1 / 24

Quantitative and Qualitative Data Analysis: What’s the Difference?

Quantitative and Qualitative Data Analysis: What’s the Difference?. Jim Smith & Christine Maidl Pribbenow 2012 Research Residency. Session Outline. Difference between Quantitative and Qualitative Data Using Mixed Methods Statistics and their Use in Education Research

hayes
Download Presentation

Quantitative and Qualitative Data Analysis: What’s the Difference?

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. Quantitative and Qualitative Data Analysis: What’s the Difference? Jim Smith & Christine Maidl Pribbenow 2012 Research Residency

  2. Session Outline • Difference between Quantitative and Qualitative Data • Using Mixed Methods • Statistics and their Use in Education Research • Analyzing Qualitative Data • Questions??

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

  4. “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 Research in the sciences vs. research in education

  5. Sources of Quantitative Data • Multiple choice or closed questions • GPA, grades • Concept inventories • Rubrics • SAT/ACT/GRE - standardized tests • Anything you can count (stars, population)!

  6. Sources ofQualitative Data • Lab notebooks • Field notes from observations • Open-ended exam or survey questions • Written papers, homework • Journal entries, reflections • On-line discussions, blogs • Email • Texts or notes from interviews or focus groups

  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. Mixed Methods Designs:Taking the Best of Both!

  9. QUANPre-test Data & Results QUANPost-test Data & Results Interpretation qualProcess Intervention QUANData & Results QUALData & Results Interpretation Concurrent Mixed Methods Designs Convergent Parallel Design Embedded Design

  10. qualData & Results QUANData & Results Interpretation Following up quanData & Results QUALData & Results Interpretation Building to QUANIntervention Trial Before-interventionqual After-interventionqual Interpretation Explanatory Design Exploratory Design Sequential Embedded Design 10

  11. Quantitative Dataand Statistics Back to Jim…

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

  13. 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”

  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. 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. • Resources • Atlas.ti and NVivo (qualitative analysis software) References

More Related