Qualitative Data analysis 1

1. EDUC 894 Week 8 Qualitative Data analysis 1

2. Plan for Today • Conference Debrief • What’s Hot in Analysis Methods: Engaging Complexity • Group-work & Team Consultations • Midterm Course Feedback Sheets --------------------Dinner Break------------------- • Group-work & Team Consultations cont. • Qualitative Data Analysis (7 pm) • Overview of Approaches • Class Activity: Analyzing Data • Moving Forward • Deliverable 3 Handout & Due Date Discussion • Reading: Auerbach only (online book, pick up handout)

3. Conference Debrief

4. Groupwork & Team Consultations

5. Analyzing Qualitative Data

6. Qualitative Data Can Be: • Interviews • Observation notes • Images • created by researchers or subjects • Videos • Student work But one thing is always true: Qualitative is voluminous (that’s a nice way of saying that it is easy to get overwhelmed)

7. The Overriding Goal(s) • Make inferences from the data to address your research questions • Connect the empirical (specific data) with the theoretical (research inquiry) • Most importantly it is a process of making meaning of the data • This often involves looking for patterns • This can occur simultaneously or in iteration with data collection • And what does the data mean to whom…?

8. Qualitative Data Analysis (QDA) Approaches: 2 Schools • Back to the miner and the traveler • What are they looking for & how will they go about it? • Think about how their interview styles have set them up for the data analysis stage…

9. QDA: The Miner Approach • “Testing Theory” (Glaser) • Does the data support our hypotheses? • Does the data fit a pre-established framework? • Closed Coding / Rating • Interpretive • Automatic Image Source: http://www.synthstuff.com/mt/archives/bertrand-miner.jpg

10. QDA: The Miner Approach II • Qualities of a Good Analysis • Objective & Reliable • Multiple raters • Inter-rater Reliability • Number of decision points • Tolerance & Reconciliation • Multiple measures • Rubrics and consistency • Valid • How do the indicators used serve as evidence of the ideas you are looking for? • Examines all data in the sample Image Source: http://www.synthstuff.com/mt/archives/bertrand-miner.jpg

11. QDA: The Miner Approach III • Is this still really “qualitative” data analysis? Image Source: http://www.synthstuff.com/mt/archives/bertrand-miner.jpg

12. QDA:The Traveler Approach • “Generating Theory” (Glaser) • What is the data trying to tell us? • What are important themes related to the topic / phenomenon / RQs? • What structure emerges from the data? • Coding processes • Range in structure and imposition of meaning • Creswell presents a generic procedure Image Source: http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg

13. QDA:The Traveler Approach II • Coding process decisions • By question or overall • This highly relates to the kind of interview you conducted • Target categories or completely open • Number of coders; how and when to interact • “Constant Comparison” • Item to item • Item to Category • Category to Category Image Source: http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg

14. QDA:The Traveler Approach III • So when are you done? • Unlike the Miner – you aren't’ focused on “leaving no stone unturned,” but you will likely go over all the data several times. • You will constantly be revising, combining and rethinking your codes - when no new themes are emerging, you may be nearing “theoretical saturation” • Or you may need to go collect more data…. Image Source: http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg

15. Theorectical Saturation Data Collection Data Analysis Close to Saturated Categories More Refined Categories Third Interview Toward Saturation of Categories Refined Categories Second Interview First Interview Preliminary Categories

16. QDA: The Traveler Approach IV • Qualities of a Good Analysis • Confirmability (Results are Investigator-free) • Triangulation • Search for Negative Evidence • Dependability (Results are Stable) • Overlapping Methods • Cycles of Analysis and Data Collection • Credibility (Results are Plausible) • Detailed analysis process • Coherence of results • Discussion of alternative explanations • Member Checks Image Source: http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg

17. QDA: Some Final Thoughts • There is great variation in how QDA is done • Your approach should be specific to your study (your RQ, your data and how it was collected) • Since each instance is unique, it is very important to document your process • The two approaches described here (generating theory and testing theory) can be combined (Glaser’s top left quadrant) • You’ll read about another way of doing this in Auerbach • Next week we’ll look at one technology to help manage masses of qualitative data (Atlas-ti) • Class next week will be in Room 3100

18. Class Activity: Analyzing Data

19. Analyzing Data Activity • Objectivist • RQ: Are X,Y & Z important issues about vacations for graduate students? Constructionist • RQ: How do graduate students think about vacations? Data source = X letters written by grad students about their vacations. Working in pairs, pick a stance and RQ and think about how you will analyze the data. In particular make sure to consider how the two of you will coordinate your efforts and how you will be able to justify either the validity & reliability (objectivist) or trustworthiness (constructionist) of your analysis.