Quantitative and Qualitative Data

Analysis– What’s the Difference?

Christine Pribbenow & Steve Nold

Educational research, assumptions, and contrasting with research in the sciences

Quantitative Data Analysis:◦ Types of Data and Statistics

Qualitative Data Analysis:◦ Definitions and Coding

Session Outline

What are some of the assumptions that you have about educational

research?

How are they helping or hindering the development of your study?

“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

Quantitative Data:The What and the

How

Steve NoldDepartment of Biology

UW-Stout

Types of StatisticsDescriptive Inferential

Means

Medians

Modes

Percentages

Variation

Distributions

Draws conclusions

Assigns confidence to conclusions

Allows probability calculations

FIGURE 5. Student performance in (A) midsemester and (B) final

exams across 2010 (n = 265) and 2011 (n = 264) offerings of

MICR2000.

Wang, Schembri and Hall JMBE 14:12-24 (2013)

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, Schembri and Hall JMBE 14:12-24 (2013)

Three Kinds of DataNominal 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

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

Hurney JMBE 13:133-141 (2012)

Boone and Boone Journal of Extension 50:2TOT2 (April 2012)

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

Problem (Theory)

Question (Hypothesis)

Methods (treatment, control groups)

Intervention

Data (Triangulation)

ConclusionsChange practice

Adapted from D.C. Howell, Fundamental Statistics for the Behavioral Sciences (6th ed.) Wadsworth Cengage Learning (2008)

Type of Data

Differences

Two categories

One category

Interval (Quantitati

ve)

Nominal or Ordinal

(Qualitative)

Frequency, %, Goodness-of-fit,

Relationships

Type of Questio

n

Frequency, %, Contingency table, Test of Association,

Number of

Groups

Number of

Predictors Multipl

e

One

Multiple Regressi

on

Measurement

Ranks

Continuous

Spearman’s rS

Degree of Relations

hip

Form of Relations

hip

Primary Interest Linear

Regression

Pearson Correlatio

n

Multiple

TwoRelation Between Groups

Independent

Dependent

Independent

samples t

Mann-Whitney U

Paired Samples t

Wilcoxon

Relation Between Groups

Independent

Dependent

Number of Indep.

Var.

Repeated Measures ANOVA

Friedman

Multiple

One

One-Way ANOVA

Kruskal-Wallis

Factorial ANOVA

1. Collect student demographic data

a) Want to discover if students between treatment and control groups had the similar ethnic backgrounds

2. Collect test grades before and after intervention

a) Want to see if your teaching intervention resulted in a significant difference in test scores between control and treated groups

3. Survey students on their own perceptions of learning

a) 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

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.

Qualitative Data:Oxymoron, right?

Christine Maidl PribbenowWisconsin Center for Education

ResearchUW-Madison

Free Association…

DATA

QUALITATIVE

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…

Definition

Hard vs. soft (mushy) Rigor Validity and reliability Objective vs. subjective Numbers vs. text What is The Truth?

Qualitative Data: Oxymoron or inherent tensions?

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

What are some sources of qualitative data?

Qualitative analysis is the “interplay between researchers and

data.”

Researcher and analysis are “inextricably linked.”

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”

Qualitative Data Analysis

Why do faculty leave UW-Madison?

Do UW-Madison faculty leave due to climate issues?

Example Research Questions

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.

Definitions: Coding and Themes

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.

Let’s Code #1

What did you highlight?Why?

Read through this reflection paper and code based on this question:

What were the student’s assumptions or misconceptions before taking this course?

Let’s Code #2

What did you highlight?Why?

Read through this reflection paper and code based on this question:

What did the student learn in the course?

Let’s Code #3

What did you highlight?Why?

Can we say that the students learned something in the course using reflection

papers?

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.

Ensuring “validity” and “reliability” in your research

Questions?

• 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

References

cmpribbenow@wisc.edu

(608) 263-4256cmpribbenow@wisc.edu