Monthly Archives: November 2016

Using spreadsheets as a qualitative analysis tool

Don’t have access to qualitative data analysis software? This week’s blog post provides some tips on how to prepare your transcripts to work within Microsoft Excel or Google Sheets.


By Dustin De Felice and Valerie Janesick

Are you in the middle of a research project? Or are you just starting and thinking about the possible tools you may need during analysis? Given the variety of choices out there, we discuss one choice not often thought of in qualitative research: spreadsheets. While there are a number of spreadsheet programs or Apps available, we focus on the steps needed to prepare your files for these two: MS Excel and Google Sheets. Either of these spreadsheets are good options, though each one has its own advantages (view our handout for a visual overview). In De Felice and Janesick (2015), we outline steps to take with Excel. For this discussion, we focus on preparing your transcripts so that they are ready to be imported into a spreadsheet. We recommend starting with the technique described by Meyer and Avery (2009). They provide an effective and simple technique for importing transcription files into Excel (version 2003). They recommend using Excel because it handles large amounts of data (numeric and text). It provides the researcher with ways of adding multiple attributes during the analysis process. Lastly, it allows for a variety of display techniques that include various ways of filtering, organizing and sorting.

In De Felice and Janesick, (2015), we recommend using Excel because the format mirrors many phenomenological methods (Giorgi, 2009; Moustakas, 1994). In addition, spreadsheet programs are powerful, yet not as overwhelming as other data analysis tools. In De Felice and Janesick (forthcoming), we extend our recommendation beyond just the use of Excel because there are a number of features available when utilizing Google Sheets. One benefit of Sheets is that it is widely available and most features are free. It allows for collaborative opportunities (including same time/same sheet editing). This feature is available within Excel, but there are a number of barriers that can limit this functionality (e.g. cost, different versions, etc.). If you are in the process of choosing between Excel and Sheets, we encourage you to read this blog post. In general, we recommend either spreadsheet for most projects, though there are some compelling reasons to use one over the other.

In terms of design, we recommend asking yourself this question before transcribing: “What unit do you have in mind when you think about your analysis?” (and Mayer & Avery, 2009). The answer to this question can help you decide how to best use either program. For example, will you use the turns in a conversation as a unit of measure, or will you use the phrase or sentence level as a unit? Of course, there are other ways to analyze the data, so we recommend starting with Meyer and Avery and their discussion on the “codable unit” (2009, p. 95-96). Before describing some of our steps, we would like to note that these steps are most effective for a researcher conducting and transcribing interviews (from audio).

Once you have identified that codable unit, you need to establish some conventions within your transcriptions to ensure your work will import. In Excel and Sheets, you can use these as separator characters: Tabs, Commas or Custom (essentially any single symbol including a hard tab as shown in figure 1). These separator characters will dictate how your text will fit into the cells and across the rows and down the columns. While there are ways of changing these separator characters, it is best to establish a convention within your transcriptions and stick with it throughout your research project.

Figure 1: Screenshot of the options available for importing a file into Google Sheets.


This import step is necessary because spreadsheets are not the easiest tools to use when transcribing. Instead we recommend using a word processor to create your transcripts in a text (.txt) format. In figure 2, we provide a screenshot from within a word processor. In the background of the screenshot, we have an example transcription that used a turn in conversation as the codable unit and the colon as a separator character.

Figure 2: Screenshot of Google docs file that we downloaded as plain text (.txt).


We have used different word processors and our preference for working with transcriptions is to use MS Word. We provide detailed steps in De Felice and Janesick (2015) on p. 1585. Our main reason for this choice is that there is no way to show formatting marks in Google Docs (see Figure 3 for a list of formatting marks available in MS Word. These features are not visible in Google Docs). These formatting marks are essential in properly preparing the transcription for importation. There is hope for an eventual add-on to correct this oversight in Google Docs, but there is currently no workaround.

Figure 3: Screenshot of formatting marks within MS Word.


Once your text file is ready for import, you can use the spreadsheet as an analysis tool. For phenomenological studies, we outline a few ways of using Excel in this capacity in De Felice and Janesick (2015). However, we recommend throughout your research project that you keep in mind this fantastic advice from Meyer and Avery: “All research projects (and researchers) are not the same. What works for one project may not be best for another.” (2009, p. 92) We offer the same advice for our suggestions here.



De Felice, D., & Janesick, V. J. (2015). Understanding the marriage of technology and phenomenological research: From design to analysis. The Qualitative Report, 20(10), 1576-1593. Retrieved from

Giorgi, A. (2009). The descriptive phenomenological method in psychology. A modified Husserlian approach. Pittsburgh, PA: Duquesne University Press.

Meyer, D. Z., & Avery, L. M. (2009). Excel as a qualitative data analysis tool. Field Methods, 21, 91-112. DOI: 10.1177/1525822X08323985

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.


Five-Level QDA

As we look ahead to ICQI 2017, we are sharing a few more reflections on this year’s conference. Here, Christina Silver and Nicholas Woolf describe their work on Five-Level QDA


Christina Silver and Nicholas H Woolf, Five-Level QDASM

In the Digital Tools Stream at ICQI 2016 we presented two papers discussing our work to develop and implement the Five-Level QDASM Method, a CAQDAS pedagogy that transcends methodologies, software programs and teaching modes. The first, called “Operationalizing our responsibilities: equipping universities to embed CAQDAS into curricular” was presented by Christina in the opening plenary session. The second, called “Five-level QDA: A pedagogy for improving analysis quality when using CAQDAS” was presented jointly by Nicholas and Christina.  Here we briefly summarize these two papers. You can find out more about the Five-Level QDA method and our current work by visiting our website.

Responsibilities for effective CAQDAS teaching in the digital age

There is an expanding range of digital tools to support the entire process of undertaking qualitative and mixed methods research, and current generations of students expect to use them (Paulus et al., 2014), whatever their disciplinary, methodological or analytic context. Although many researchers use general-purpose programs to accomplish some or all of their analysis, we focus on dedicated Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages. CAQDAS packages are now widely used and research illustrates that uptake continues to increase (White et al. 2012; Gibbs, 2014; Woods et al. 2015). However, there’s little evidence that their use is widely embedded into university curricula. There may be several reasons for this (Gibbs, 2014), including the difficulty of attending to diverse learner needs, which are affected by learners’ methodological awareness, analytic adeptness and technological proficiency (Silver & Rivers, 2015).

These issues highlight the importance of developing effective ways of embedding CAQDAS teaching into university curricula. This long-standing issue has been debated for as long as these programs have been available, and it is widely agreed that the appropriate use of digital technologies must be taught in the context of methodology (Davidson & Jacobs, 2008; Johnston, 2006; Kuckartz, 2012; Richards, 2002; Richards & Richards, 1994; Silver & Rivers, 2015; Silver & Woolf, 2015). However, few published journal articles illustrate teaching of CAQDAS packages as part of an integrated methods courses (recent exceptions are Paulus & Bennett, 2015; Bourque & Bourdon, 2016 and Leitch et al., 2016). Several reflective discussions regarding the integration of methodological, analytical and technological teaching are insightful and useful (e.g., Carvajal, 2002; Walsh, 2003; Davidson & Jacobs, 2008; di Gregorio & Davidson, 2008), as are concrete examples of course content, modes of delivery, course assignments and evaluation methods (e.g., Este et al., 1998; Blank, 2004; Kaczynski & Kelly, 2004; Davidson et al., 2008; Onwuegbuzie et al., 2012; Leitch et al., 2015; Paulus & Bennett, 2015; Bourque & Bourdon, 2016; Jackson, 2003). However, these writers provide varying degrees of detail about instructional design and are contextually specific, focusing on the use of a particular CAQDAS program, a disciplinary domain, and/or a particular analytic framework. Their transferability and pedagogical value may therefore be limited where there is an intention to use different methodologies, analytic techniques and software programs.

There are clearly challenges and lack of guidance in the literature for teaching qualitative methodology, analytic technique and technology concurrently. Although the challenges are real they need not be seen as barriers. A pedagogy that transcends methodologies, analytic techniques, software packages and teaching modes could prompt a step-change in the way qualitative research in the digital environment is taught (Silver & Woolf, 2015). The Five-Level QDA method  is designed as such a pedagogy with the explicit goal of addressing these challenges.

The Five-Level QDA Method: a CAQDAS pedagogy that spans methodologies, software packages and teaching modes

The Five-Level QDA method (Woolf, 2014; Silver & Woolf, 2015; Woolf & Silver, in press) is a pedagogy for learning to harness CAQDAS packages powerfully. The phrase “harness CAQDAS packages powerfully” isn’t just a fancy way of saying “use CAQDAS packages well”, but means using the chosen software from the start to the end of a project, while remaining true throughout to the iterative and emergent spirit of qualitative and mixed methods research. It isn’t a new or different way of undertaking analysis, but explicates the unconscious practices of expert CAQDAS users, developed from our experience of using, teaching, observing and researching these software programs over the past two decades. It involves a different way of harnessing computer software from a taken-for-granted or common sense approach of simply observing the features on a computer screen and looking for ways of using them.

The principles behind the Five-Level QDA Method

The core principle is the need to distinguish analytic strategies – what we plan to do – from software tactics – how we plan to do it. As uncontroversial as this sounds, strategies and tactics in everyday language are commonly treated as synonyms or near-synonyms, leading unconsciously to the methods of a QDA and the use of the CAQDAS package’s features being considered together as a single process of what we plan to do and how we plan to do it. A consequence of this is that the features of the software to either a small or a large degree drive the analytic process.

The next principle is recognizing the contradiction between the nature of CAQDAS which is to varying degrees iterative and emergent, and the predetermined steps or cut-and-dried nature of computer software. When this is not consciously recognized, either the strategy is privileged, with the consequence that the software is not used to its full potential throughout a project; or the tactics are privileged, with the consequence that the iterative and emergent aspects of a QDA are suppressed to some degree. However, when the contradiction is consciously recognized it becomes necessary to reconcile it in some way. One approach is through a compromise, or trade-off, in which the analytic tasks of a project are raised to a more general level and expressed as a generic model of data analysis in order to more easily match the observed features of CAQDAS packages (terms in italics have a specific meaning in Five-Level QDA).

The Five-Level QDA method, following Luttwak’s (2001) five level model of military strategy, takes a different approach to reconciling the contradiction between strategies and tactics by placing it in a larger context in order to transcend the contradiction. Regardless of research design and methodology, there are two levels of strategy – the project’s methodology and objectives (Level 1), and the analytic plan (Level 2) that arises from those objectives. There are similarly two levels of tactics – the straightforward use of software tools (Level 4) and the sophisticated use of tools (Level 5). We use the term tools in a particular way. We are not referring to software features, but ways of acting on software components – things in the software that can be acted upon. Whereas CAQDAS packages have hundreds of features, they have far fewer components, typically around 15-20.

The critical middle level between the strategies and tactics is the process of translation (Level 3).Rather than raise the level of analytic tasks to the level of software features, the level of analytic tasks is lowered to the level of its units, which are then matched, or translated, to the components of the CAQDAS package. This method ensures that the direction of action of the process is always initiated in a single direction: from analytic strategies to software tactics, and never the other way around. This ensures that the analytic strategies drive the analytic process, not the available features of the software. Because translation operates at the level of individual analytic tasks the method is relevant across methodologies and software programs. Figure 1 provides an overview of the Five-Level QDA method.

Figure 1. Five-Level QDA Chart

two levels of strategy >>>>> translated to >>>>> two levels of tactics
Level 1 Level 2 Level 3 Level 4 Level 5

The purpose and context of a project, usually expressed as research questions and a methodology

Analytic plan

The conceptual framework and resulting analytic tasks


Translating from analytic tasks to software tools, and translating the results back again

Selected tools

Straightforward choice of individual software operations

Constructed tools

Sophisticated use of software by combining operations or performing them in a custom way

Several people at our presentations fed-back to us that this chart implies the process is linear and hierarchical, which of course is misleading because all forms of QDA are to varying degrees iterative and emergent. Since ICQI, therefore, we have created a diagram that we hope more accurately reflects the iterative, cyclical nature of the process (Figure 2).

Figure 2. Five-Level QDA diagram


Tools for teaching the Five-Level QDA Method

In order to illustrate translation in the context of learners’ own projects we have developed two displays: Analytic Overviews (AOs) and Analytic Planning Worksheets (APWs). AOs provide a framework for the development of a whole project, described at strategies Levels 1 and 2, whereas APWs scaffold the process of translation, facilitating the skill of matching the units of analytic tasks to software components in order to work out whether a software tool can be selected, or needs to be constructed. We don’t have space here to illustrate translation in detail, but further details about teaching the Five-Level QDA method via the use of AOs and APWs are discussed in Silver & Woolf (2015) and Woolf & Silver (in press).

Implementing and researching the Five-Level QDASM Method

The Five-Level QDA method provides an adaptable method for teaching and learning CAQDAS. Since 2013 we’ve been using it in our own research and teaching in many different contexts, and we’ve used feedback from our students and peers to refine the AOs and APWs. We’re also and working with several universities to improve provision of CAQDAS teaching, using the Five-Level QDA method in different learning contexts.

We believe that the Five-Level QDA method is adaptable to a range of instructional designs including different face-to-face workshop designs and remote learning and via textbook and complimentary video-tutorials (Woolf & Silver, in press). This is because it provides a framework through which qualitative and mixed methods research and analysis can be taught at the strategies level within the context of digital environments. Instructional designs that teach qualitative methodology and analytic techniques via the use of CAQDAS packages illustrate that qualitative methodology and technology need not be introduced to students separately (e.g. Davidson et al, 2008, Bourque 2016, Leitch 2016). The Five-Level QDA method pre-supposes that it is not possible to adequately teach technology without methodology, but we would also argue that in our increasingly digital environment it is increasingly less acceptable to adequately teach methodology without technology. The intentionally separate emphasis given to analytic strategies and software tactics within a single framework enables the teaching of methodology and technology concurrently within an instructional design that is adaptable to local contexts, as well as serving as a method to harness CAQDAS for researchers’ own projects.

We are currently evaluating our work and welcome opportunities to work with others to implement and further research the effectiveness of the Five-Level QDA method in different contexts.


Blank, G. (2004). Teaching Qualitative Data Analysis to Graduate Students. Social Science Computer Review22(2), 187–196.

Bourque, C. J., & Bourdon, S. (2016). Multidisciplinary graduate training in social research methodology and computer-assisted qualitative data analysis: a hands-on/hands-off course design. Journal of Further and Higher Education9486(April), 1–17.

Carvajal, D. (2002). The Artisan ’ s Tools . Critical Issues When Teaching and Learning CAQDAS. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research3(2, Art 14).

Davidson, J. (n.d.). Thinking as a Teacher : Fully Integrating NVivo into a Qualitative Research Class.

Davidson, J., & Jacobs, C. (2008). The Implications of Qualitative  Research Software for Doctoral  Work. Qualitative Research Journal8(2), 72–80.

Davidson, J., Jacobs, C., Siccama, C., Donohoe, K., Hardy Gallagher, S., & Robertson, S. (2008). Teaching Qualitative Data Analysis Software ( QDAS ) in a Virtual Environment : Team Curriculum Development of an NVivo Training Workshop. In Fourth International Congress on Qualitative Inquiry (pp. 1–14).

Este, D., Sieppert, J., & Barsky, A. (1998). Teaching and Learning Qualitative Research With and Without Qualitative Data Analysis Software. Journal of Research on Computing in Education31(2), 17.

Gibbs, G. R. (2014). Count: Developing STEM skills in qualitative research methods teaching and learning. Retrieved from

Jackson, K. (2003). Blending technology and methodology. A shift towards creative instruction of qualitative methods with NVivo. Qualitative Research Journal, 3(Special Issue), 15.

Johnston, L. (2006). Software and Method: Reflections on Teaching and Using QSR NVivo in Doctoral Research. International Journal of Social Research Methodology9(5), 379–391.

Kaczynski, D., & Kelly, M. (2004). Curriculum Development for Teaching Qualitative Data Analysis ONline. QualIT 2004: International Conference on Qualitative Research in IT and IT in Qualitative Research, (November), 9.

  1. Leitch, J., Oktay, J., & Meehan, B. (2015). A dual instructional model for computer-assisted qualitative data analysis software integrating faculty member and specialized instructor: Implementation, reflections, and recommendations. Qualitative Social Work

Onwuegbuzie, A. J., Leech, N. L., Slate, J. R., Stark, M., Sharma, B., Frels, R., … Combs, J. P. (2012). An exemplar for teaching and learning qualitative research. The Qualitative Rport17(1), 646–647. Retrieved from

Paulus, T. M., & Bennett, A. M. (2015). “I have a love–hate relationship with ATLAS.ti”TM: integrating qualitative data analysis software into a graduate research methods course. International Journal of Research & Method in Education, (June), 1–17.

Silver, C., & Rivers, C. (2015). The CAQDAS Postgraduate Learning Model: an interplay between methodological awareness, analytic adeptness and technological proficiency. International Journal of Social Research Methodology5579(September), 1–17.

Silver, C., & Woolf, N. H. (2015). From guided-instruction to facilitation of learning : the development of Five-level QDA as a CAQDAS pedagogy that explicates the practices of expert users. International Journal of Social Research Methodology, (July 2015), 1–17.

Walsh, M. (2003). Teaching Qualitative Analysis Using QSR NVivo 1. The Qualitative Report8(2), 251–256. Retrieved from

White, M. J., Judd, M. D., & Poliandri, S. (2012). Illumination with a Dim Bulb? What do social scientists learn by employing qualitative data analysis software (QDAS) in the service of multi-method designs? Sociological Methodology42(1), 43.–76.

Woods, M., Paulus, T., Atkins, D. P., & Macklin, R. (2015). Advancing Qualitative Research Using Qualitative Data Analysis Software (QDAS)? Reviewing Potential Versus Practice in Published Studies using ATLAS.ti and NVivo, 1994–2013. Social Science Computer Review, 0894439315596311.

Woolf, N. H., & Silver, C. (in press). Qualitative analysis using ATLAS.ti: The Five-Level QDA Method. London: Routledge.

Woolf, N. H., & Silver, C. (in press). Qualitative analysis using MAXQDA: The Five-Level QDA Method. London: Routledge.

Woolf, N. H., & Silver, C. (in press). Qualitative analysis using NVivo: The Five-Level QDA Method. London: Routledge.

Transparency, QDAS, and Complex Qualitative Research Teams

by Judith Davidson

Judith Davidson is an Associate Professor in the Research Methods and Program Evaluation Ph.D. Program of the Graduate School of Education, University of Massachusetts Lowell.  She is a founding member of the ICQI Digital Tools Special Interest Group.  She is currently working on a book about complex qualitative research teams, which she is writing in NVivo!

Qualitative researchers increasingly find themselves working as members of a complex research team. Multiple members, multiple disciplines, geographically dispersed—these are just some of the forms of diversity that we face in our research endeavors.  Many of these research teams employ Qualitative Data Analysis Software (QDAS).

While there are many reasons to use QDAS in complex team research, the one I wish to talk about here is support of transparency: making clear how results were reached or showing proof of the process of interpretation that indicates the conclusions are believable. Transparency has long been held up as a virtuous and important notion in qualitative research, but as with many things in qualitative research, many of our descriptions relate to individually conducted research, not team-based research projects. Moreover, our considerations of transparency have not yet made much sense of qualitative research conducted with QDAS.

As part of the 2016 ICQI Digital Tools strand and a panel examining issues related to QDAS use with complex teams, I presented a paper titled “Qualitative Data Analysis Software Practices in Complex Qualitative Research Teams:  Troubling the Assumptions about Transparency (and Portability)” (Davidson, Thompson, and Harris, under review) that sought to get at some of the issues that arise at the nexus of complex teams, qualitative research, QDAS, and transparency.

Our paper applied Jackson’s notion of transparency-in-motion (Jackson, 2014) to the methodological process of a complex team project in which we had been engaged, Building a Prevention Framework to Address Teen “Sexting” Behaviors, or the ‘Sexting Project’  (Harris et al; Davidson, 2014).  Jackson’s ideas were derived from a study of ‘lone ranger’ researchers, doctoral students using QDAS in their own, individual research work.  In contrast, the goal of our paper was to demonstrate how Jackson’s descriptive categories of transparency-in-motion (triage, show, and reflect) are enacted by real teams working with real world restrictions that teams often face in trying to use QDAS.  In the article, we follow the development of one finding from the Sexting Project, the continuum of sexting, to show how QDAS use wove in and out of the stages of triage, show, and reflect as this term evolved for the research team (Davidson, 2011).

The Sexting Study was conducted by a multi-disciplinary team located at three institutions of higher education in three regions of the United States.  Focus group data about views of teen sexting was collected from three separate audiences at these locations; teens, teen caregivers, and those who worked with and for teens.  It was one of the first qualitative research studies conducted on the topic of sexting.  All data collected for the study was organized in an NVivo database maintained by the lead site.

The following table gives a quick and dirty overview of the discussion from the paper.

Sub-categories of Jackson’s notion of Transparency-in-motion (2014) Applications from the Sexting Project (Harris, et al, 2013), illustrating the malleable relationship of transparency and QDAS
Triage: Emphasize, Sort, Classify In NVivo, Davidson and Thompson coded focus group responses to “Why do youth sext?”  Noticed differences in regard to relationship, peer group, and gender.
Show:  Share, Illustrate, Hold-up In full team meetings, Davidson and Thompson used NVivo to examine the responses to this question and to dig down into the differences noted.
Reflect:  Examine Content, Negotiate Meaning Full group reflects and develops the notion of “the continuum of sexting”. Davidson and Thompson return to recode the responses in NVivo as points on this continuum: Mutual Interest; Self Interest; Intent to Harm

Analysis of our process demonstrated that NVivo fulfilled possibilities for triage-in-motion (triage, show, and reflect) through deep individual analysis with the tool and broader episodic analysis with the full research team.  Despite differential access to NVivo by team members (only lead team had a site license), the tool was able to offer all researchers better opportunities for working with the data and visualizing relationships within the data. Despite these restrictive circumstances, the QDAS tool could play this role, because there was senior leadership knowledgeable and experienced in the use of the tool who could support full group opportunities to use and think with it.

These findings indicate Jackson’s notion of transparency-in-motion has relevance to both individual researchers and research teams.  Using the key criteria of triage, show, and reflect, researchers were able to manage the use of QDAS as they continuously worked toward transparency-in-motion under less than ideal conditions.   Discussions of transparency have long been prominent in qualitative research, but we suggest that in today’s post-modern/post-structural world transparency-in-motion may be a more useful perspective for qualitative researchers to adopt.



Davidson, J. (2011).  Qualitative research network at UMass Lowell:  Brown Bag on “Giving Birth to Theory in Qualitative Research:  Adolescents and the Sexting Continuum”. Wednesday November 9, 2011.  

Davidson, J. (2014).  Sexting: Gender and Teens.  The Netherlands: Sense Publications.

Davidson, J., Thompson, S., Harris, A., (under review).  Qualitative Data Analysis Software Practices in Complex Qualitative Research Teams:  Troubling the Assumptions about Transparency (and Portability).

Harris, A., Davidson, J., Letourneau, E., Paternite, C., Miofshky, K.T. (September 2013.  Building a Prevention Framework to Address Teen “Sexting” Behaviors.  (189 pgs).  Washington DC: U.S. Dept. of Justice Office of Juvenile Justice & Delinquency Prevention.

Jackson, K. (2014).  Qualitative Data Analysis Software, Visualizations, and Transparency: Toward an Understanding of Transparency in Motion.  Paper presented at the Computer Assisted Qualitative Data Analysis conference, May 3, 2014.  Surrey, England.


Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice, which funded the project.