4.6b Data Interpretation

As a data-driven decision-maker, the coach is expected to support educators to interpret qualitative and quantitative data to inform their decisions and support individual student learning.

Data Interpretation

In the Program Evaluation project, after collecting all the raw data from the focus group, interview sessions, and teacher diaries, I had to think through a framework for organizing the data for analysis and interpretation. Since all the data was qualitative in nature, the data analysis and interpretation exercise proved more challenging than dealing with quantitative data.

Sentiment Analysis

I started off by grouping similar topic parent responses together under the same category. Once all the collected data has been categorized and sorted, the data were analyzed to derive insights and actions. As the data collected was mainly qualitative in nature, I used sentiment analysis as an indicator to determine if the sentiment behind each response was positive or negative.

Next, I performed a further layer of analysis on all the responses to determine if each response was a suggestion for something new, a praise for something done well, or a critique for something not done well and needs improvement. I used a color coding system to keep the data organized.

Impact on Decision Making

I found the method of categorizing each qualitative data by either “Start”, “Keep”, or “Stop” to be helpful in guiding the final decision making. The “Start-Keep-Stop” analysis can be summarized as follows:

  • Start – A suggestion to begin doing something that was not thought of previously. Identifies a gap in student needs that has not been previously addressed and hence an opportunity to “start doing.”
  • Keep – A positive response indicating that something was done well or right and should be kept and continued.
  • Stop/Improve – A negative response indicating that something was not working well and that the practice should be discontinued or improved. 


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