Output, Output Metrics, and Data Points

How to define the effective output, output metrics, and Data Point

Outputs are the immediate, tangible results of a program's activities. They indicate whether the desired outcomes are likely to be achieved. Output metrics are quantifiable measures used to assess the success of these outputs. This guide will explore the importance of defining outputs and output metrics, using the "Girls Code" program as an example. We'll also introduce the concept of data points, which are essential for tracking metrics.

Defining Outputs: Outputs are the direct products of program activities. They still need to reflect on change, but these steps are necessary to achieve the desired outcomes.

Example of Output for Girls Code (Example 1): Output: "Girls learn to code and start building applications."

Defining Output Metrics: Output metrics help you measure the success of your outputs. These metrics should be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART).

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

Creating Effective Output Metrics: We can create effective output metrics using the SMART criteria. In the example above, we aim to measure the number of girls who build an app after completing the course. The metric is specific, measurable (count of girls), achievable, relevant to the program's goals, and time-bound (post-course).

Example of Output for Girls Code (Example 2): Output: "Girls get jobs in the tech industry after finishing the course."

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

How to Derive Data Point 

The examples provided below are tailored for users who are proficient in SQL. If you're unfamiliar with SQL, you can still document the data sources without SQL queries.

Data Point for Output Metric 1:

• 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, project_type"
• Calculation: "COUNT of unique project_id where completion_date is within the program period, and project_type is 'app'."

Data Point for Output Metric 2.1:

• 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 Output Metric 2.2:

• Data Point: "Total number of girls that got a job in the tech industry."
• Data Source: "Post-Program Survey data"
• Data Table: "Survey Responses Table"
• Data Fields: "user_id, got_job (Yes/No), job_industry"
• Calculation: "COUNT of unique user_id where got_job = 'Yes' and job_industry = 'Tech'."

Survey Questions for Data Point (Output Metric 2.2): One of the survey questions that can provide data for this output metric is:

• Did you get a job after completing the course? (Yes/No)
  • If yes, what industry did you get a job in? (Dropdown options: Tech, Healthcare, Education, etc.)

By defining specific outputs, output metrics, and associated data points, we can effectively track and measure the progress and impact of the "Girls Code" program. These metrics and data points will provide valuable insights into the program's success in equipping girls with coding skills, enabling them to build applications, and helping them secure jobs in the tech industry. The use of