Quantitative and Qualitative Data

Analysis

Stephanie Gardner & Miriam Segura-Totten

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

What are some of the assumptions that you have about educational research?

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

Keep these questions in mind…

Research in science vs. education

• “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”• Often oriented toward practical

application in specific contexts (classroom research)

• “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

Some assumptions (?)Science Education

Quantitative Data:The What and the How

Stephanie GardnerDepartment of Biology

Purdue University

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

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

Nominal, Ordinal or Interval?

Hill et al., JMBE 15(1):5-12 (2014)

Think- Pair-Share Consider the data type for the MARSI and BAS and evaluate the

summary in the table below

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)

Descriptive or Inferential?

Hill et al., JMBE 15(1):5-12 (2014)

Think- Pair-Share Consider the figure below and evaluate the descriptive and

inferential statistics

1. Collect student demographic data

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

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

Example Instructional Intervention Study

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 (Quantitative)

Nominal or Ordinal(Qualitative)

Frequency, %, Goodness-of-fit,

Relationships

Type of Question

Frequency, %, Contingency table, Test of Association,

Number of Groups

Number of Predictors

Multiple

One

Multiple Regression

Measurement

Ranks

Continuous

Spearman’s rS

Degree of Relationship

Form of Relationship

Primary Interest

Linear Regression

Pearson Correlation

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