Qualitative Data Analysis

1. Qualitative Data Analysis

2. What is Qualitative Analysis? • It is the non-numerical examination and interpretation of observations. • Theorizing and analysis are tightly interwoven. • The primary activity of analysis is the search for patterns and explanations for those patterns. • The writing process itself is significant for structuring analysis.

3. What are Qualitative Data? • Qualitative data are records of observations or interactions that are complex and contextualised, and that are not easily reduced to numbers • These records are ‘made’ rather than ‘collected’, that is, ‘they are not just lying around, like autumn leaves, ready to be swept into heaps’ (Richards, 2005. p. 37) • Making data through: • Interviews (individuals and in groups) • Observations (field notes, photos, video) • Document analysis • Tools for making data • (Source: Bruce Johnson, “Immersed or Drowning in Data: What’s the Difference?” April 2008)

4. Analytical Thinking in Qualitative Data(Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005) • Standing back form the information given • Examining it in detail from many angles • Checking closely whether each statement follows logically from what went before • Looking for possible flaws in the reasoning, the evidence, or the way that conclusions are drawn • Comparing the same issues from the point of view of other writers • Being able to see and explain why different people arrived at different conclusions • Being able to argue why one set of opinions, results or conclusions is preferable to another • Being on guard for literary or statistical devices that encourage the reader to take questionable statements at face value • Checking for hidden assumptions • Checking for attempts to lure the reader into agreements

5. Creative Thinking in Qualitative Data(Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005) • Be open • Generate Options • Divergence before convergence • Use multiple stimuli • Side track, zig-zag, and circumnavigate • Change patterns • Make Linkages • Trust yourself • Work and Play at it

6. Credibility of Qualitative Data Study • The credibility for qualitative inquiry depends on three distinct but related inquiry elements: • Rigorous techniques and methods for gathering high-quality data that is carefully analysed, with attention to issues of validity, reliability, and triangulation • The credibility of the researcher, which is dependent on training, experience, track record, status, and presentation of self • Philosophical belief in the phenomenological paradigm, that is, a fundamental appreciation of naturalistic inquiry, qualitative methods, inductive analysis and holistic thinking

7. Principles of Analysis Qualitative Data(Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005) • Proceed systematically and rigorously (minimise human error) • Record process, memos, journals, etc. • Focus on responding to research questions • Appropriate level of interpretation appropriate for situation • Time (process of inquiry and analysis are often simultaneous) • Seek to explain or enlighten • Evolutionary/emerging

8. Reducing Qualitative Data Record • Issue is not whether you reduce your data, but when! • … but you want to keep your detailed records for as long as you can handle them • Dilemmas! • Transcribe or summarise interviews? Or a mix? • What to include and exclude • Field notes – how detailed? • Video – compress? Pre-edit? • Storage; security and risk management • Your stories…… • (Source: Bruce Johnson, “Immersed or Drowning in Data: What’s the Difference?” April 2008)

9. Qualitative Data Coding • Descriptive coding – information about the people and their contexts (age, gender, job, location, etc) • Topic coding – what topics are being discussed in this passage? • Analytic coding – what’s going on here? What are the broader themes at work? • (Source: Bruce Johnson, “Immersed or Drowning in Data: What’s the Difference?” April 2008)

10. Recording and Managing Qualitative Data • Before data can be analyzed, they must be recorded and then gathered together into a form that makes analysis possible. • Data can be recorded in text form, by audio- or videotape, photographically, and by memory. • Each recording process has its advantages and disadvantages. • Sometimes sacrifice comprehensiveness and accuracy in favor of recording in a way that is least disruptive of participants.

11. Qualitative Data Analysis Continuum(Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005) Raw Data Descriptive Statements Interpretation

12. Qualitative Analytical Process(1. Analysis Considerations) • Words • Context (tone and inflection) • Internal consistency (opinion shifts during groups) • Frequency and intensity of comments (counting, content analysis) • Specificity • Trends/themes • Iteration (data collection and analysis is an iterative process moving back and forth) • (Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005)

13. Qualitative Analytical Process(2. The Procedures) • Coding/indexing • Categorisation • Abstraction • Comparison • Dimensionalisation • Integration • Iteration • Refutation (subjecting inferences to scrutiny) • Interpretation (grasp of meaning - difficult to describe procedurally)

14. The Qualitative Analytical Process(Adapted from descriptions of Strauss and Corbin, 1990, Spiggle 1994, Miles and Huberman, 1994) Components Procedures Outcomes Data Reductions Description Coding Categorisation Abstraction Comparison Dimensionalisation Integration Interpretation Data Display Conclusions & Verification Explanation/ Interpretation

15. Unstructured Method • Content analysis is the process of identifying, coding and categorising the primary patterns in the data • Constant comparative analysis • reads raw data and identifies an important point • Continues reading and identifies another point • Compares to first point and so on

16. Conducting Content Analysis • Prepare and organise raw data • Source code all raw data • Copy raw data • Store originals of raw data in safe place • Read • Theme coding system • Compare first theme with second theme and so on

17. Conducting Content Analysis (cont.) • Data index and classification • Transfer indicated passages to a file • Open coding • Axial coding • Rules for inclusion • Selective coding • Mapping • Write report

18. A Rich, Messy and Complex Process • Overlap between gathering and analysis • Manifest vs latent content • Decisions are yours • Gathering data, analysing data and writing report are not mutually exclusive

19. Using a Computer Package • Can only assist human judgement • Nvivo

21. Qualitative Data Management Tools • QSR NUD.IST • (Non-numerical unstructured data indexing searching and theorising) • Enables efficient data management by supporting the processes of indexing, searching and hence data theorising • Creates an environment to store and explore data and ideas, it does not determine the research approach. • The major advantage of the package is that it enables an efficient and flexible approach to rigorously and systematically analysing qualitative data. • (Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005)

22. QSR NUD.IST(Source: Mary Brennan, “AEF 801, Research Methods and Project Management,” February 2005) • The QSR NUD.IST software tools are incorporated into two interlocking systems; a document system and an index system • Document Database • Enables text to be stored, edited and retrieved; memos to record ideas can be attached to text; and word and phrase searches can be conducted on the documents • Index Database • Enables the researcher to: code the data; conduct multiple concept or coded category searches thereby providing responses to research questions and theory development; and provides the means to record ideas about the data through memos attached to the various indices