Transforming Data into Findings

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 Transforming Data into Findings teaches you to do all that, irrespective of whether you adopt induction, deduction, abduction or retroduction as the logic of scientific reasoning in your study. The seminar opens with a definition of patterns in qualitative analysis and their role in helping researchers move beyond merely “identifying themes” in their data. We then examine the range of patterns researchers may identify in their data, including those based on code cooccurrence, proximity, sequence, and surrounding. The ladder of abstraction in qualitative analysis is introduced, with different qualitative designs associated with distinct levels of abstraction based on their intended outcomes. This ladder of abstraction is particularly helpful for understanding how much work your analysis will require if you plan, let’s say, to identify associations across categories rather than 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 and which 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 on Zoom as well as on-site 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 include 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 software course Introduction to NVivo, Qualitative Data Analysis with NVivo, NVivo Master Course, or Introduction to MAXQDA.

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.