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SD101A - Qualitative Data Analysis: Concepts and Approaches (W1)

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 1 to Friday 5 August 2016
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

Basic knowledge of qualitative research required. No requirement of qualitative analysis.

NVivo 11 Pro for Windows will be used by the instructor to demo the application of the QDA process in a qualitative software environment. If you want to do the demo in NVivo, you must possess basic knowledge of the software as NVivo will not be taught in class. The refresher course Introduction to NVivo for Qualitative Data Analysis offers a comprehensive entry to NVivo for those who wish to use it. Participants may also use other qualitative software they’re familiar with, including Word or Excel, for the demo sessions.

Short Outline

Are you planning to conduct interviews or focus groups for your data collection, or perhaps do participant observation during key 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 and field notes you collected and analyse them. Will you know how?

 

The aim of this course is to provide participants with strategic understanding and applied skills in planning, conducting, and reporting the process of qualitative data analysis (QDA) in a research project. At the end of the course, participants will be able to plan, conduct, and critically assess the quality of QDA in their own and other people’s research. Key topics covered are: common problems and prejudices associated with QDA, role of QDA in the research design, process and strategies in coding qualitative data, techniques to seek patterns and identify relationships, and best practices in displaying and reporting the outcome of QDA. The course alternates between lectures, guided exercises, and workshops with NVivo. Participants conducting qualitative research as part of their PhD / postdoc will particularly benefit from the course.

Long Course Outline

Definition of qualitative analysis

Qualitative analysis is the search for patterns in textual, visual, or artefact data and the explanation for why these patterns exist in the first place. While statistics rely on the use of probability theory to estimate population inferences, qualitative analysis uses theory to decontextualise the raw data in segments and recontextualise them in categories to generate concepts, identify relationships, and formulate hypotheses for theory development. Qualitative analysis may be conducted inductively by identifying conceptual categories directly in the data, or deductively by applying predefined theoretical notions onto the material. A mixed approach to analysis - when both induction and deduction are used in different points in time in the analytical process – is increasingly popular since it makes use of the researcher’s theoretical sensitivity and allows space for meaning making and category generation. 

 

Criticisms about qualitative analysis

Historically qualitative data analysis has been criticised for being opaque and subjective given that it is sometimes difficult to see how researchers went from hundreds of interviews 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 know what data 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. The real analytical tasks researchers should be concerned about – that is, examining commonalities and differences across units of analysis, discovering what patterns exist amongst these and synthesizing those in a storyline, model, or schema - is most frequently than not absent in qualitative studies. Conversely, the widespread myths that the ‘method will emerge from the data’ or that researchers’ tacit knowledge will lead them to ‘make sense’ of the data, account for generally poor analytical practices and opaque reporting.

 

Contribution of this course

This course introduces some of the key concepts and stragegies to plan, conduct and report QDA in a transparent, traceable, and auditable manner. You will learn the best fit between the research questions you ask, your study purpose, the data you intend to collect, and the approach to analyse your qualitative data. Whether you intend to conduct interviews or focus groups, collect policy papers or press articles, use Internet data, or conduct open-ended survey questions and wonder how you should analyse your data, this course is for you.

 

Objectives

The course’s learning objectives are to:

  1. Review the problems and prejudices associated with QDA
  2. Illustrate the relationship between research design and QDA
  3. Learn different approaches to code qualitative data
  4. Seek patterns across themes, cases and contexts
  5. Generate graphic displays to present findings

 

Expected outcomes

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

  1. Recognise the ‘black box’ problem in many qualitative studies
  2. Design a QDA plan congruent with one’s research design
  3. Choose the right strategy to code qualitative data
  4. Apply the right technique to seek patterns across the data
  5. Communicate qualitative findings effectively

 

Course schedule

Day 1 – Foundations. The course opens with a lecture on the foundations of qualitative data analysis: definitions, history, problems and challenges ahead. The qualitative analysis cycle is introduced as a device to understand that qualitative analysis often, if not always, occurs iteratively between the phases of data coding, patterns seeking, and results display. We then look at the function that data analysis plays in a qualitative study by connecting the analysis phase with the rest of the research design: that is, with a well-formulated research question, a clearly-defined research purpose, a congruent data collection, and an effective presentation of findings. Our attention then turns to a number of approaches to analyse qualitative data. In turn, the aim, specificities, and requirements of thematic analysis, qualitative content analysis, grounded theory, cross-case analysis, and analytic induction are presented. The second half of the class is a workshop where students develop the analytical plan of their study.

 

Day 2 – Data coding. Concepts and practice associated with coding qualitative data are introduced on day 2. In qualitative research, coding is the process by which data are segmented in coding units and assigned a code. A code is a word, or short phrase, that captures what the data is about. A code can mean to a concept, a construct, an event, a place, or an organisation. Coding is the first phase of qualitative analysis, so knowing how to code one’s data and the outcome coding should yield is of central importance. Central notions to coding such as meaning unit, coding unit, codes and codebook, coding scheme, and coding outcomes are discussed. Students then have the opportunity to work on the development of their coding scheme and the coding protocol of their study, first using pen and paper and then in NVivo. The second half of the class is a workshop where students code interview data (or their own if they have any). NVivo’s coding functions are taught alongside features that enhance the development of an audit trail for transparency and auditability.

 

Day 3 – Patterns seeking. Day 3 addresses inductive and deductive approaches to seeking patterns across the data and to create second and third level constructs as the analysis reaches its end and formal conclusions are formulated. Depending on the study design, the tasks of seeking patterns and that of identifying relationships may take place while coding inductively or, by means of retrieval techniques where coding co-occurrence in cross-tabulated tables indicates an association between codes which should be investigated further for possible emerging relationships. The use of other patterns seeking techniques such as frequency counting, code clustering, code subsuming and code aggregation will be demonstrated in the light of the instructor own and other people’s research.  The second half of the class is a workshop where participants try the different techniques of seeking patterns using their own or sample data, so that relationships between codes may be uncovered, comparisons built, meaning generated, and interpretation supported.

 

Day 4 – Reporting findings. Day 4 covers best practices when reporting qualitative analysis and communicating research findings. In qualitative inquiry, reporting findings involves the presentation of data into a coherent structure, often supported with a narrative, so that a clear, unambiguous message is conveyed to the target audience. The types of analytical outputs in qualitative research - descriptive vs explanatory accounts – are introduced. Our attention then turns to the purpose of graphic displays for qualitative data: we see that models are best to illustrate conceptual integration, matrices are appropriate for cross-tabulated information, tables are well-suited to present typology, and diagrams work well to depict structure. We conclude the first half of the class by looking at the quality criteria when reporting qualitative research generally, and those concerned with communicating qualitative results in particular. The second half of the class is a workshop on the visualization displays available in NVivo and their application in students’ research context.

 

Day 5 - Master Class. The Master Class consists of participant presentations about how they intend to analyse their data in their current research. A short powerpoint must highlight the congruence between their research questions, the study purpose, data collection method, strategies for coding and analysis, and reporting strategy. Those at the start of their research are equally welcomed to present what they prospectively intend to do. Presentations are voluntary, are limited to 20 minutes, and do not form part of the formal evaluation system of the Summer School.

 

Assignment for 2 ECTS credits

Students can earn 2 ECTS credits upon the production and satisfactory marking of an academic essay. Deadline for the essay is August 12th 2016.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

NVivo 11 Pro for Windows is used for the hands-on sessions in the course. Participants are invited to use NVivo or other qualitative software they’re familiar with during the hands-on sessions. However demonstrations will solely be done with NVivo 11 Pro for Windows. The 14-day free trial of NVivo 11 Pro can be downloade here. Alternatively, you can run NVivo 10 for Windows on your laptop. 

 

DO NOT COME TO THE COURSE WITH NVIVO FOR MAC. NVivo for Mac is incomplete compared to NVivo for Windows and has a different interface. Participants with Mac laptops should consult the compatibility options here to run NVivo 11 Pro using Boot camp, Parallels, or VMware Fusion. It is your responsibility to ensure that NVivo 11 works well on your laptop as no troubleshooting will be provided during or outside teaching hours.


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

 

  1. On your Desktop, launch NVivo by clicking on the NVivo 11 shortcut icon.

  1. On the Start screen, in the New section, click Sample Project.


 

  1. NVivo opens a copy of the sample project which is stored in your default project location.

  2. 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).

Hardware Requirements

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

 

 

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 (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)

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.

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.

Westbrook, L. (1994). Qualitative Research Methods: A Review of Major Stages, Data Analysis Techniques, and Quality Controls. Library & Information Science Research, 16(3), 241-254.

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

Research Designs

Introduction to Interpretive Research Designs 

Expert Interviews for Qualitative Data Generation

Introduction to NVivo for Qualitative Data Analysis

Recommended Courses After

Advanced Qualitative Data Analysis

Analysing Discourse I – Analysing Politics:

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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