Chapter 13 Analyzing Qualitative Data

1. Chapter 13Analyzing Qualitative Data Ashley Webb Paul Sedor Will Vonier Sonia Ortiz

2. Introduction • Qualitative data defined • Examples of Qualitative data • To be useful, data analysis must be conducted • Two types of qualitative data analysis include deductive and inductive • Computer aided qualitative data analysis software (CAQDAS) • CAQDAS includes NVivo, ATLAS.ti, N6, and HyperRESEARCH. • Software for quantitative data is universal, use of CAQDAS for qualitative data is not so widely practiced

3. Qualitative Vs. Quantitative • While ‘number depends on meaning,’ (Dey) it is not always the case that meaning is dependent on number. • ‘The more ambiguous and elastic our concepts, the less possible it is to quantify our data in a meaningful way.’ (Dey) • Qualitative data is associated with concepts and is characterized by its richness and fullness based on your opportunity to explore a subject in as real a manner as is possible.

4. Distinctions Between Quantitative and Qualitative Data

5. Qualitative Research • Data grouped into categories before it can be meaningfully analyzed, otherwise the most that may result may be an impressionistic view of what they mean. • Analyzing qualitative data not an ‘easy option.’ • Data analysis should be consider at time of formulating proposal. • Process of analyzing qualitative data begins at same time as you collect the data and continues afterwards.

6. Preparing for Data Analysis • Chapter 8 – secondary data • Chapter 9 – observations can be video-recorded • Chapter 10 – transcribe recordings and notes to prevent loss of data • Chapter 11 – open questions used to collect qualitative data • Chapter 12 – analyzing quantitative data • Now in Chapter 13, we focus on the conversion of qualitative data to word-processed text since this is the form we will most likely use to analyze our qualitative data.

7. Transcribing Qualitative Data • Non-standardized interviews • interview is normally audio-recorded and transcribed, that is, reproduced as a written account • Interviewer interested in not only what participants said, but the way it was said • Alternative ways of reducing the time needed to transcribe audio-recordings along with the potential problems that could be involved. (pg. 475) • Data cleaning • Ensure factual accuracy

8. Checklist for Transcribing your Interviews • Have you thought about how you intend to analyze your data and made sure that your transcription will facilitate this? • Have you chosen clear interviewer and respondent identifiers and used them consistently? • Have you saved your transcribed data using a separate file for each interview? • Have you checked your transcript for accuracy and where necessary, ‘cleaned up’ the data?

9. Using Electronic Textual Data • Forms of textual data • email interviews • electronic versions of documents, including organizational emails and web-based reports. • Spend time preparing it for analysis. Involves making sure data is: • Suitably anonymized by using separate codes for yourself and different participants • Appropriately stored for analysis, for example one file for each interview, each meeting’s minutes or each organizational policy • Free of typographical errors that you may have introduced and where these occurred have been ‘cleaned up’

10. Overview of Qualitative Analysis • No standardized approach to analysis of qualitative data • Several different strategies to deal with the data collected These strategies are divided into four main categories: • Understanding the characteristics of language • Discovering regularities • Comprehending the meaning of text or action • Reflection • First two categories - analytic strategies (deductive) • Second two categories – no predetermined structures (inductively)

11. Activities Involved in Qualitative Analysis • The general set of procedures listed below elaborate on the aspects of qualitative analysis and involves the following activities: • Categorization • ‘Unitizing’ data • Recognizing relationships and developing the categories you are using to facilitate this • Developing and testing theories to reach conclusions

12. Categorization • Classifying data into meaningful categories • Sources to derive names for these categories: • Utilize terms that emerge from the data • They are based on the actual terms used by the participants • They come from terms used in existing theory and the literature • Categories have an internal and external aspect

13. “Unitizing” Data Attach relevant ‘bits’ or ‘chunks’ of the data, which refers to units of data, to the appropriate category or categories. This can be done using CAQDAS, the manual approach, or by indexing categories. Example:

14. Recognizing Relationships and Developing Categories • Search for key themes and patterns or relationships in the rearranged data • Revise categories and continue to rearrange the data • May subdivide or integrate categories as ways of refining or focusing the analysis • Keep an up-to-date definition of the categories

15. Developing and Testing Hypotheses or Propositions • Development of hypotheses to reveal patterns within the data and recognize relationships between categories • All the relationships need to be tested • Test the hypotheses or propositions that inductively emerge from the data • Development of valid and well-grounded conclusions

16. Interactive Nature of the Process • Allows to recognize important themes, patterns and relationships as you collect data • Allows to adjust the future data collection • This process has implications for the way in which you will need to manage your time and organize your data and related documentation • It will be necessary to arrange interviews or observations with enough space between them

17. Analytical Aids • Summaries, including those for interviews, observations, and documents, and also, interim ones • Self-memos • A researcher’s diary

18. Analytical Aids: Summaries • Summary of the key points that emerge from the written part of the notes • Allows to identify relationships between themes • Useful to make comments about the person(s) interviewed or observed, the setting in which this occurred and anything occurred during the interview

19. Analytical Aids: Memos • Allow to record ideas that occur about any aspect of the research • May vary in length from a few words to one or more pages • Can be written as simple notes • Should be filed together, not with notes or transcripts • May be updated as research progresses

20. Analytical Aids: Researcher’s Diary • Record ideas and the reflections of these • Act as an aide-memorie to the intentions about the directions of the research • Allows the identification of the development of ideas and the way in which the research ideas are developed • Provides an approach that suits the way in which you like to think

21. Approaches to Qualitative Analysis • As mentioned earlier, data collection is either: • Deductive -use existing theory to guide your approach • Inductive - develop a unique theory that is base solely upon your data.

22. Approaches to Qualitative Analysis Using a Theoretical or Descriptive Framework: If you use existing theory to create your research question – you may also use existing info to build your frame work and organize you analysis of data. Disadvantages: • closing the research prior to finding the best conclusion • influenced by the social activities of the participants. Advantages : • Link research to an existing body of knowledge • Help get started • Provide an initial analytical framework

23. Approaches to Qualitative Analysis Exploring without a predetermined theoretical or descriptive framework: Collect data then analyze them to see if there are any patterns or themes that develop. • May be difficult • May not lead to success for inexperienced researchers • There is no clearly defined framework in the beginning • You will need to analyze the data as it is collected • Then develop a conceptual framework as a basis for the rest of your work – also called Grounded approach • This method has proven to be resource intensive as well as requiring a large amount of time. Most research combines elements of both the inductive and deductive approach

24. Deductively-based Analytical Procedures Pattern matching: Predicting outcomes based on theories to show what you are expecting to find as a result of your research. This approach involves: • Developing a conceptual or analytical framework that is based on existing theory • Test the framework to explain your data that is collected • May not lead to success for inexperienced researchers If your predicted outcomes match what is shown through the conceptual framework then this leads to an explanation thus discounting any questions of validity.

25. Deductively-based Analytical Procedures Explanation Building: Build an explanation while collecting data and analyzing rather than testing a predicted explanation. Similar to grounded theory but is designed to test a theory rather than generate a grounded one. And uses the following procedural stages: • Devise a theoretical based hypothesis • Collect data and compare the findings to the theoretical based hypothesis • If necessary amend the hypothesis according to any new findings • Collect more data and compare the findings to the revised hypothesis • Continue the process until you achieve a sufficient explanation

26. Deductively-based Analytical Procedures Impact of a deductive approach on the analysis process: • You will still follow the general process of analyzing qualitative data. • Your hypothesis will still need to be rigorously tested, but using predicted explanations should force the answer to your research question to be more specific. • This will depend upon how thorough you used the existing theory and framework as well and the appropriateness of the hypothesis and conceptual framework that emerges from your data.

27. Inductively-based Analytical Procedures Several inductively based analytical procedures to analyze qualitative data such as: • Data display and analysis • Template analysis • Analytic induction • Grounded theory • Discourse analysis • Narrative analysis Many of these combine elements of inductive and deductive approaches. Reasons to use inductive analysis: • Looking to generate a direction for future work • Research scope is constrained by restrictive theoretical propositions that are not governed by the participants personal views • The theory may point to later actions because it was developed as a result of the setting in which the research was conducted • The theory may be of a nature general enough to be applied in other contexts The inductive approach should not be used as a means to avoid in depth preparation prior to starting your research project.

28. Deductively-based Analytical Procedures Data display and analysis: Based on the book by Miles and Huberman that focused on the process of ‘doing analysis.’ Process contains 3 sub-processes: • Data reduction - clarifying and simplifying the data • Data display – organizing and grouping data into visual displays so that patterns and relationships can be recognized • Drawing and verifying conclusions The exact procedures to follow in these sub-processes are not specified and can be amended according to what is appropriate within the context of your project.

29. Deductively-based Analytical Procedures • Template analysis: • A procedure to analyze qualitative data based on the work of King. A template is a list of themes that are revealed by the data that has been collected. • A template is a list of codes/categories that emerge from the collected data. • Template analysis is less structured and prescriptive than the grounded approach and allows more flexibility when altering to fit the needs of your specific research project. • When altering the codes or adding new codes you should be careful and be aware of its impact on previous coding activity. Also you must document your reasons well.

30. Deductively-based Analytical Procedures • Template analysis: Ways in which a template may be revised: • Insertion of a new code into the hierarchy as the result of a relevant issue being identified through data collection for which there is no existing code • Deletion of a code from the hierarchy if it is no longer relevant • Changing the scope of a code (altering its level within the hierarchy) • Reclassifying a code to a different category • The template may continue to be revised until all of the data has been collected, analyzed and coded to satisfaction. • The template approach can help select points that need to be addressed further during the course of your research as well uncover themes and issues that may not have been apparent from the onset of the project.

31. Analytical Induction • Definition • Less defined explanation of phenomenon • Case Study process • Methods of case studies • In-depth Interviews • Observations • Combination • Criticisms

32. Grounded Theory • “Theory Building” • Stages • Open Coding • Axial Coding • Selective Coding • Implications

33. Discourse Analysis • Analysis of language • Different discourses / norms • Three-dimensional Analytical Framework • Text • Discursive Practice • Social Practice • Disadvantages

34. Narrative Analysis • In-depth Interviews • Like a story • Beginning • Middle • End • Structural Elements • Two usages

35. Quantifying Qualitative Data • Count frequencies of events, reasons, or references • Ignores nature and value of data Using CAQDAS for Analysis • Functions of software • Exploring latest versions

36. Questions