ECPR Winter School
University of Bamberg, Bamberg
22 February - 1 March 2019




WD202 - Advanced Qualitative Data Analysis

Instructor Details

Instructor Photo

Marie-Hélène Paré

Institution:
Universitat Oberta de Catalunya

Instructor Bio

Marie-Hélène Paré is an eLearning consultant who lectures in programme evaluation in the Master in Health Social Work at the Open University of Catalonia, and a qualitative data analysis consultant. She was educated in Quebec, Beirut and Oxford, where she read social work.

A clinician by training, Marie-Hélène worked for several years in psychosocial care with survivors of war rape and war trauma in humanitarian emergencies for MSF, MDM and UNRWA in war-torn countries. She moved to academia to research community participation in mental health and psychosocial support and research in humanitarian emergencies, which she studies using mixed methods.

Marie-Hélène has lectured in qualitative data analysis in more than forty universities and research centres worldwide, including universities in Iran and Qatar. Since 2009 she has been an instructor for the annual courses on qualitative data analysis at the ECPR Methods School, and she teaches similar courses at the IPSA-NUS Summer School at the National University of Singapore.

  @TheQualAnalyst


Course Dates and Times

Monday 25 February – Friday 1 March, 09:00–12:30
15 hours over 5 days

Prerequisite Knowledge

Methodological requirements

This course requires understanding of the philosophy underlying critical realist epistemology and some of its associated methods to analyse qualitative data.

I expect you to have experience in analysing qualitative data, including coding and managing a coding scheme; seeking patterns across themes and cases; formalising associations in propositions or falsifying hypotheses against empirical material; representing findings in graphic displays and recording the analytic process in memos. If you have done all the above, you are ready to take this course.

Merely identifying themes in qualitative data and reporting these using quotes is not analysis, and critically falls below the requirements of this course. If you don’t meet the above requirements, I recommend you enrol on an introductory course in qualitative analysis or thoroughly read the prerequisite texts below.

Foundations of QDA

Blaikie, N. W. H. (2010). Research Questions and Purposes (chapter 3 pp. 56-78). Designing social research (2nd ed.). Cambridge: Polity Press.

Gibson, W. J., & Brown, A. (2009). Introduction to qualitative data: analysis in context (chapter 1 pp. 1-14). Working with Qualitative Data. London: Sage.

Spencer, L., Ritchie, J., O'Connor, W., & Barnard, M. (2014). Analysis: Principles and Processes (chapter 10 pp. 269-293). In C. Ritchie, J. Lewis, C. M. N. Nicholls & R. Ormston (Eds.). Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: Sage.

Coding Qualitative Data

Coffey, A., & Atkinson, P. (1996). Concepts and Coding (Chapter 2 pp.26-45). Making Sense of Qualitative Data. Thousand Oaks: Sage

Saldaña, J. (2009). Writing Analytic Memos (Chapter 2). The Coding Manual for Qualitative Researchers (pp. 32-44). London: Sage

Tesch, R. (1990). The Mechanics of Interpretational Qualitative Analysis (Chapter 10 pp.113-134). Qualitative Research: Analysis Types and Software Tools. New York: Falmer Press

Seeking Patterns Across Data

Bazeley, P. (2013). Comparative analyses as a means of furthering analysis (chapter 9 pp. 254-281). Qualitative Data Analysis: Practical strategies. London: Sage.

Bazeley, P. (2013). Relational analysis (chapter 10 pp. 282-323). Idem

Bazeley, P. (2013). If...then...is it because? Developing explanatory models and theories (chapter 11 from pp. 327 to 358). Idem.

Software requirements

You must be an advanced NVivo user to follow the course, meaning that you can teach the basic and advanced functions of NVivo to a colleague. You must know how to independently create nodes, relationship nodes, classifications and sets; set up a framework matrix; run text, coding and matrix queries; use 'see also' links and annotations; and generate maps to depict data and findings. The short course WA113 Introduction to NVivo for Qualitative Data Analysis provides introductory knowledge to NVivo, but not enough to follow this course satisfactorily.

This is a bring-your-laptop course where we use NVivo 12. Download the NVivo 14-day free trial for Windows and Mac. You must ensure that NVivo works well on your machine regardless of the OS, because no technical assistance will be provided at the Winter School. Find more on installation instructions in the Software and Hardware section below.

Short Outline

On this course, you will gain an advanced understanding and applied skills in qualitative content analysis (QCA), thematic analysis (TA), cross-case analysis (CCA) and grounded theory (GT) using NVivo.

The course addresses the gap in the literature and in scholarship training on how to conduct the above methods from the stage of data coding to presenting findings in a CAQDAS environment.

By the end of the course, you will be able to describe the aim and specificities of each method; implement each method’s coding and analytic procedures in NVivo; and assess the quality of reporting of published studies that used the four methods.

This is an advanced course. You should be cognizant of the philosophy underlying critical realist epistemology and some of its associated methods to analyse qualitative data, and you should be an advanced NVivo user.  

Note to prospective participants
The four methods taught in this course have been extensively used across the social sciences, but less in political science. Accordingly, the teaching and quality appraisal exercises draw from an array of disciplines (psychology, education, management, sociology, etc.) If you want to learn the four methods in the context of political science, look for an alternative course to avoid disappointment.    

Tasks for ECTS Credits

2 credits (pass/fail grade) Attend at least 90% of course hours, participate fully in in-class activities and carry out the necessary reading and/or other work prior to, and after, class.

3 credits (to be graded) As above, and complete one task (tbc).

4 credits (to be graded) As above, and complete two tasks (tbc).

Long Course Outline

Who is this course for?
This course is designed for participants who wish to acquire methodological expertise in qualitative data analysis generally and, more specifically, widen their understanding and applied skills in conducting QCA, TA, CCA and GT in NVivo. The course will benefit participants who plan to conduct one of the above methods in their PhD or postdoctoral research, and to those wanting to generally broaden their area of methodological expertise in qualitative research. The course responds well to participants who have collected their data and want to apply the methods’ coding and analytic procedures on their dataset as well as those who don’t have data yet. 

Contribution of this course
Among the methods available to analyse qualitative data qualitatively, the four methods on which this course is based have been widely used across the social sciences. Their procedures to carry out analysis are straightforward, which makes the analytic journey transparent, traceable, and auditable. Each method is also unique in its own right, in that each one suits a particular type of research question; responds to specific objectives; requires a distinct sampling strategy; implements specific coding and analytical procedures; and generates concrete findings.

The course also sheds light on some of the malpractices and misrepresentations from which the four methods suffer in the qualitative literature, because of the lack of standardised training in qualitative analysis and researchers’ obscured reporting. To this end, the course’s daily assignments ask participants to appraise the quality of reporting of published studies that use the four methods.

At the end of this course, you will be able to:

  1. Describe the aim, objectives, and expected outcomes of QCA, TA, CCA and GT
  2. Demonstrate how each method suits a given research design
  3. Implement each method’s coding and analytic procedures in NVivo
  4. Generate graphic displays that match each method’s findings
  5. Appraise the quality of reporting of studies that used the four methods
  6. Propose designs where methods integration is feasible.

Day 1, Qualitative content analysis (Schreier, 2012) Qualitative content analysis is particularly suited to studies that aim to explore and describe the manifest and latent meaning of categories in text, multimedia, pictures, and social media data. In the first part of the class, we review the methodological tenets that distinguish the quantitative from the qualitative approach to content analysis and look at sampling requirements, coding units vs unit of analysis, and the building of a coding frame where categories are organised. This leads us to conduct the initial phase of data coding and conduct a preliminary reliability check to assess the categories' adequacy to capture meaning in the data. In the second part of the day, we move to NVivo, where we aggregate categories in sets and cross-tabulate them in search of coding co-occurrence. We display the results in models where we use both qualitative and quantitative indicators to show the coding occurrence across categories.

Day 2, Thematic analysis (Boyatzis, 1998) Thematic analysis is an indisputably popular method used by qualitative researchers in the social sciences. However, when looking at the different approaches to thematic analysis, Boyatzis' approach is one of the very few that has formalised its procedures in a series of clearly-defined stages known as seeing and encoding themes, codes development, and scoring / clustering of themes. We begin the day by looking at the concepts of pattern recognition and labelling consistency which are fundamental in Boyatzis’ understanding of how a theme is first seen, recognised, and then consistently ascribed the same meaning by the researcher. In the second part of the day, we move to NVivo where we cross-tabulate codes in matrices to find out where coding across themes overlaps. Instances of coding co-occurrence are examined and conceptual associations are formalised in relationship nodes, NVivo's unique feature to put forward propositions, and formulate / falsify hypotheses.

Day 3, Cross-case analysis (Miles & Huberman, 1994) Among the different schools of case study research, the strategies proposed by Miles and Huberman for within- and cross-case analysis have had a tremendous impact in the way qualitative researchers examine similarities and differences across cases, make generalisable claims and promote theoretical elaboration. The first part of this day centres on the first stage of cross-case analysis, that is, a description of what is going on in each case and explanations about why the phenomenon occurs the way it does. We then move on to identify the overall pattern that gives explanation to the overall phenomenon and we formulate propositions about what could happen if similar circumstances were met elsewhere. In the second half of the day, we reproduce these stages in NVivo using matrix queries, memos, 'see also' links, relationship nodes and the model.

Day 4, Grounded theory (Strauss & Corbin, 1998) Grounded theory is often claimed to be the method of choice for many qualitative researchers when conducting qualitative analysis. However, under scrutiny, only a scarce amount of studies actually implement the tenets proposed by the different schools of GT. We start the day by looking at the malpractice of labelling a study 'a grounded theory' to legitimise one’s work while implementing none of the methodology's tenets, and the negative impact that this malpractice has had on the GT representation in academia. In NVivo we examine the association between open coding and theoretical sampling in the generation of categories until saturation is reached. In the second part of the day, the categories of axial coding are applied onto the data and patterns of relationships between categories are identified. We conclude with the phase of selective coding, where a core category is identified and theoretical hypotheses formalised using relationship nodes.

Day 5, Integration & quality appraisal We address the possibilities for methods integration and propose some tools to assess the quality of qualitative analysis. The day starts with a comparative overview of the similarities and differences of the four methods along the epistemological spectrum. This overview leads us to assess how different stances regarding knowledge creation inevitably influence the type of research questions asked, the type of analytic devices each method uses, and the level of abstraction reached in the results they generate. We then look at how, in the analytic process, some of the methods’ features – i.e. approaches to code generation, sampling strategy, means to validate findings – may be combined in a single study, only when it is methodologically justified. In the second half of the day, we review some appraisal tools proposed in the qualitative literature to assess the quality of qualitative analysis.

Teaching & data
Teaching methods include lectures, guided exercises with NVivo, and group work. All four methods will be taught using sample data provided by the instructor. If you have your own data, you are welcome to use them during the guided exercises. If you want to develop your appraisal skills, complete the daily assignments, which consist of assessing the quality of reporting of published studies that used the four methods, outside of class hours.

Day-to-Day Schedule

Day 
Topic 
Details 
1Qualitative content analysis
- The coding frame
- Initial coding
- Final coding

9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions

2Thematic analysis
- Seeing and encoding themes
- Codes development
- Scoring, scaling and clustering, themes

9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions

3Cross-case analysis
- Descriptive, interpretive and pattern codes
- Exploring and describing cases
- Explaining and predicting cases

9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions

4Grounded theory
- Open coding
- Axial coding
- Selective coding

9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions

5Method integration
- Similarities and differences across the methods
- When and why to integrate
- Assessing the quality of QDA

9:00 – 10:30: lectures and group work 10:30 – 10:45: break 10:45 – 12:30: workshop on assessing the quality of QDA

Day-to-Day Reading List

Day 
Readings 
0

Readings are all compulsory as they provide the theoretical grounding and methodological basis for the lectures and exercises in NVivo. Page references in bold below refer to only parts of a given chapter so do look carefully at the pages indicated.

1

Schreier, M. (2012). Qualitative Content Analysis in Practice. London: Sage.

  • Chapter 1. Introduction: What is qualitative content analysis (pp. 1-9 until section The Origins of quantitative content analysis).
  • Chapter 4. The Coding Frame (pp. 58-77 until section Example of how non-saturated….).
  • Chapter 7. Segmentation and Units of Coding (pp. 126-137 until section Example of different definitions…).
  • Chapter 11. How to Present your Results (pp. 220-235 until section Group comparisons).
2

Boyatzis, R. E. (1998). Transforming Qualitative Information: Thematic Analysis and Code Development. Thousand Oaks: Sage.

  • Chapter 1. The Search for the Codable Moment (pp.1-16 until section Latent- Versus Manifest Content Analysis).
  • Chapter 2. Developing Themes and Codes (pp. 29-53).
  • Chapter 6. Scoring, Scaling and Clustering Themes (pp. 128-143).
3

Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook (2nd ed.,). Thousand Oaks: Sage.

  • Chapter 4. Early Steps in Analysis (pp. 55-62 from B. Codes and Coding to sub-section The importance of structure; pp. 69-72 from C. Pattern Coding to section D. Memoing).
  • Chapter 5. Within-case Displays: Exploring and Describing (pp. 90-93 to section Building the Display Format; pp.127-133 from section E. Conceptually Ordered Displays to sub-section Folk Taxonomy).
  • Chapter 6. Within-Case Displays: Explaining and Predicting (pp. 143-148 to section B. Explanatory Effects Matrix; pp.153-155 from section Brief Description to sub-section Getting started).
  • Chapter 7. Cross-Case Displays: Exploring and Describing (pp. 172-177 to section B. Partially Ordered Displays).
4

Strauss, A. L., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.,). Thousand Oaks: Sage.

  • Chapter 1. Introduction (pp. 3-14)
  • Chapter 8. Open coding (pp. 101-105; pp.113-121 from section Discovering Categories).
  • Chapter 9. Axial coding (pp. 123-142).
  • Chapter 10. Selective coding (pp. 143-148 until section Techniques to Aid Integration; pp. 153-161 from section Using Diagrams).
5
  • Barbour, R. S. (2014). Quality of Data Analysis. In U. Flick (Ed.). Qualitative Data Analysis (pp. 496-509). London: Sage.
  • Burns, N. (1989). Standards for qualitative research. Nursing Science Quarterly, 2(1), 44-52.
  • King, N., & Horrock, C. (2010). An Introduction to Interview Data Analysis. (Chapter 9 pp. 158-165 from section Assessing the quality of qualitative analysis to section Writing up a thematic analysis). Interviews in Qualitative Research. London: Sage.
  • Lincoln, Y. S & Guba, E. G. (1985). Appendix A: Audit Trail Categories, Files Types and Evidence. Naturalistic Inquiry (pp. 382-384). Thousand Oaks: Sage.
Software Requirements

NVivo 12 Pro for Windows / NVivo 12 for Mac.

Please bring your own laptop, on which you must run NVivo 12 Pro for Windows or NVivo 12 for Mac.

Download the NVivo 14-day free trial for Windows and Mac

It is your responsibility to ensure that NVivo works well on your laptop before the course. Once you have NVivo installed, verify that it works properly by following the instructions below:

  1. Launch NVivo
  2. On the Start screen (Windows version), in the New section, click Sample Project
  3. On the Welcome to NVivo for Mac screen (Mac version), click Create a copy of the sample project
  4. NVivo opens the sample data project
  5. If you can’t open the sample project, contact QSR International by submitting a support request form online (see section Contact Us Online at the bottom of the page).

Hardware requirements

NVivo 12 Pro for Windows

 

Minimum

Recommended

Processor

1.2 GHz single-core processor (32-bit) 1.4 GHz single-core processor (64-bit)

2.0 GHz dual-core processor or faster

Memory

2 GB RAM or more

4 GB RAM or more

Display

1024 x 768 screen resolution

1680 x 1050 screen resolution

Operating system

Microsoft Windows 7 SP1

Microsoft Windows 7 SP1 or later

Hard disk

5 GB of available hard-disk space

8 GB of available hard-disk space

 

NVivo 12 for Mac

 

Minimum

Recommended

Processor

Intel Core 2 Duo, Core i3, Core i5, Core i7, or Xeon processor

Intel Core i5, Core i7, or Xeon processor

Memory

4 GB RAM or more

8 GB RAM or more

Display

1080 x 800 screen resolution

1440 x 900 screen resolution

Operating system

Mac OS X 10.11 (El Capitan)

Mac OS X 10.11 (El Capitan)

Hard disk

3GB of available disk space

4GB SSD of available disk space

Hardware Requirements

See software requirements.

Literature

Bernard, H. R., & Ryan, G. W. (2010). Analyzing Qualitative Data: Systemic Approaches. Thousand Oaks: Sage.

Boyatzis, R. E. (1998). Transforming Qualitative Information: Thematic Analysis and Code Development. Thousand Oaks: Sage.

Coffey, A., & Atkinson, P. (1996). Making Sense of Qualitative Data: Complementary Research Strategies Thousand Oaks: Sage.

Dey, I. (1993). Qualitative Data Analysis: A User-Friendly Guide for Social Scientists. London: Routledge.

Ezzy, D. (2002). Qualitative Analysis. Practice and Innovation. London: Routledge.

Gibbs, G. R. (2007). Analyzing Qualitative Data. London: Sage.

Gibson, W. J., & Brown, A. (2009). Working with Qualitative Data. London: Sage.

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine De Gruyter.

Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied Thematic Analysis. Thousand Oaks: Sage.

LeCompte, M. (2000). Analyzing Qualitative Data. Theory Into Practice, 39(3), 146-154.

Leech, N. L., & Onwuegbuzie, A. J. (2007). An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation. School Psychology Quarterly, 22(4), 557-584.

Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond Constant Comparison Qualitative Data Analysis: Using NVivo. School Psychology Quarterly, 26(1), 70-84.

Lofland, J., Snow, D., Anderson, L., & Lofland, L. H. (2004). Analyzing Social Settings: A Guide to Qualitative Observation and Analysis (4th ed.). Belmont: Cengage Learning.

Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis (2nd ed.). Thousand Oaks: Sage.

Sandelowski, M. (1995). Qualitative Analysis: What It Is and How to Begin. Research in Nursing & Health 18(4), 371 -375.

Schreier, M. (2012). Qualitative Content Analysis in Practice. London: Sage.

Seale, C. (1999). The Quality of Qualitative Research. London: Sage.

Strauss, A. L. (1987). Qualitative Analysis for Social Scientists. New York: Cambridge University Press.

Strauss, A. L., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Thousand Oaks: Sage.

Tesch, R. (1990). Qualitative Research: Analysis Types and Software Tools. New York: Falmer Press.

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Summer School

Qualitative Data Analysis: Concept and Approaches

Expert Interviews for Qualitative Data Generation

Introduction to NVivo for Qualitative Data Analysis

Winter School

Introduction to NVivo for Qualitative Data Analysis

Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Convenors

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.


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