Analyzing data
Once you have collected data, you must somehow transform it into evidence that you can use to answer your assessment questions. Quantitative data can undergo quantitative analysis, and qualitative data can be analyzed either qualitatively or quantitatively.
Qualitative analysis
Given the variety of methods and goals for assessing a connected learning initiative, not to mention the variation in connected learning programs in general, the exact process you follow to analyze your qualitative data will also vary. However, you will probably be following these general steps:
- Read through the data several times until you are thoroughly familiar with the content.
- Perform a content analysis. This can be as simple as applying useful keywords or categories to each response, or, for more formal analysis, developing a “codebook” of descriptive labels (or “codes”).
- Look for themes and patterns in the data you’ve categorized or coded. What codes show up the most? Are there codes that are usually found together? Do some participant groups use certain codes more than other groups? How can you synthesize this data into evidence for the answer to your assessment or evaluation question?
Quantitative analysis
Quantitative analysis is the process of “analyzing data that’s numbers-based.”1 Descriptive analysis provides statistics that describe a group of people or items, like the average attendance of a library program or the median age of participants. Inferential statistics help you predict differences or relationships between groups, or make predictions about the real world based on a sample group. For a crash course on quantitative data analysis, check out this Quantitative Data Analysis 101 Tutorial from Grad Coach.
Quantitative analysis of qualitative data
It is often useful to use quantitative (number-based) analysis on qualitative data. For instance, you can count the number of teens who mentioned interest in a STEM career during their interviews, or what percentage of participants completed a challenge. Be careful not to misinterpret the numbers. For instance, a very talkative teen who gave a 10-minute interview might mention a STEM career more often than a quieter person who only talked for 3 minutes, but that doesn’t necessarily mean the talkative person is more interested in STEM careers.
Worksheet #4: Analyze Talkback Data
Now it’s time to analyze the data you collected from your talk back board. Work through the following steps of the analysis process.
If you have qualitative data…
- If your respondents wrote answers to your questions, you probably have qualitative data. Read through all the responses you gathered, and make notes about possible categories (or “codes”) to label the responses. Keep notes on any other ideas that come to mind during this process.
- When you’re done reading all the responses and taking notes, look back at your list of potential codes. Are there any that should be combined into a broader concept, or any that should be split apart to capture more detail? Are they phrased in a way that accurately reflects the responses you collected? Finalize your code list.
- Read through your responses again, and this time, sort them into the relevant categories. Keep count of how many responses are applicable to each code.
If you have quantitative data…
- If you had close-ended questions — that is, participants just indicated which answer they aligned with — then the work of organizing and categorizing responses is already done. Use the table above to list the number of responses for each answer option.
The Impact Libraries Project has two examples of talkback boards with analysis templates that use Google Sheets. Use these templates if you wish to go further with your data analysis.