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3. Set a code to the data collected: Assign labels or codes to different parts of the data to identify
themes, patterns, or categories. For example, the researcher reads through the interview transcripts and
highlights sections discussing "ease of use," "technical issues," and "benefits of the app," tagging them with
corresponding codes.
4. Analyse your data for insights: Examine the coded data to identify deeper patterns, relationships, and
insights. For example, The researcher groups codes related to "ease of use" and "technical issues" into a
broader theme of "user experience" and analyses how these themes impact overall user satisfaction with the
app.
5. Reporting on insights derived from analysis: Present the findings clearly, using quotes and visual aids to
support your conclusions and recommendations. For example, the researcher writes a report highlighting
the main themes along with positive and negative feedback.
Difference Between Qualitative and Quantitative Data Interpretation
Qualitative Data Interpretation Quantitative Data Interpretation
Categorical Numerical
Provides insights into feelings and emotions Provides insights into quantity
Answers how and why Answers when, how many, or how often
Methods – Interviews, Focus Groups Methods – Assessment, Tests, Polls, Surveys
Example question – Why do students like attending Example question – How many students like attending
online classes? online classes?
Task #Coding & Computational Thinking
Word Puzzle
Instructions:
• Partner with a person to play the game.
• There will be three rounds of Word puzzle.
• Play 3 rounds.
Now answer the following questions:
• Who won round one?
• Who won round two?
• Who won round three?
If you answered any of the above questions, you collected data!
Data Literacy 277

