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Advanced Qualitative Data Analysis - FULLY BOOKED contact to be added to a waiting list

Marie-Hélène Paré

The Qualitative Analyst

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.


Course Dates and Times

Monday 29 February to Friday 4 March 2016
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

Methodological requirements
This course requires solid understanding of the philosophy, practices and outcomes underlying post-positivist methods to analyse qualitative data. Previous experience in analysing one's qualitative data is required including coding and managing a coding scheme, memoing the analytic process in memos, seeking patterns across themes and within-cases, formalising associations in emergent propositions or falsifying hypotheses against empirical material, and representing findings in displays. If you have done all the above, you are ready to take this course. Please note that merely identifying themes in qualitative data and reporting themes using quotes is no analysis and critically falls below the methodological requirements of this course. If you don’t meet the above requirements, I recommend you enrol to an introductory data analysis class instead (see course outline of the upcoming ECPR Summer School) or diligently read the proposed readings at the end of this outline.

Software requirements
This course is solely taught with NVivo 10 for Windows. DO NOT COME TO THE COURSE WITH NVIVO FOR MAC. The NVivo for Mac is incomplete compared to the Windows version. You must be an advanced NVivo 10 for Windows user and know how to create nodes and relationship nodes, manage node classifications and sets, run queries (text, coding, matrix), use see also links and annotations, run cluster analysis and generate models. These functionalities will not be taught in class. The course Introduction to NVivo for Qualitative Data Analysis taught at the WS only covers the basics of NVivo and is insufficient to follow this course.  This is a bring-your-laptop course where you must come with NVivo 10 for Windows installed on your laptop. You can download the 14-day free trial for Windows 10 for Windows here. See section 9 Software and Hardware requirements below for software installation instructions.

Short Outline

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, explain why each method is suitable to answer specific research questions, and assess the quality of studies of each method by means of critical appraisal. Being an advanced course, participants should demonstrate solid understanding of the philosophy, practices and outcomes underlying post-positivist methods to analyse qualitative data. Participants must be advanced NVivo 10 users. NVivo functionalities will not be taught in class.

Note to prospective participants: The four methods which this course is based on have been extensively used across the social sciences, but much less in political science. Accordingly, the teaching and appraisal study examples are drawn from disciplines such as psychology, education, management, sociology, public health, nursing, and human geography. Participants wishing to learn the application of the four methods solely in the context of political science should look for an alternative course to avoid disappointment.

Printable course outline

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 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. For the latter, sample data will be provided.

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 and this makes the analytic journey transparent, traceable, and auditable on the part of the researcher. Each method is unique in its own right in that it suits a particular type of research questions, responds to specific objectives, requires a distinct sampling strategy, implements specific coding procedures, and generates tailored findings. The course also sheds light on some of the malpractices and misrepresentations that the four methods suffer in the qualitative literature both as a result of the lack of standardised training in qualitative analysis and researchers’ sloppy analytic practices and obscured reporting. To this end, the course’s daily assignment involve that participants appraise the quality of studies that used each method in the light of the content seen. The course thus promotes the development of critical skills in assessing other researchers' work as well as self-awareness to accurately report one's analysis.

Learning objectives
At the end of this course, participants will be able to:

  1. Describe the aim, objectives and outcomes of QCA, TA, CCA and GT
  2. Demonstrate how each method suits a given research design
  3. Implement each methods’ analytic procedures in NVivo
  4. Generate graphic displays that match each methods' findings
  5. Appraise the quality of studies that used the methods
  6. Identify situations where methods integration is or not 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. We then review the different ways that codes may be generated using theory-driven, research-driven, data-driven, or an hybrid-driven approach. 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. Theoretical sets are created as second level constructs and the patterns identified are displayed in the model.

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. We reproduce these stages in NVivo using matrix queries, memos, see also links, relationship nodes and the model. In the second half of the class, we apply some of the strategies to describe the similarities and differences across cases. Finally, we examine the conditions under which a phenomenon may be generalised using NVivo's framework matrix.

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 implement 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. We then locate both epistemologically and philosophically the origins of grounded theory as it was initially conceived by Glaser and Strauss in 1967, and look at the methodological developments of what was coined the postpositivist school of GT by Strauss & Corbin. 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. Emerging propositions are sought using matrix queries and those showing depth and width across respondent groups are contrasted with group queries. 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 similarities, differences, and 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. This leads us to examine the criteria that shape these assessment tools and formulate a critique on how some tools succeed, while others and fail, to capture the process of analysis from the stages of data coding, seeking patterns, validating claims and presenting findings.

Teaching & data
Teaching methods include lectures, individual exercises with NVivo, and quality appraisal outside class hours. 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 hands-on exercises. After class, compulsory readings are required for the next day. For those wishing to develop or polish their appraisal skills, quality appraisals are proposed where participants assess the implementation fidelity of each method in published qualitative studies.



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 Data: 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.

NOTE: The class on cross-case analysis is very dense as it also includes the preliminary phase of within-case analysis. That, in total, covers five very dense chapters which exceeds the reading for one day. To decrease the reading load, please choose amongst three chapters amongst the following five based on your interest and level of knowledge. For instance, participants who enrolled in my course Qualitative Data Analysis: Concepts and Approaches at the ECPR Summer School should skip Chapter 4 on coding as this was covered in class (re: descriptive, interpretive and pattern codes). Also, if your primary interest is on within-case analysis and not cross-case analysis, then read chapters 5-6 and choose a third chapter – either chapter 4 on coding or one chapter on cross-case analysis.

Chapter 4. Early Steps in Analysis (pp. 55-72 from section B. Codes and Coding to section D. Memoing).

Chapter 5. Within-case Displays: Exploring and Describing (pp. 90-93 until section Building the Display Format; pp.127-131 from section E. Conceptually Ordered Displays to sub-section Thematic Conceptual Matrix).

Chapter 6. Within-Case Displays: Explaining and Predicting (pp. 143-148 until section B. Explanatory Effects Matrix; pp.151-155 from section D. Causal Network to sub-section Getting started; pp.165-167 from section E. Making and Testing Predictions to sub-section Mechanics of feeding back predictions).

Chapter 7. Cross-Case Displays: Exploring and Describing (pp. 172-177 until section B. Partially Ordered Displays).

Chapter 8. Cross-Case Displays: Ordering and Explaining (pp. 207-208 until section B. Case-Ordered Effects Matrix; pp.222-227 from section E. Causal Models to section Causal Chains).


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 is a bring-your-laptop course where you must come with NVivo 10 for Windows installed on your laptop. You can download the 14-day free trial for Windows 10 for Windows here. DO NOT COME TO THE COURSE WITH NVIVO FOR MAC. The NVivo for Mac is incomplete compared to the Windows version (see comparison table here). If you want to run NVivo 10 for Windows on your Mac, consult the compatibility options here.  It is your responsibility to ensure that NVivo works well on your machine. No troubleshooting will be provided during the course. Once NVivo is installed on your laptop, you must create a test project to ensure that the software works properly. Follow the instructions below.

  1. On your Desktop, launch NVivo by clicking on the NVivo 10 shortcut icon.
  2. In the Welcome interface, below the Get Started heading, click New Project.
  3. This will take you to the NVivo interface. If you get here, this means that NVivo is properly installed on your laptop. No further action is required. Close NVivo.
  4. If you don’t get to the NVivo interface or if the OK button is greyed out at the stage of creating a test project, uninstall NVivo from your computer and reinstall it a second time.
  5. If, after a second installation, you still can’t create a test project you must contact QSR International helpdesk to find out what the problem is. To contact QSR, submit a Customer Support Form.
  6. It is your responsibility to ensure that NVivo runs properly and is installed on time for the course.

Hardware Requirements

As per QSR International
Processor: 2.0 GHz Pentium 4-compatible processor or faster
Memory: 2 GB RAM or more
Hard disk: Approximately 4 GB


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

Boyatzis, R. E. (1998). Transforming Qualitative Data: 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

<p><br /> <strong>Summer School</strong></p> <ul> <li>Qualitative Data Analysis: Concept and Approaches</li> <li>Expert Interviews for Qualitative Data Generation</li> </ul>

Additional Information


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.