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Rhode Island College • Feinstein School of Education & Human Development •
Dept. of Educational Studies
FNED 562
Inquiry into Classroom Practice
Week #9: Thinking about Data
What was due for this session?
Literature Review
Continue Fieldnotes
Overview
Your Research Study is underway. You've already made decisions about the population you'll be investigating, your teacher researcher methodology, your pedagogical perspective, your particular research questions, and a proposal that outlines your research. You've begun your intervention, and the data has started to come in.
Now you'll be working through a step-by-step E-Learning Module as a way to start to analyze your data to date. An E-Learning Module has many benefits to to: participants can work at their own pace, choose among curated texts, and make individual decisions that are relevant to their own learning.
E-Learning Module Prep
Create a Google Doc: To get started, please open up a Google Doc and title it, "Data Analysis: E-Learning Module." Please open up permissions so others in our course can view your document. As you enter in items from the E-Learning Module, please number them and title them (i.e. "1) List your Data so Far") for the sake of clarity.
Link your Doc: Please note the link to your Google Doc on this chart.
Step 1) Defining Qualitative Data Analysis (QDA): Before you can engage in data analysis, you need to know what it is. Review the links below.
Then create a 1-2 sentence definition in your own words of QDA, inserting 2 short direct excerpts with correct in-paper APA attribution style.
(Note: Please note what it is, not what it isn't in comparison to quantitative analysis.)
What is Qualitative Data Analysis? Qualitative Data Analysis Techniques Qualitative Data Analysis – What is it About?
Step 2) List your Data so Far: What data might you have accumulated so far?
Insert a table like the one on page 125 of The Power of Questions into your E-Learning Module doc. You'll fill in this table periodically as you do the activities here in this E-Learning Module. Now think about possible qualitative teacher researcher study data: Teacher journaling; Written texts; Student learning artifacts; Classroom discourse; Audio recordings; Video recordings; Interviews; Focus groups; Photos; and/ or Videos.
Make a list of the data you have collected so far in the left column of your table, titled, "Data Source." Be as specific as possible (more specific than it is on page 125, in fact), as it will help you to differentiate among data. (For example, "Student self-reflections after climate change lab," "Lunch conversations with 4 students about reading textbooks," "Student illustrations of the stages of loss.")
Complete and submit Memo #8 to both your personal blog and to Blackboard: Memo #8: Data collection to date.
Begin by naming and defining each data collection tool you have implemented. Write 3-4 sentences that summarize the data you’ve collected from this tool. At this point in your research study, you should have at least five (5) data collection tools assembled. Questions to ask yourself as you describe your data collection to date are as follows:
What types of data have I attempted so far in my educational setting?
What has been the preliminary results of each data tool?
What has worked? What hasn’t worked?
What do I need to do differently or more for future data collection in order to have three claims/ themes and triangulation?
Step 3)
Outlining Possible Themes/ Categories/ Issues in 3 Ways:
Chapter 7 of The Power of Questions is titled, "Making Sense of Your Learnings: Analyzing Data." On page 116, the subheading is "Organizing your evidence into themes or categories." (Note: The subsequent sections in this chapter will also be helpful to you in this E-Learning Module.) This section suggests that, to move from data collection to analysis, you need to begin "organizing your evidence into themes or categories that will help you make sense" of what you have collected so far. Let's examine 3 different ways you can identify possible themes/ categories. As you do so, fill in the top row of your table, which you began in step #2.
Optional: This paper, titled, "Techniques to Identify Themes in Qualitative Data," is a composite of many approaches to thinking about themes in qualitative data.
a) Literature Review: Reread your Literature Review. In your table, note possible themes/ patterns/ issues that seem to be common areas of discussion within your Literature Review.
b) Sociocultural Terminology: Sometimes constructs within social and cultural theory can help to explain phenomenon in qualitative teacher research. In your table, note possible themes/ patterns/ issues that seem to be consistent with these sociocultural terms.
c) Personal: Begin the process of analysis by carefully reading through all your field notes, interview transcripts, and other data to date. Look for key patterns, themes, and issues in the data. A "pattern" refers to a descriptive finding such as ‘Most of the participants reported that they lacked enthusiasm in the curriculum." A "theme" is a broad category or topic such as"‘barriers to engagement." An "issue" is an important topic or problem for debate or discussion. In your table, note possible themes/ patterns/ issues that seem to emerge from your personal reading of your data. Feel free to list as many possible Themes/ Categories/ Issues as you want at this time. You'll likely combine some of these later on.
Step 4) Coding your Data: Now you'll choose the two best data sources you have. You'll know they're the "best" because they have the most possible themes/ categories/ issues contained within them. You'll code these two data sources, one at a time. You can choose to use physical, electronic, or both as a melange, depending on your data items and personal style.
Here are some sources that show what coding looks like and offers you sample methods of the physical process of coding. Review them to learn enough to complete this Step 4.
"Analyzing Qualitative Data" overview.
Step 5) Draw It All Together: Write the next memo, which continues your process of data analysis.
Complete and submit Memo #9 to both your personal blog and to Blackboard: Coding and Themes
Coding is analysis. You review a data set, dissect it meaningfully, and make a point to keep the relations between the parts intact. That’s the stuff of analysis. Defining a code helps you refine your thinking as you progress through your analysis. You should discipline yourself to define each of your codes using academic/ scholarly vocabulary such as a terminology of sociocultural analysis.
You’re looking to create tentative linkages between theoretical concepts that align with your teaching philosophy and the data. You’re taking core concept and categories and identifying them in detail through relationships.
Visualizing relationships helps make sense of the data with respect to the emerging theory. Here are some questions to drive your coding.
- What themes are emerging from your data collection?
- Describe your coding process so far.
- In what ways have you been able to triangulate your findings?
- What further data collection, analysis, and coding do you need to accomplish?
Step 6) Summarizer: Write 1 sentence that summarizes what you've learned specifically about data analysis during this E Learning Module.
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