Seeking Patterns in Qualitative Data

Key points

  • This seminar proposes innovative strategies to transform qualitative data into findings
  • You will learn to seek patterns in qualitative data, uncover associations, identify relationships and create second-level constructs
  • The seminar can be taken as a standalone unit or combined with another seminar, or any of the method courses or an NVivo course.

Description
How do you move from coding your data to identifying patterns, exploring associations and uncovering relationships across themes and cases? How do you uncover the underlying structure of your data to formulate explanations, generate theoretical predictions, make causal inference,  generate hypotheses or build a middle-range theory? The seminar Seeking Patterns in Qualitative Data teaches you to do all that, irrespective of whether you adopt induction, deduction or abduction as logic of scientific reasoning in your study. The seminar opens with a definition of what patterns are in qualitative analysis and the role they play in helping researchers move beyond merely identifying themes in their data. We then look at the range of patterns researchers may look for in their data between patterns of cooccurrence, proximity and sequence. The ladder of abstraction in qualitative analysis is introduced, whereby different qualitative methodologies are associated to different levels of abstraction based on their intended outcome. This ladder of abstraction is particularly helpful in understanding how much work your analysis will require if you plan to, let’s say, identify associations across themes versus develop a middle-range theory. The second half of the seminar consists of a workshop in which participants apply the seminar techniques and concepts to either their own data or sample data.

Objectives
By the end of the seminar, you will be able to:

  • Define the role that patterns play in transforming raw data into findings
  • Seek patterns of cooccurrence, sequence and proximity in the data
  • Take hard decisions about which patterns to keep from those to discard
  • Describe how induction, deduction and abduction informed pattern seeking
  • Differentiate between the six levels of inferences in qualitative research
  • Decide which level of inference your study aims to
  • Provide a transparent audit trail that ascertains the trustworthiness of your results.

Prerequisites
This is an introductory course on qualitative data analysis. Although no previous knowledge of qualitative data analysis is required, participants should have some familiarity with qualitative research.

Schedule
Half-day from 9:00 to 12:00 or full-day from 9:00 to 16:00.

Location
This course is taught online in Zoom as well as onsite on a request basis.

Teaching methods
Lectures with guided exercises in which participants work on their own data or sample data.

Fee
Please email me for information on fees.

Combining seminars
This seminar is the first of a series of four seminars that also includes Foundations to Qualitative Data Analysis, Coding Qualitative Data and Presenting Qualitative Findings. The seminar can be taken alone or in conjunction with the other related seminars. It can also be taught before, after or simultaneously with the Introduction to NVivo or Qualitative Data Analysis with NVivo courses.

Key readings
Bazeley, P. (2009). Analysing Qualitative Data: More Than Identifying Themes. Malaysian Journal of Qualitative Research, 2(2), 6-22. Retrieved from http://www.researchsupport.com.au/Bazeley_MJQR_2009.pdf.
Bazeley, P. (2013). Qualitative Data Analysis: Practical Strategies. London: Sage.
Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook (2nd ed.). Thousand Oaks: Sage.

This seminar was taught at

As a freelance methodologist, I train social scientists and humanitarian practitioners in qualitative analysis, decolonising research and participatory methodologies. I coach research teams, teach doctoral-level courses in method schools and I consult for humanitarian aid agencies worldwide.

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