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Advanced Qualitative Data Analysis

Course Dates and Times

Monday 5 to Friday 9 March 2018
15 hours over 5 days

Marie-Hélène Paré

The Qualitative Analyst

This course provides participants with advanced understanding and applied skills in conducting qualitative content analysis (QCA), thematic analysis (TA), cross-case analysis (CCA) and grounded theory (GT) using NVivo. The course addresses the gap both in the literature and in scholarship training on how to conduct the four above methods from the stage of data coding to presenting findings in a CAQDAS environment. Upon completion of the course, participants 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. Being an advanced course, participants should be cognizant of the philosophy underlying critical realist epistemology and some of its associated methods to analyse qualitative data, and be advanced NVivo users.  

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.) Participants wishing to learn the four methods in the context of political science should look for an alternative course to avoid disappointment.    

Printable course outline


Tasks for ECTS Credits

  • Participants attending the course: 2 credits (pass/fail grade) The workload for the calculation of ECTS credits is based on the assumption that students attend classes and carry out the necessary reading and/or other work prior to, and after, classes.
  • Participants attending the course and completing one task (see below): 3 credits (to be graded)
  • Participants attending the course, and completing two tasks (see below): 4 credits (to be graded)

Instructor Bio

Marie-Hélène teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis. She was educated in Quebec, Beirut and Oxford where she read social work. A clinician by training, she worked as a mental health officer in humanitarian missions for MSF, MDM and UNWRA in psychosocial aid programs for survivors of war trauma in East Africa and the Middle East. Her clinical work led her to research the harm that INGOs can do in the name of doing good when imposing Western paradigms in culturally and politically different contexts.

Marie-Hélène is an NVivo Certified Platinum Trainer and is a member of the NVivo Core Trainer Team who teaches the NVivo online courses. She is a sought-after methodologist who has taught qualitative data analysis in more than sixty universities and research centres worldwide, in countries including Qatar and Iran. Since 2009, Marie-Hélène has taught the introductory and advanced courses in qualitative data analysis at the ECPR Methods School and teaches similar courses at the IPSA-NUS Summer School in Singapore. Her methodological interests range from advances in qualitative data analysis, qualitative evidence synthesis, decolonising epistemology and participatory methodologies. 

Read more about Marie-Hélène.


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 to participants who plan to conduct one of the above method 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 that 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

Amongst the methods available to analyse qualitative data qualitatively, the four methods which this course is based on 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 questions; 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 that the four methods suffer from in the qualitative literature, both because of the lack of standardised training in qualitative analysis and researchers’ obscured reporting. To this end, the course’s daily assignment involve that participants appraise the quality of reporting of published studies that used the four methods.


Learning objectives

At the end of this course, participants 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): Day 1 opens with qualitative content analysis as proposed by Schreier (2012). Qualitative content analysis is a method that is particularly suited for studies that aim to explore and then 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 proceed with looking 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 class, we move to NVivo where we aggregate categories in sets and cross-tabulate them in the 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 indisputably a 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 open the class 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 class, 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): Amongst 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, so to make generalisable claims and promoting theoretical elaboration. The first part of the class 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 with identifying the overall pattern that gives explanation to the overall phenomenon and we formulate propositions about what could happened if similar circumstances would be met elsewhere. In the second half of the class, 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 by many qualitative researchers when conducting qualitative analysis. However, under scrutiny, only a scarce amount of studies actually implements the tenets proposed by the different schools of GT. The malpractice of labelling a study "a grounded theory" to legitimise one’s work while none of the methodology's tenets have been implemented, and the negative impact that this malpractice has had on the GT representation in academia, opens the first part of the class. 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 class, the categories of axial coding are applied onto the data and patterns of relationships between categories are identified. We conclude the class with the phase of selective coding, where a core category is identified and theoretical hypotheses are formalised using relationship nodes.


Day 5 Integration & quality appraisal: Day 5 addresses the possibilities for methods integration and proposes some tools to assess the quality of qualitative analysis. The class opens with a comparative overview of the similarities and differences of the four methods along the epistemological spectrum. This overview brings 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 codes generation, sampling strategy, means to validate findings - may be combined in a single study only and when this is methodologically justified. In the second half of the class, we review some appraisal tools that have been 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. Participants that have their own data are welcomed to use them during the guided exercises. For those wishing to develop their appraisal skills, the daily assignments, which consist in assessing the quality of reporting of published studies that used the four methods, are proposed outside class hours.

Methodological requirements

This course requires understanding of the philosophy underlying critical realist epistemology and some of its associated methods to analyse qualitative data. Previous experience in analysing qualitative data is necessary 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. Note that merely identifying themes in qualitative data and reporting these using quotes is no analysis, and critically falls below the requirements of this course. If you don’t meet the above requirements, I recommend you enrol to 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). 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 that runs before this course provides introductory knowledge to NVivo is not enough to follow this course satisfactorily.


This course is taught using NVivo 11 Pro for Windows. You must bring your laptop and run this version of NVivo or alternatively run NVivo 11 Plus. The 14-day free trial can be downloaded here. This course is unsuitable for NVivo 11 for Mac as this version is incomplete compared to Windows. You can run NVivo 11 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only, your Mac meets the system requirements here. See the section Software and Hardware requirements below for installation instructions.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


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 at the time of change.

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, please contact us before registering.

Day Topic Details
1 Qualitative content analysis - Initial coding - Final coding - Reporting 9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions
2 Thematic analysis - Seeing and encoding themes - Codes development - 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
3 Cross-case analysis - Exploring and describing - Explaining and predicting - Displaying results 9:00 – 10:30: lectures and hands-on sessions 10:30 – 10:45: break 10:45 – 12:30: lectures and hands-on sessions
4 Grounded 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
5 Integration & assessment - Similarities and differences - Possibilities for integration - Quality assessment tools 9:00 – 10:30: lectures and group work 10:30 – 10:45: break 10:45 – 12:30: The second part of the class is a group workshop where key concepts and tasks taught during the week, and which need further discussions and hands-on practice, will be reviewed.
Day Readings

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).

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).

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).

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).
  • 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.

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.

Software Requirements

This course requires that you run NVivo 11 Pro for Windows on your laptop or, alternatively, NVivo 11 Plus. You can download the 14-day free trial here. DO NOT COME TO THE COURSE WITH NVIVO 11 FOR MAC as this version is incomplete compared to NVivo 11 Pro for Windows. Mac users should consult the compatibility options and system requirements to run NVivo 11 Pro for Windows using Boot camp or Parallels on their Mac. It is your responsibility to ensure that NVivo works well on your laptop as no troubleshooting will be provided at the Winter School.


Once NVivo is installed on your laptop, verify that it works properly. Follow the instructions below.

  1. On your Desktop, launch NVivo by clicking on the NVivo 11 shortcut icon
  2. On the Start screen, in the New section, click Sample Project
  3. NVivo opens a copy of the sample project which is stored in your default project location.
  4. 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).


NVivo system requirements  





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

2.0 GHz dual-core processor or faster


2 GB RAM or more

4 GB RAM or more


1024 x 768 screen resolution

1680 x 1050 screen resolution or higher

Operating system

Microsoft Windows 7

Microsoft Windows 7 or later

Hard disk

Approximately 5 GB of available hard-disk space (additional hard-disk space may be required for NVivo project data)

Approximately 8 GB of available hard-disk space (additional hard-disk space may be required for NVivo project data)



Hardware Requirements

See software requirements.


  • 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.

Recommended Courses to Cover Before this One

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