EAD 800-Qualitative Research Data Analysis

1. EAD 800-Qualitative ResearchData Analysis Dr. Pamela L. Eddy

2. Data Management (Huberman &Miles, 2001) Raw Material (Tapes, notes, documents) Partially processed data (e.g., Transcriptions) Coded Data & Coding Scheme Researcher Memos/Reflections Search & Retrieval Records (your path) Data Displays (charts, jottings, concept maps)

3. Data Management (Huberman &Miles, 2001) Analysis Episodes—step by step guide Report text—drafts Chronological log/documentation of data collection Index of all the above material

4. Recording and Managing Data Methods for keeping track Color Coding Computer Files File Boxes Transcriptions

5. Analysis—Research Design Influence of research design on analysis Influence of researcher orientation on analysis Inductive—”loose” design vs. Deductive—”tight” design

6. Analysis Strategies General Review—jotting notes Development of codes/categories “Counts” of frequency of codes

7. Phases of Analysis—(Marshall & Rossman, 1999) Organizing the data Generating categories, themes Coding the data Testing of emergent understandings Searching for alternative explanations

8. Within-Case Analysis Description and Explanation—tell the story Provide data display—see the concepts Role of Theory—guide data collection/boundary analysis Causality—local vs. global impacted by other variables

9. Cross-case Analysis Tension of reconciling the particular and the universal Yin (1984)—replication strategy—discover in one case apply to next Themes across cases—displays

10. Biography Identify Stories Epiphanies Theorize toward developing patterns and meanings

11. Phenomenology Tell the story of “What happened?” Describe “how” it was experienced. The “essence.”

12. Grounded Theory Open coding/Axial coding Selective coding—development of stories Develop conditional matrix

13. Ethnography Look for themes—patterns Interpret to make sense of these findings

14. Case Studies Look for themes—patterns Use direct interpretation Develop naturalistic generalizations

15. Analysis Spiral Iterative Process—Describing, classifying, and interpreting loop Uncover larger meanings Creation of comparison table or matrix

16. Typological Analysis Establish typologies Divide data according to typology Look for patterns Decide if patterns are supported by the data Look for relationships among the patterns Write patterns in one-sentence generalizations Select data excerpts that support your generalizations

17. Inductive Analysis Start from selective work out to general—frames of analysis Create domains—ignore others Determine if domains are supported by data Analyze within domains Themes across domains Master outline of relationships

18. Interpretive Analysis Check research journal for impressions Read data and record impressions Reread data—note where impressions are supported or challenged Write summary Review with participants

19. Political Analysis Write a reflection explicating your ideological positions Mark data where above is supported Write up generalization of this data Reread entire data set—are your points supported? Negotiate meanings with participatns

20. Polyvocal Analysis Identify all the voices in data—your own too! Mark data where certain voices are heard Decide which voices to include Take stories to participants—if possible Write revised stories

21. Verification/Validity Biases/Shortcomings Data overload Salience of first impressions Selectivity—overconfidence in some data Co-occurrences taken as correlations Unreliability of information from some Over accommodation of information

22. Checks and Balances Triangulation—multiple sources Auditing—systematic review Are findings grounded in the data? Are inferences logical? Is the category structure appropriate Can inquiry decisions/method shifts be justified? What is the degree of researcher bias? What strategies are used for increasing credibility?

23. Remember…. Analysis starts with data collection Your framework guides your analytic approach (ontology) Changes may happen—that’s the point!