1 / 25

Session Outline

Qualitative Data: Oxymoron, right? Christine Maidl Pribbenow Wisconsin Center for Education Research cmpribbenow@wisc.edu. Session Outline. General discussion about educational research, assumptions, and contrasting educational research with research in the sciences

sahkyo
Download Presentation

Session Outline

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Qualitative Data:Oxymoron, right?Christine Maidl PribbenowWisconsin Center for Education Researchcmpribbenow@wisc.edu

  2. Session Outline • General discussion about educational research, assumptions, and contrasting educational research with research in the sciences • Define common qualitative analysis terms • Code text and discuss

  3. Free Association…

  4. DATA

  5. QUALITATIVE

  6. Definition Qualitative data is information which does not present itself in numerical form and is descriptive, appearing mostly in conversational or narrative form. Words, phrases, text…

  7. Qualitative Data: Oxymoron or inherent tensions? • Hard vs. soft (mushy) • Rigor • Validity and reliability • Objective vs. subjective • Numbers vs. text • What is The Truth?

  8. What are some of the assumptions that you have about educational research?How are they helping or hindering the development of your study?

  9. Research in the sciences vs. research in education • “Hard” knowledge • Produce findings that are replicable • Validated and accepted as definitive (i.e., what we know) • Knowledge builds upon itself– “skyscrapers of knowledge” • Oriented toward the construction and refinement of theory • “Soft” knowledge • Findings based in specific contexts • Difficult to replicate • Cannot make causal claims due to willful human action • Short-term effort of intellectual accumulation– “village huts” • Oriented toward practical application in specific contexts

  10. What are some sources of qualitative data? • Lab notebooks • Open-ended exam questions • Papers • Journal entries • On-line discussions, blogs • Email • Twitter/ ‘tweets’ • Notes from observations • Responses from interviews and focus groups

  11. Qualitative Data Analysis Qualitative analysis is the “interplay between researchers and data.” Researcher and analysis are “inextricably linked.”

  12. Qualitative Data Analysis • Inductive process • Grounded Theory • Unsure of what you’re looking for, what you’ll find • No assumptions • No literature review at the beginning • Constant comparative method • Deductive process • Theory driven • Know the categories or themes using rubric, taxonomy • Looking for confirming and disconfirming evidence • Question and analysis informed by the literature, “theory”

  13. Example Research Questions Why do faculty leave UW-Madison? Do UW-Madison faculty leave due to climate issues?

  14. Definitions: Coding and Themes • Coding process: • Conceptualizing, reducing, elaborating and relating text– i.e., words, phrases, sentences, paragraphs. • Building themes: • Codes are categorized thematically to describe or explain phenomenon.

  15. Let’s Code #1 Read through the reflection paper written by astudent from an Ecology class and highlight words, parts of sentences, and/or whole sentences with some “code” attached and identified to those sections.

  16. What did you highlight? Why?

  17. Let’s Code #2 Read through this reflection paper and code based on this question: What were the student’s assumptions or misconceptions before taking this course?

  18. What did you highlight? Why?

  19. Let’s Code #3 Read through this reflection paper and code based on this question: What did the student learn in the course?

  20. What did you highlight? Why?

  21. Can we say that the students learned something in the course using reflection papers? Why or why not?

  22. Ensuring “validity” and “reliability” in your research • Use mixed methods, multiple sources. • Triangulate your data whenever possible. • Ask others to review your design methodology, observations, data, analysis, and interpretations (e.g., inter-rater reliability). • Rely on your study participants to “member check” your findings. • Note limitations of your study whenever possible.

  23. Questions?

  24. References • Designing and Conducting Mixed Methods Research, Creswell, J.W., and Plano Clark, V.L., 2006, Sage Publications. • Discipline-Based Education Research: A Scientist’s Guide, Slater, S.J., Slater, T.F., and Bailey, J.M., 2010, WH Freeman. • “Educational Researchers: Living with a Lesser Form of Knowledge,” Labaree, D.L., 1998, Educational Researcher, 27(8), 4-12. • Software • Atlas.ti and Nvivo

  25. cmpribbenow@wisc.edu(608) 263-4256

More Related