1. Analyzing data Chapter 6
Action Research: Improving Schools and Empowering Educators (4/e)
Craig A. Mertler SAGE Publications, 2014

2. Qualitative Data Analysis Techniques
Qualitative data are analyzed inductively
Specific observations  look for patterns  develop hypotheses  develop general conclusions
Potentially overwhelming task
Goal is to reduce volume of information collected
Risk minimizing, simplifying, distorting data
Must rely on a coding scheme—system for grouping data into categories of similar information
Highly individualized type of system

3. Qualitative Data Analysis Techniques
Often necessitates reading, rereading, rereading again your data
Must get to “know” your qualitative data very well
Steps in the process:
Reduce amount of narrative data through use of coding scheme
Describe main characteristics of categories (connect data to research questions)
Interpret what has been simplified and organized

4. Qualitative Data Analysis Techniques
Also, engage in introspection
Reflective practice that helps to ensure that you remain objective and “emotionally unattached” to data
Assistance with analysis through software
Analysis of qualitative data cannot be “done” on the computer (due to inductive nature)
Software can help store and organize data

5. Quantitative Data Analysis Techniques
Quantitative data are analyzed deductively
Identify topic  focus with research questions or hypotheses  collect and analyze data  develop conclusions
Can use either descriptive or inferential statistics
Descriptive statistics—procedures that simplify, summarize, and organize numerical data
Inferential statistics—procedures used to determine how likely given statistical results are for an entire population based on a sample

6. Quantitative Data Analysis Techniques
Descriptive statistics
Measures of central tendency—single value to indicate what is typical or standard about a group of individuals
Mean
Median
Mode
Measure of dispersion—single value to indicate how scores are different, or what is atypical
Range
Standard deviation

7. Quantitative Data Analysis Techniques
Descriptive statistics (cont’d.)
Measures of relationship—statistical measure of strength of association between variables
Correlation coefficients

8. Quantitative Data Analysis Techniques
Descriptive statistics (cont’d.)
Visual displays of data—not really a statistical procedure; simply ways to “show” data
Frequency distribution table
Histograms
Bar charts
Pie charts

9. Quantitative Data Analysis Techniques
Inferential statistics
Determination of how likely a given statistical result is for an entire population, based on a sample of that population
Pre-set alpha () level—how much of the time would the results be due only to chance (typically equal to .05)
Compare to probability level (p-value)—results from the analysis
Rules for interpretation:
If p < , the difference is statistically significant; decision is “reject the null hypothesis”
If p > , the difference is not statistically significant; decision is “fail to reject the null hypothesis”

10. Quantitative Data Analysis Techniques
Inferential statistics (cont’d.)
Common types of inferential analyses:
Independent-measures t-test
Repeated-measures t-test
Analysis of variance (ANOVA)
Chi-square test
Statistical significance versus practical significance
Groups
Measure
Final Exam Score
Ms. Sizemore's
Class
Mr. James'
Measures
Group
Mrs. Love's
Class
Unit Pretest
Unit Posttest
Groups
Measure
Statewide Achievement Test Scores
Westside School
Central School
Eastside School

11. Quantitative Data Analysis Techniques
“Analyzing” standardized test data
Norm-referenced scores—student performance is compared to performance of other, similar students
Criterion-referenced scores—student performance is reported in terms of number of questions attempted, number answered correctly, etc.
Numerous types of scores exist, including:
Standard scores
Grade equivalent scores
National percentile ranks
Normal curve equivalent scores
National stanine scores

12. Quantitative Data Analysis Techniques
Statistical software
Numerous software packages exist; some are very costly
Very effective, Web-based alternative: StatCrunch (

13. Mixed-methods Data Analysis Techniques
Explanatory mixed-methods:
Quantitative data analyzed first, followed by qualitative
Interpretation of qualitative results should focus on extension, elaboration of quantitative results
Exploratory mixed-methods:
Qualitative data analyzed first, followed by quantitative
Interpretation of qualitative results should should lead to or inform collection and analysis of quantitative data
Triangulation mixed-methods:
Quantitative data and qualitative data are analyzed simultaneously

14. Reporting Results of Analyses
Some general rules of thumb exist
Reporting results of qualitative data analyses
Must convert massive amounts of narrative data into something easily digested by readers
Try to be impartial
Include references to yourself, where warranted
Take readers along “on your journey”
Include representative samples to enhance your presentation
Place interesting, but nonessential, information in appendices

15. Reporting Results of Analyses
Reporting results of quantitative data analyses
General guidelines:
Suggestions for expressing data as numerals (APA Manual)
Suggestions for expressing data using words (APA Manual)
Report numerical data in descending order
Report total numbers before reporting numbers in categories
Use tables to organize large amounts of numerical data
Use figures to present results visually

16. Data analysis template
Planning for Data Analysis

17. Action research checklist 6
Action Research Checklist 6: Analyzing Data in Action Research ☐ Revisit your research question(s) and your previous decisions about the use of qualitative, quantitative, or mixed-methods data for your action research. ☐ Develop a plan for analyzing your data (see below). ☐ If you have collected qualitative data, decide how you plan to analyze your data: ☐ Will you code, organize, and analyze your data by hand? ☐ How will you actually do this (notecards, sticky notes, etc.)? ☐ Will you use some sort of software (see the “Related Websites” section of this chapter) to code, organize, and analyze your data? ☐ If you have collected quantitative data, decide how you plan to analyze your data: ☐ Be sure to specify the type of analysis—descriptive (e.g., frequencies, mean, median, graphs, etc.) or inferential statistics (e.g., t-test, ANOVA, chi-square test, etc.)—you plan to use. ☐ Will you analyze your data by hand, perhaps using only a calculator? ☐ Will you use some sort of software (e.g., StatCrunch, or others in the “Related Websites” section of this chapter) to analyze your data? ☐ Anticipate how you will present the results of your data analysis: ☐ Will you present all of your results in narrative form? ☐ Will you utilize any tables, graphs, etc.? ☐ Develop a timeline for your data analyses.