General discussion about educational research, assumptions, and contrasting educational research with research in the sciences Define common qualitative analysis terms Code text and discuss
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…
Hard vs. soft (mushy) Rigor Validity and reliability Objective vs. subjective Numbers vs. text What is The Truth?
“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 “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
Lab notebooks Open-ended exam questions Papers Journal entries On-line discussions, blogs Twitter/ ‘tweets’ Notes from observations Responses from interviews and focus groups
Qualitative analysis is the “interplay between researchers and data.” Researcher and analysis are “inextricably linked. ”
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”
Why do faculty leave UW-Madison? Do UW-Madison faculty leave due to climate issues?
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.
Read through the reflection paper written by a student from an Ecology class and highlight words, parts of sentences, and/or whole sentences with some “code” attached and identified to those sections.
Why?
Read through this reflection paper and code based on this question: What were the student’s assumptions or misconceptions before taking this course?
Why?
Read through this reflection paper and code based on this question: What did the student learn in the course?
Why?
Why or why not?
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.
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), Software Atlas.ti and Nvivo