Quantitative and Qualitative Data Analysis Stephanie Gardner & Miriam Segura-Totten.
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Transcript of Quantitative and Qualitative Data Analysis Stephanie Gardner & Miriam Segura-Totten.
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