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?

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 (?) ScienceEducation

Quantitative Data: The What and the How Stephanie Gardner Department 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 Statistics Descriptive 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)

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 (6 th ed.) Wadsworth Cengage Learning (2008) Type of Data Differences Two categories One category Interval (Quantitative) Nominal or Ordinal (Qualitative) Relationships Type of Question Number of Groups Number of Predictors Multiple One Multiple Regression Measurement Ranks Continuous Spearman’s r S Degree of Relationship Form of Relationship Primary Interest Linear Regression Pearson Correlation Multiple Two Relation 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