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Session Outline

Quantitative and Qualitative Data Analysis– What’s the Difference? C hristine Pribbenow & Steve Nold. Session Outline. E ducational research, assumptions, and contrasting with research in the sciences Quantitative Data Analysis: Types of Data and Statistics Q ualitative Data Analysis:

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Session Outline

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  1. Quantitative and Qualitative Data Analysis– What’s the Difference?Christine Pribbenow & Steve Nold

  2. Session Outline • Educational research, assumptions, and contrasting with research in the sciences • Quantitative Data Analysis: • Types of Data and Statistics • Qualitative Data Analysis: • Definitions and Coding

  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. 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

  5. Quantitative Data:The What and the HowSteve NoldDepartment of BiologyUW-Stout

  6. Means Medians Modes Percentages Variation Distributions Draws conclusions Assigns confidence to conclusions Allows probability calculations Types of Statistics Descriptive Inferential

  7. Wang, Schembriand Hall JMBE 14:12-24 (2013) FIGURE 5. Student performance in (A) midsemester and (B) final exams across 2010 (n = 265) and 2011 (n = 264) offerings of MICR2000.

  8. FIGURE 6. Student Evaluation of Course and Teaching (SECaT) scores across 2010 and 2011 offerings of MICR2000. Students were invited to voluntarily respond to surveys regarding their evaluation of teaching within MICR2000 in 2010 (n = 108) and 2011 (n = 87) using a standardized University-Wide Student Evaluation of Course and Teaching (SECaT) survey instrument. Student responses corresponded to a 5 -point Likert scale and quantified as follows: 1 = Strongly Disagree; 2 = Disagree; 3 = Neutral; 4 = Agree; 5 = Strongly Agree. Bars represent mean +/– standard error of the mean (SEM). *Denotes a statistically significant difference between student responses for 2010 and 2011 offerings of MICR2000, as determined by the Mann-Whitney U test (p < 0.05). Wang, Schembriand Hall JMBE 14:12-24 (2013)

  9. Three Kinds of Data Nominal Ordinal Interval Categorical No mean ● Education level ● Gender Sounds like “NAME” Natural ordering Unequal intervals ● Rankings ● Survey data Sounds like “ORDER” Extends ordinal data Equal intervals ● Temperature ● Time Sounds like what it is

  10. Borgon et al., JMBE 13:35-46 (2013)

  11. HurneyJMBE 13:133-141 (2012) Boone and Boone Journal of Extension 50:2TOT2 (April 2012)

  12. Darland and Carmichael JMBE 13:125-132 (2012)

  13. Problem (Theory) Question (Hypothesis) Methods (treatment, control groups) Intervention Data (Triangulation) Conclusions Change practice

  14. Frequency, %, Goodness-of-fit, One category Nominal or Ordinal (Qualitative) Frequency, %, Contingency table, Test of Association, Two categories Degree of Relationship Pearson Correlation Primary Interest Continuous Form of Relationship Linear Regression One Measurement Type of Data Spearman’s rS Ranks Number of Predictors Relationships Multiple Regression Multiple Independent samples t Independent Interval (Quantitative) Type of Question Mann-Whitney U Relation Between Groups Two Paired Samples t Dependent Wilcoxon One-Way ANOVA Number of Groups Differences One Kruskal-Wallis Number of Indep. Var. Independent Multiple Relation Between Groups Factorial ANOVA Multiple Repeated Measures ANOVA Dependent Adapted from D.C. Howell, Fundamental Statistics for the Behavioral Sciences (6th ed.) Wadsworth Cengage Learning (2008) Friedman

  15. Collect student demographic data • Want to discover if students between treatment and control groups had the similar ethnic backgrounds • Collect test grades before and after intervention • Want to see if your teaching intervention resulted in a significant difference in test scores between control and treated groups • Survey students on their own perceptions of learning • Want to see if your teaching intervention resulted in a significant increase among responses to Likert-scale questions regarding student learning gains between control and treated groups

  16. Graduate school level: You have categorized your students into three performance groups; novice, developing, and expert based on high school GPA and SAT data. You want to compare the performance of these groups on a critical thinking assessment before and after your teaching intervention.

  17. Qualitative Data:Oxymoron, right?Christine Maidl PribbenowWisconsin Center for Education ResearchUW-Madison

  18. Free Association…

  19. DATA

  20. QUALITATIVE

  21. 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…

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

  23. 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

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

  25. 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”

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

  27. 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.

  28. 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.

  29. What did you highlight? Why?

  30. 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?

  31. What did you highlight? Why?

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

  33. What did you highlight? Why?

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

  35. 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.

  36. Questions?

  37. 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

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

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