Methodology

The Equity Index scores are based on more than 20 individual metrics secured primarily from government and other official sources, using the latest available data for each metric. We intend to add additional metrics, comparisons with individual cities, and data to show progress over time.

All data is based on a Index of Disparity constructed in a research paper (Percy Keppel 2002) and adopted by Policy Link’s National Equity Index.

This equation is relatively simple, performs well at diagnosing inequity, and offers an unbiased approach when assessing different race/ethnic groups and population sizes.

In short, the Index of Disparity measures the dispersion of a given subgroup (in our case race/ethnicity) around a metrics overall average. When large differences between groups are recorded, the Index will report larger numbers indicating inequity.

After applying the Index of Disparity equation to each metric, we take the square root of each metric score to limit the effect of outliers on the data.

We scale the numbers from 0-100 where 0 represents the largest example of disparity between race/ethnic groups and 100 represents no disparity between race/ethnic groups. The 0-100 scale is used to make the data presentation more intuitive for the audience.

Now that each metric score is calculated and scaled properly, we find the framework score (Proficiency, Excellence, Access) by taking the geometric mean of each metric in each framework.

Lastly, we find the life stage score (K-8, HS, College, Career) by taking the geometric mean of each framework score in each life stage.

Index calculation

Index of disparity=[riR]RNIndex~of~disparity = \frac{\sum{[|r_i - R|]}}{R * N}

GeometricMean=x1x2...xNNGeometric Mean = \sqrt[N]{x_1 * x_2 * ... * x_N}

ScalingEquation=100Index of DisparityiMin(Index of Disparity)Max(Index of Disparity)Scaling Equation = 100 - \frac{Index~of~Disparity_i - Min(Index~of~Disparity)}{Max(Index~of~Disparity)}


Proficiency: How well are we preparing Black and Latino students to pursue and succeed on Computer Science pathways?

Access: How much access do Black and Latino communities have to the resources and opportunities necessary to succeed on Computer Science pathways?

Excellence: How well are we preparing Black and Latino students to succeed at elite levels on Computer Science pathways?