1. Analyzing Research Data and Presenting Findings
Introduction to Educational Research (5th ed.) Craig A. Mertler & C.M. Charles
Chapter 8
Analyzing Research Data and Presenting Findings

2. Qualitative versus Quantitative Data
Qualitative data
Analyzed inductively—thought process using logic to make sense of observations
Goal is to uncover patterns, themes, categories, etc.
Quantitative data
Analyzed mathematically
Goal is to describe data, show degree of relationship or difference, & determine likelihood that sample findings also exist in population

3. Analyzing Ethnographic Data
Differs from the generic description of qualitative analysis
Research questions often emerge after data collection (and even analysis) have begun; they are not foreseen
Conclusions are drawn from a broad view of human behavior
Analytical process is somewhat iterative and circular
Identify topics (through data collection)
Cluster topics into categories
Form categories into patterns
Draw explanations (conclusions) from patterns
This on-going analytical process is known as logico-inductive or hypothetico-inductive analysis
A very subjective process—bias is a strong possibility

4. Analyzing Quantitative Data
Key is the separation of the researcher from the participants and the data—objectivity!
Parameters—numerical indices that describe populations
Statistics—numerical indices that describe samples
parameters
populations
statistics
samples

5. What Statistics Are Used For
To summarize data in order to reveal what is typical and atypical about a group
To show relative standing of individuals in a group
To show relationships among variables
To show similarities and differences among groups
To identify error inherent in sample selection
To test for significance of findings
To make inferences about a population

6. Descriptive Statistics
Numerical indices and procedures that summarize and simplify data about samples
Measures of central tendency:
Mean (M)
Median (Mdn or Md)
Mode (Mo)
Measures of variability:
Range (R)
Variance (s2)
Standard deviation (s or SD)

7. Descriptive Statistics (cont’d.)
Measures of relative position:
Percentile rank (%ile or PR)
Stanine
Converted scores (z-score, T-score, grade-equivalent, etc.)
Measures of relationship:
Pearson correlation coefficient (r)
Many other correlation coefficients

8. Inferential Statistics
Numerical indices and procedures used to make inferences about a population based on information from a sample
Error estimates
Confidence intervals
Tests of significance
Significance of correlation
Significance of differences between means (t-tests, F-tests, etc.)

9. Cautions in Using Statistics
Don’t try to do too much...
Stay within your skills and knowledge of statistics!
Your research questions and/or hypotheses should guide your analysis
Statistical significance vs. practical significance

10. Presenting Your Findings
Research findings are the information that results from your analysis
Findings from qualitative data analysis are usually presented as summary statements and discussion about the patterns which were observed
Findings from quantitative data analysis are typically presented in narrative form as well as visually (in tables or graphs)

11. Applying Technology…
Web sites covering topics related to data analysis
Dr. Trochim’s discussion of "Data Preparation" (
Dr. Trochim’s discussion of "Descriptive Statistics" (
Dr. Trochim’s discussion of "Correlation" (
Dr. David W. Stockburger’s online statistics textbook (
Dr. David W. Stockburger’s bibliography and statistics web resources (
WebStat online statistical analysis program (
Dr. Trochim’s interactive page for "Selecting Statistics" (