Data Points

Data points are information that feeds metrics, allowing you to track progress. Data points are essential for mapping between the logic model and the dashboard.

Why Data Points Matter: Data points provide an apparent binding factor between your program's goals and the data that reflects progress. Accurate data points ensure you can make informed decisions and assess program impact.

Defining Data Points for Activity Metrics, Output Metrics, and Outcome Metrics:

Data Point for Activity Metric (Girls Code Activity Metric): Activity Metric: "# number of girls registered in the online platform." Data Point: "Total girls registered." Data Source: "LMS system (Learning Management System)."

Data Table: "Users Table"

Data Fields: "user_id, registration_date."

Calculation: "COUNT of unique user_id where registration_date is within the program period."

Data Point for Output Metric (Example 1): Output Metric: "# number of girls that build an app post course."

Data Point: "Total girls who built an app."

Data Source: "LMS system (Learning Management System)."

Data Table: "Projects Table" Data Fields: "project_id, user_id, completion_date"

Calculation: "COUNT of unique project_id where completion_date is within the program period and project type is 'app'."

Data Point for Output Metric (Example 2): Output Metric: "# Number of girls that finished the course successfully with a passing score on the completion test."

Data Point: "Total girls that passed the coding test." Data Source: "LMS system (Learning Management System)"

Data Table: "course_tests table"

Data Fields: "test_id, user_id, passing_score, score_obtained, test_completion_date" Calculation: "COUNT of unique user_id where score_obtained >= passing_score and test_completion_date is within the program period."

Data Point for Outcome Metric (Outcome Metric Example): Outcome Metric: "% of girls that earn more than the median salary for the position based on the location."

Data Point 1: "The median salary for that location and that position." Data Source 1: "Job market data" Data Table 1: "Salary Survey Table"

Data Fields 1: "job_title, location, median_salary"

Calculation 1: "SELECT median_salary WHERE job_title = 'Software Developer' AND location = 'XYZ location'."

Data Point 2: "# of girls that have a salary greater than the median income for that position and that location."

Data Source 2: "Survey data"

Data Table 2: "Post-Program Survey Table"

Data Fields 2: "user_id, annual_compensation, job_title, location"

Calculation 2: "COUNT of unique user_id where annual_compensation > (median_salary from Data Point 1) AND job_title = 'Software Developer' AND location = 'XYZ location'."

Using Data Points to Calculate Outcome Metrics: Outcome metrics often require multiple data points for calculation. In the example above, we used two data points to calculate the percentage of girls who earn more than the median salary for their position in a specific location. By gathering data from both job market data and post-program surveys, we were able to calculate the outcome metric accurately.

Conclusion:

Defining clear outputs, output metrics, and data points is critical for tracking your program's progress and measuring its impact. By using SMART criteria and ensuring that your metrics are linked to relevant data points, you can make data-driven decisions and effectively communicate the success of your program to stakeholders.