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Religion and Political Theory: Secularism, Accommodation and The New Challenges of Religious Diversity, Edited by Jonathan Seglow and Andrew Shorten

SD101A - Qualitative Data Analysis: Concepts and Approaches

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 30 July - Friday 3 August

Group 1: 09:00-10:30 / 11:00-12:30

Group 2: 14:00-15:30 / 16:00-17:30

Prerequisite Knowledge

Basic knowledge of qualitative research is necessary. This is a bring-your-laptop course where NVivo software is used for demonstration only. No knowledge of NVivo is required. This is not a course on how to analyse qualitative data with NVivo. For such course, see Introduction to NVivo for Qualitative Data Analysis

Short Outline

Are you planning to conduct interviews or focus groups for your data collection, or perhaps do participant observation during events or meetings? Or will you be collecting policy papers, press articles, or Internet data from blogs, Facebook or Twitter? If you do any of the above, you will soon or later have to face the pile of data you collected and analyse them. Will you know how? This course provides strategic understanding and applied skills in planning, conducting, and reporting the process of qualitative data analysis (QDA) in one’s research. The course teaches the foundational concepts underlying the process of qualitative analysis and explores, in sequence: (1) the influence that epistemology and research design has on choosing an analytical method; (2) approaches to code qualitative data, that is, deciding the right coding unit, efficiently managing a coding scheme, developing meaningful categories and memoing the coding process judiciously; (3) strategies to transform qualitative data into findings and the nuts and bolts of retrieval procedures; (4) debates surrounding the quality of qualitative research and the techniques to confirm the trustworthiness of qualitative results and (5) best and worse practices when presenting qualitative findings including crafting visual displays that tell the story of the findings effectively and appraising the quality of reporting of qualitative analysis in published studies. The course blends lectures and applied exercises and daily assignment after class hours. Participants conducting qualitative research as part of their PhD, postdoc, or for government or research centres will particularly benefit from this course.

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)

To receive 2 ECTS, you will have done the readings and taken part actively in the course.

In addition, 2 other credits may be earned upon the production of daily assignments and an academic essay. Daily assignments involve the production of a short paper (700-900 word) that answers questions about the concepts seen every day in class. They are produced outside class hours and must be sent by email at the end of each day. Four assignments must be produced in total. The academic essay is a longer paper (4000 words) and must demonstrates the knowledge and skills gained over the course of the week, and their application in one’s research. Participants have until August 10th 2018 to produce the essay.

Long Course Outline

Please see full outline.

What is qualitative data analysis?

Qualitative analysis is the search for patterns in textual, visual or artefact data to uncover associations, identify relationships and propose explanations about the studied phenomenon. The process of analysis involves several steps, often described in sequence although much intertwined in practice, known as getting familiar with the material, coding the data, identifying patterns and generating results and presenting findings. A range of methods of qualitative analysis exists (thematic analysis, grounded theory, content analysis, etc.) all of which come with their epistemological standpoint, sampling requirements, coding procedures, techniques to generate findings and quality criteria. This course does not focus on a method of qualitative analysis in particular; rather, it explores the common denominators all methods share and provides tools and food for thought to implement them in practice.

Why is this course relevant?

Historically, qualitative analysis has been criticised for being opaque and subjective given that it is sometimes difficult to see how researchers went from hundreds of interview pages to a handful of conclusions since no discussion is provided about what the researchers actually did when they analysed their data. For this reason, qualitative analysis has been associated with a ‘black box’ problem: we are told about what data and how they were collected and what results these yielded, but nothing in between. There is also a misconception that qualitative analysis merely involves the identification of themes in the data and the reporting of themes in the form of quotes, often lots of them. The analytical task researchers should be concerned about - that is, the examination of commonalities and differences across units of analysis, discovering patterns and relationships across the data, and synthesizing these in a storyline, model, or schema - is most frequently than not absent in published qualitative studies. Conversely, the widespread myths that the ‘method will emerge’ from the data, or that researcher’s tacit knowledge will lead him/her to ‘make sense’ of the data, account for opaque reporting since no audit trail accompany the analytic process.

What contribution does this course make?

This course teaches the key concepts to plan, conduct and report qualitative analysis in a transparent, traceable and auditable way in one’s research. Participants will learn (1) key design issues to take into account when planning the phase of analysis in qualitative study, (2) different approaches to code qualitative data, (3) strategies to transform qualitative data into findings, (4) debates about the quality of qualitative research and techniques to confirm the trustworthiness of qualitative findings and (5) best and worse practices when presenting qualitative findings in doctoral theses and journal articles. The different stages of qualitative analysis will be demonstrated with NVivo software so participants can put into practice the concepts seen and become cognizant of the advantages and pitfalls of using QDA software. To that end, participants are invited to use NVivo 12 Pro for Windows or NVivo 12 for Mac or other QDA software they are familiar with. See below the section Software and Hardware Requirements.

Objectives

The course learning objectives are:

  1. To demonstrate the influence that methodology has on the choice of data analysis
  2. To describe the different approaches to code qualitative data
  3. To distinguish the strategies to transform qualitative data into findings
  4. To situate the competing philosophies regarding validity in qualitative research
  5. To present qualitative results evocatively and effectively

Expected outcomes

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

  1. To develop an analytical plan of their study
  2. To choose the right approach to code their data
  3. To identify what strategy to use to transform data into findings
  4. To reflect on their positionality regarding validity in qualitative research
  5. To present qualitative findings convincingly and transparently

Course schedule

Day 1 – Foundations of QDA. The course opens with a lecture on the foundations of qualitative analysis with definitions, historical problems and challenges ahead. The qualitative analysis cycle is introduced as a heuristic device to understand that qualitative analysis, often if not always, occurs iteratively between the phases of coding data, generating meaning, confirming findings and presenting results. We situate the phase of qualitative analysis within the research design and consider the influence that research questions, study purpose and type of data collected have on the choice of data analysis method. Our attention then turns to some popular approaches to QDA; in turn, the aim, sampling requirements and analytical procedures of qualitative content analysis, thematic analysis, cross-case analysis, grounded theory and analytic induction are presented. The second half of the class is a workshop where participants set-up an NVivo project, import and classify their data in preparation for day 2. 

Day 2 – Coding qualitative data. The concepts and approaches for coding qualitative data are introduced on day 2. In qualitative research, coding is defined as the process by which data are segmented in coding units and assigned a code that represents a concept, construct or idea. Coding is a core task in qualitative analysis, so knowing how to code one’s data meaningfully and efficiently is of key importance. The key concepts that shape the coding process - meaning and coding units, codes, codebook and coding schemes - are discussed alongside situations where descriptive, interpretive, and pattern codes can be used to capture distinct levels of abstraction in the data. We then examine the specificities of theme- versus design-based coding schemes and review their respective strengths and requirements. We conclude with the use of visualisations to map the coding process and the emerging relationships between codes. A coding workshop with NVivo follows where participants code their own data and reflect on the process.

Day 3 – Transforming data into findings. Transforming data into findings starts with the question: “I have a mountain of information here. Which bits go together (Miles & Huberman, 1994: 256)”. Coding is a first step in the transformation process as it reduces the amount of data in codes and organises these in thematic families. However, coding alone is no analysis. Qualitative analysis involves that the researcher detects the structure the lies behind the data and explains how the structure connects people, places, processes or practices in one coherent storyline. To do so, a range of strategies for transforming data are proposed and are followed with a discussion on the logic of scientific reasoning: in turn, examples of inductive, deductive, abductive and retroductive analyses are presented. The nuts and bolts of retrieval procedures that operate on data concurrence, overlap, sequence, proximity, precedence and exclusion in NVivo are introduced. We conclude with a discussion on the ladder of abstraction in qualitative analysis.

Day 4 – Presenting qualitative findings. Beyond using quotes to illustrate participant views, in what ways can qualitative findings be presented? What are the best and worse practices when presenting qualitative findings in journal articles and theses? And what to think of the ubiquitous catchy phrase “themes were identified in the data” when themes were part of the interview guide? We explore these questions so participants gain a critical understanding about what visual display(s) can tell the story of their findings effectively and evocatively. To that end, we learn that models are best to illustrate conceptual integration, matrices are good for cross-tabs, tables work for typology, and diagrams suit the depiction of structure. We move on with a discussion and examples on the reporting of analysis, results and interpretation in a qualitative report. The second part of the class is a workshop during which participants assess the quality of the reporting of qualitative analysis in a qualitative study.

Day 5 – Validating qualitative results. Validity of qualitative research is a debated issue amongst qualitative scholars as it brings together divergent perceptions and antagonistic practices depending on whether you espouse empiricism, critical realism, constructionism or subjectivism as your anchored epistemology. We review the current debates on the quality of qualitative research, examining what the conventional scientific paradigm has proposed and what the naturalistic paradigm, and other complementary strategies, have put forward since 1980s. Participants then reflect on their own epistemological positionality, first individually and then in small group discussions. We look at different techniques to confirm the trustworthiness of qualitative results, amongst which we discuss member checking, researcher effects, triangulation and using deviant cases, and comment on their appropriateness depending on the research context and the nature of data collected. The class concludes with the Master class where teams present examples of qualitative analysis of a research project.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

NVivo software is used for the workshop sessions in class. Participants are invited to install NVivo 12 Pro for Windows or NVivo 12 Mac on their laptop or other QDA software they are familiar with for the course. Demonstrations, however, as well as troubleshooting will only be done for NVivo. The NVivo 14-day free trial for Windows or Mac can be downloaded here. It is your responsibility to ensure that NVivo works well on your laptop before the course as no troubleshooting will be provided during or outside teaching hours by the instructor, teaching assistant, ECPR staff, or CEU IT services. Once NVivo is installed on your laptop, verify that it works properly. Follow the instructions below.

  1. Launch NVivo.
  2. On the Start screen (Windows version), in the New section, click Sample Project. On the Welcome to NVivo for Mac screen (Mac version), click Create a copy of the 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 of the page).
Hardware Requirements

Participants to bring their own laptop with software installed. NVivo hardware requirements - as per QSR International

Windows version

 

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 or higher

Operating system

Microsoft Windows 7

Microsoft Windows 7 or later

Hard disk

Approximately 5 GB of available hard-disk space

Approximately 8 GB of available hard-disk space

 

Mac version

 

Minimum

Recommended

Processor

Mac computer with an Intel Core 2 Duo, Core i3, Core i5, Core i7, or Xeon processor

Mac computer with an Intel Core i5, Core i7, or Xeon processor

Memory

2 GB of RAM (as defined by the Mac OS X Mavericks minimum requirements)

4 GB RAM

Display

1280 x 800 screen resolution

1440 x 900 screen resolution or higher

Operating system

Mac OS X 10.9 (Mavericks) or later

Mac OS X 10.9 (Mavericks) or later

Hard disk

Approximately 2 GB of available hard-disk space

Approximately 4 GB SSD of available hard-disk space

Literature

Bazeley, P. (2013). Qualitative Data Analysis: Practical strategies. London: Sage.

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

Boeije, H. R. (2010). Analysis in Qualitative Research. London: 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.

Flick, U. (Ed.). (2014). The Sage Handbook of Qualitative Data Analysis. London: Sage.

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

Grbich, C. (2013). Qualitative Data Analysis: An Introduction (2nd ed.). London: Sage.

Harding, J. (2013). Qualitative Data Analysis: From start to Finish. London: Sage.

Kawulich, B. B. (2004). Data analysis techniques in qualitative research. Journal of Research in Education, 14(1), 96-113.

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.

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

Richards, L. (1998). Closeness to Data: The Changing Goals of Qualitative Data Handling. Qualitative Health Research, 8(3), 319-328.

Ritchie, J., Lewis, J., Nicholls, C. M. N., & Ormston, R. (Eds.). (2014). Qualitative Research Practice: A Guide for Social Science Students and Researchers: Sage.

Ryan, G. W., & Bernard, H. R. (2003). Techniques to Identify Themes. Field Methods, 15(1), 85-109.

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

Saldaña, J. (2009). The Coding Manual for Qualitative Researchers. London: Sage.

Spradley, J. P. (1979). The Ethnographic Interview. Fort Worth: Holt, Rinehart and Winston.

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

Thomas, D. R. (2006). A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation, 27(2), 237-246.

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

Research Designs

Introduction to Interpretive Research Designs 

Expert Interviews for Qualitative Data Generation

Analysing Discourse – Analysing Politics

Introduction to NVivo for Qualitative Data Analysis

Winter School

Introduction to NVivo for Qualitative Data Analysis

Recommended Courses After

Winter School

Advanced 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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