CAQDAS provide a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al. 2002). Following Little’s “decision calculus”, we believe qualitative research will benefit from a “[...] set of procedures for processing data and judgements to assist [...] in decision making.” (Little 1970). These procedures are to be “simple, robust, easy to control, adaptive, complete on important issues and easy to communicate” (Little 2004, p.1855) and we believe that, when used appropriately, CAQDAS allows qualitative researchers a “dialogue with the computer” and thus a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field – in effect, creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data. In doing so, we believe that CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates today: the highly inductive grounded theory approach promoted by Glaser (Glaser 1992; Glaser and Strauss 1967), and the highly structured, deductively oriented, linear qualitative analysis advocated by e.g. Eisenhardt (1989) and Yin (Yin 2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers, whilst accommodating progressive focusing.
- Rudolf
- Eva
- Eva ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. The use of CAQDAS is suggested to accommodate for the non-linear and evolving process of interaction between qualitative data and the theoretical and conceptual backbones of research, while helping in the operational management and formalisation of the research. To this end, CAQDAS is simply seen as a meritorious tool, that helps in legitimising the acknowledgement of complexity and ‘messiness’ in the reporting of qualitative research, and encourages greater transparency and credibility, otherwise called “trustworthiness” (Ghauri and Firth 2009; Sinkovics, Penz and Ghauri 2005, 2008). CAQDAS provide a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al. 2002). Following Little’s “decision calculus”, we believe qualitative research will benefit from a “[...] set of procedures for processing data and judgements to assist [...] in decision making.” (Little 1970). These procedures are to be “simple, robust, easy to control, adaptive, complete on important issues and easy to communicate” (Little 2004, p.1855) and we believe that, when used appropriately, CAQDAS allows qualitative researchers a “dialogue with the computer” and thus a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field – in effect, creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data. In doing so, we believe that CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates today: the highly inductive grounded theory approach promoted by Glaser (Glaser 1992; Glaser and Strauss 1967), and the highly structured, deductively oriented, linear qualitative analysis advocated by e.g. Eisenhardt (1989) and Yin (Yin 2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers, whilst accommodating progressive focusing.
- Eva
- Eva ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. The use of CAQDAS is suggested to accommodate for the non-linear and evolving process of interaction between qualitative data and the theoretical and conceptual backbones of research, while helping in the operational management and formalisation of the research. To this end, CAQDAS is simply seen as a meritorious tool, that helps in legitimising the acknowledgement of complexity and ‘messiness’ in the reporting of qualitative research, and encourages greater transparency and credibility, otherwise called “trustworthiness” (Ghauri and Firth 2009; Sinkovics, Penz and Ghauri 2005, 2008). CAQDAS provide a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al. 2002). Following Little’s “decision calculus”, we believe qualitative research will benefit from a “[...] set of procedures for processing data and judgements to assist [...] in decision making.” (Little 1970). These procedures are to be “simple, robust, easy to control, adaptive, complete on important issues and easy to communicate” (Little 2004, p.1855) and we believe that, when used appropriately, CAQDAS allows qualitative researchers a “dialogue with the computer” and thus a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field – in effect, creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data. In doing so, we believe that CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates today: the highly inductive grounded theory approach promoted by Glaser (Glaser 1992; Glaser and Strauss 1967), and the highly structured, deductively oriented, linear qualitative analysis advocated by e.g. Eisenhardt (1989) and Yin (Yin 2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers, whilst accommodating progressive focusing.
- Eva ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. The use of CAQDAS is suggested to accommodate for the non-linear and evolving process of interaction between qualitative data and the theoretical and conceptual backbones of research, while helping in the operational management and formalisation of the research. To this end, CAQDAS is simply seen as a meritorious tool, that helps in legitimising the acknowledgement of complexity and ‘messiness’ in the reporting of qualitative research, and encourages greater transparency and credibility, otherwise called “trustworthiness” (Ghauri and Firth 2009; Sinkovics, Penz and Ghauri 2005, 2008). CAQDAS provide a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al. 2002). Following Little’s “decision calculus”, we believe qualitative research will benefit from a “[...] set of procedures for processing data and judgements to assist [...] in decision making.” (Little 1970). These procedures are to be “simple, robust, easy to control, adaptive, complete on important issues and easy to communicate” (Little 2004, p.1855) and we believe that, when used appropriately, CAQDAS allows qualitative researchers a “dialogue with the computer” and thus a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field – in effect, creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data. In doing so, we believe that CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates today: the highly inductive grounded theory approach promoted by Glaser (Glaser 1992; Glaser and Strauss 1967), and the highly structured, deductively oriented, linear qualitative analysis advocated by e.g. Eisenhardt (1989) and Yin (Yin 2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers, whilst accommodating progressive focusing.
- Eva
- Eva
- Eva ‘Computer-Assisted Qualitative Data Analysis Software’ may convey an inappropriate sense of the software taking over the analytical process. The use of CAQDAS is suggested to accommodate for the non-linear and evolving process of interaction between qualitative data and the theoretical and conceptual backbones of research, while helping in the operational management and formalisation of the research. To this end, CAQDAS is simply seen as a meritorious tool, that helps in legitimising the acknowledgement of complexity and ‘messiness’ in the reporting of qualitative research, and encourages greater transparency and credibility, otherwise called “trustworthiness” (Ghauri and Firth 2009; Sinkovics, Penz and Ghauri 2005, 2008). CAQDAS provide a toolset for the analysis of abundant qualitative data that can be understood similar to decision support systems used by practitioners (Shim et al. 2002). Following Little’s “decision calculus”, we believe qualitative research will benefit from a “[...] set of procedures for processing data and judgements to assist [...] in decision making.” (Little 1970). These procedures are to be “simple, robust, easy to control, adaptive, complete on important issues and easy to communicate” (Little 2004, p.1855) and we believe that, when used appropriately, CAQDAS allows qualitative researchers a “dialogue with the computer” and thus a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field – in effect, creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data. In doing so, we believe that CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates today: the highly inductive grounded theory approach promoted by Glaser (Glaser 1992; Glaser and Strauss 1967), and the highly structured, deductively oriented, linear qualitative analysis advocated by e.g. Eisenhardt (1989) and Yin (Yin 2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers, whilst accommodating progressive focusing.
- Rudolf Following Little’s decision calculus, we believe qualitative research can benefit from a ‘set of procedures for processing data and judgements to assist [...] in decision making’ (Little, 1970). These procedures are to be ‘simple, robust, easy to control, adaptive, complete on important issues and easy to communicate’ (Little, 2004, p.1855). When used appropriately, CAQDAS allows qualitative researchers to have a ‘dialogue with the computer’ and develop a greater degree of effectiveness and rigour at each step of the research process. This is achieved through documenting the interactive process of going forwards and backwards between theory and the field. By creating an auditable ‘footprint’ of the progressive dialogue between the researcher and their data, CAQDAS can help researchers define the space in between the two opposing views that dominate qualitative research debates: the highly inductive grounded theory approach promoted by Glaser (Glaser, 1992; Glaser & Strauss, 1967), and the highly structured, deductively oriented qualitative analysis advocated by Eisenhardt (1989) and Yin (2003). In essence, the debate between these opposing views is a debate about the relative importance of creativity versus formalisation, of meaning versus validity. We believe that the two are equally important and achievable through an emphasis of strong research logic, flexibility and thorough documentation. In particular, CAQDAS can assist qualitative researchers in managing each step of the research process and in making their methodology more accessible to peers and reviewers. Nonetheless, it should be recognised that in qualitative research, the realistic purpose of a systematic audit trail is not to ensure replicability, but precisely to explain the idiosyncrasies of each qualitative research project that preclude replicability. As such, we argue that CAQDAS can enable the production of robust and defensible qualitative research that can stand up to close scrutiny.
- Rudolf & Eva
- Rudolf & Eva Despite concerns about CAQDAS fostering a temptation to quantify, fragment or over-simplify qualitative research (Bryman & Bell, 2003; Jack & Westwood, 2006), our experience leads us to concur with Kelle (1997) that these dangers have been exaggerated. Through the empirical example provided in this chapter, we have not only demonstrated the progressive focusing process of a typical qualitative research project, but also showed how CAQDAS can be used to open up dialogues and interaction between human and computer (Little, 2004) and enhance the credibility of research through systematisation and record-keeping (Ghauri & Firth, 2009; Sinkovics et al., 2005, 2008). We argue that, far from obscuring the inherently ‘messy’ and constantly evolving nature of qualitative research, CAQDAS can be used to facilitate the analysis of large volumes of data through systematic comparison with theory. The role of CAQDAS in this fluid and dynamic interaction between theory and data is to aid a more formalised process that potentially makes qualitative inquiry more logical, transparent and trustworthy. To this end, we hope that this chapter contributes to overcome the artificially linear reporting of qualitative research towards a more ‘real-world’ presentation, whilst also observing requirements of rigour and trustworthiness in data and reporting. CAQDAS helps... Facilitates the organisation and processing of data Enhances dialogue between researcher and textual data Improves the communication of qualitative research (selling the ‘story’) Transparency improvements – audit-trail and efficient management of large volumes of qualitative data
- Silvana/Rudolf