The document outlines key components of data analysis in action research, emphasizing its role in informing educational practices through systematic inquiry. It provides a step-by-step guide for teacher candidates on conducting action research, including identifying problems, collecting data, and analyzing results using various qualitative and quantitative methods. Additionally, it discusses the importance of ethical considerations and best practices to ensure reliable and valid findings.

1. DATA ANALYSIS
IN
ACTION RESEARCH

2. Today’s Goals
Provide the teacher candidate with some background
knowledge on displaying their action research results.
Provide support to teacher candidates on completing their data
analysis section of their action research project.

3. Introduction
•What is Action Research?
•A systematic inquiry conducted by practitioners to improve their
own practice.
•Involves cycles of planning, acting, observing, and reflecting.
•The Role of Data Analysis
•Crucial for understanding, interpreting, and improving practice.
•Provides evidence-based insights for decision-making.

4. The Purpose of Action Research
•Contributes to the theory & knowledge base to enhance
practice
•Supports the professional development of practitioners
•Builds a collegial networking system
•Helps practitioners identify problems & seek solutions
systematically
•Can be used at all levels & in all areas of education

5. The Process of Action Research
•Identify the problem; select an area of focus.
•Review the related research literature.
•Collect the data.
•Organize, analyze & interpret the data.
•Take the action (apply the findings).

6. Overview
Identify the
problem or area
Review related
research literature
Collect data
Organize, analyze
& interpret
Take action;
apply findings

7. Planning

8. Planning Action Research
1. Write an area-of-focus statement.
2. Define the variables.
3. Develop research questions.
4. Describe the intervention or
innovation.
5. Describe the action research
group.
6. Describe the negotiations that
need to happen.
7. Develop a timeline.
8. Develop a statement of
resources.
9. Develop data collection ideas.
10. Put action plan into action.

9. Area-of-Focus Statement
•Identifies the purpose of the study
•Identifies the anticipated outcome
•Identifies the problem to be
addressed
•Completes the statement: “The
purpose of this study is…”

10. Define the Variables
•Write definitions of exactly what you will
address.
•Definitions should accurately represent what
factors, contexts & variables mean to you.
•Be clear about what is being studied, so that
you know it when you see it!

11. The Research Questions
•Develop questions that “breathe life” into the
area-of-focus statement.
•Research questions should be open-ended!
•Research questions help give a focus to the plan.
•They also help validate that you have a workable
plan.

12. Intervention or Innovation
•Describe your proposed solution to the initial
problem.
•This is just a statement about what you will do to
address the teaching and learning issue you have
identified.
•In “formal research” this would be the experimental
treatment.

13. Strategies for Meeting the Criteria
•Talk Little, Listen a lot!
•Begin Writing Early!
•Let Readers “See” for Themselves
•Report Fully
•Be Candid
•Seek Feedback
•Write Accurately
(Wolcott, 1994)

14. Data Analysis
in
Action Research

15. Purpose of the Data Analysis
You are to concisely and accurately display the results
of your study.
In other words, someone who views your data analysis
section should get an idea of the results of your study
at a glance.

16. Types of Data in Action Research
•Qualitative Data
•Interviews, observations, field notes, documents
•Rich, detailed information about experiences and perspectives.
•Quantitative Data
•Surveys, tests, measurements
•Numerical data that can be statistically analyzed.
•Mixed Methods
•Combining both qualitative and quantitative data for a more comprehensive
understanding.

18. Collect the Data
•Using a variety of data collection strategies,
gather information that will contribute to the
findings
•Triangulate
•Data should be analyzed as it is collected

19. Organize, Analyze & Interpret the Data
•As the data is collected, it is also
continually organized & analyzed
•As new perspectives are gained on the
original area of focus, the problem
statement may change
•Interpretation is based on ongoing
analysis & continually reviewing the area
of focus

20. Data Collection Methods
•Interviews
•Structured, semi-structured, or unstructured
•Observations
•Participant observation, non-participant observation
•Surveys
•Questionnaires, polls
•Documents
•Field notes, memos, reports

22. Data Analysis Techniques
•Qualitative Analysis
•Coding: Identifying key themes and categories
•Thematic Analysis: Organizing data around recurring themes
•Narrative Analysis: Examining stories and narratives
•Quantitative Analysis
•Descriptive Statistics: Summarizing data (e.g., mean, median, mode)
•Inferential Statistics: Drawing conclusions about a population from a
sample
•Data Visualization: Using graphs and charts to present findings

25. Data Analysis Tools
•Software Tools
•NVivo, Atlas.ti, MAXQDA (for
qualitative data)
•Excel, SPSS, R (for quantitative
data)
•Manual Methods
•Coding sheets, matrices, mind
maps

26. Data Analysis
through Excel

27. Choosing a Graph
A Line Graph
◦ You want to use a line graph when you wish to show a trend over a period of time.
A Bar Graph
◦ Bar graphs are most often used to compare results between two or more categorical
variables.
A Pie Chart
◦ The pie chart is often over used and is only appropriate when you wish to compare
parts to a whole. Comparing pie charts to one another is typically not a good idea.
Other graphs are available for your consideration but the above are the most
popular and straightforward.

28. Line Graph
A line graph is best used when you wish to display a trend
over a period of time.
For example, if you have been giving a series of assessments
along with implementing a new teaching strategy and you
wish to show that achievement is slowly rising (or mistakes
are decreasing) over time, a line graph would be appropriate.

29. This table shows a series of average test scores
between two groups of students
week 1 week 2 week 3 week 4 week 5
Old Strategy 83 84 85 87 89
New Strategy 82 85 89 92 94

32. Microsoft Excel gives you this chart.
Average Test Scores
76
78
80
82
84
86
88
90
92
94
96
week 1 week 2 week 3 week 4 week 5
Weeks of Treatment
Percentage
Correct
Control
Treatment

33. Bar Graphs
Bar graphs are best used whenever you are comparing
two or more categorical variables. In the following table
the Pre-test and Post-test scores of a group of students
are displayed.

34. Consider how you would display this
data.
Pre-test Post-test
Student 1 75 84
Student 2 88 99
Student 3 90 82
Student 4 63 80
Student 5 85 97
Student 6 79 89
Student 7 94 100
Student 8 83 80
Student 9 88 90
Student 10 68 86

35. This would be a typical bar graph.
Sample Bar Graph
0
10
20
30
40
50
60
70
80
90
100
Students
Percentage
Correct
Pre-test
Post-test

36. Pie chart
In this pie chart,
the purpose is to
show that a large
portion of the
class has an
Individual
Education Plan.
Percentage of Students with IEP's
Students with IEP's
76%
Students without IEP's
24%
Students w ithout IEP's
Students w ith IEP's

37. Sample Pre- and Post-Test Summary for Fifteen Item Test for 10rd
Grade. This slide is a
bar graph from Excel with color added to highlight the data. The data table is also
included below with both the average score and number of students included.
Pre and Post Test Summary of Number Recognition Scores Based on 15 Items
0
5
10
15
20
Average Score 8.7 11.3
Number of Students 18 17
Pre-Test Post-Test

39. Ethical Considerations
•Confidentiality: Protecting participants' privacy
•Informed Consent: Obtaining participants' voluntary
agreement
•Data Security: Ensuring data is stored and accessed
securely

41. Challenges in Data Analysis
•Data Overload: Managing large amounts of data
•Data Quality: Ensuring data is reliable and valid
•Researcher Bias: Avoiding personal biases in
interpretation

42. Best Practices for Data Analysis
•Clear Research Questions: Ensure data collection and analysis align
with research goals
•Systematic Approach: Follow a structured process for data analysis.
•Triangulation: Use multiple data sources to verify findings.
•Member Checking: Validate findings with participants.
•Reflection and Iteration: Continuously reflect on and refine analysis.

43. Conclusion
•Importance of Data Analysis in Action Research
•Provides evidence-based insights for improving practice.
•Supports informed decision-making.
•Contributes to knowledge generation in the field.
•Call to Action: Encourage participants to engage in
rigorous data analysis in their own action research
projects.