Postsecondary District Comparison Tool

Step 1: Select a District

Step 2: Selection Criteria

  • Please select a district

Comparable Districts Based on Selected Criteria


Additional Information

Compare Based on Enrollment: Check this box, and those districts closest to the selected district in 2015 audited enrollment will be listed. The audited enrollment counts exclude pre- kindergarten and not graded students.

Compare Based on Risk Factors: Check this box, and the districts that are most similar to the selected district in their predicted levels of risk will be listed. Predicted risk is based on a combination of four factors—cumulative poverty, student mobility, and chronic absenteeism. These three factors significantly influence effectiveness rates.

In most cases, the selected districts will have similar levels of risk on all four risk factors, but especially on the dominant factor, the level of cumulative poverty. For more about the risk factors, see the Risk Factors tab.

Both boxes checked: The software will report those districts that appear in both lists. Sometimes there are few or no districts that are similar in both enrollment and predicted risk.

Limitations: If a district is unusually high in a particular factor, for example, in chronic absenteeism, it might not be similar to the other districts in its other risks, even though it has a similar level of predicted risk.

For districts at the very highest or very lowest levels of predicted risk, there may be very few or no districts above or below them in predicted risk levels, so few or no similar districts will be reported.

Why did KSDE account for risk factors?

KSDE researchers used a set of risk factors and linear regression to predict the average postsecondary effectiveness rates. By including the risk factors as independent variables, the predicted effectiveness rates account for them. Once we’ve accounted for risk factors in the predicted rate, we can compare districts’ actual effectiveness rates to their predicted ones. It is a way to compare all districts on their effectiveness rates after factors known to depress effectiveness rates, over which districts have limited influence, have been accounted for.

What were the risk factors included in the regression?

KSDE researchers used linear regression to identify factors that depress districts’ postsecondary effectiveness rates. The significant detractors were (1) cumulative poverty, (2) student mobility, (3) chronic absenteeism, and (4) expulsion and suspension rates. They explained half of the variance of accredited districts’ effectiveness rates. Students’ cumulative poverty was by far the strongest detractor of effectiveness rates.

In a separate regression, the percentage of non-AP virtual students also significantly lowered district effectiveness rates.

Cumulative poverty: The proportion of the students’ school years spent in poverty. A school year in which the student spent some time eligible for free lunch was valued at one. If the student was not eligible for free lunch during the school year, but was for reduced-price lunch, the school year was valued at 0.5. For the selected five cohorts, these values were combined into the numerator. The denominator was the count of the total number of school years the selected five cohorts have attended Kansas schools.

Student mobility: The proportion of school changes during the school year. After selecting for the grades and years the five cohorts attended Kansas schools, the numerator is the number of times these students changed schools during a school year. The denominator was the count of the total number of school years the selected five cohorts have attended Kansas schools.

Chronic absenteeism: After selecting the five cohorts, the numerator was the count of student years in which students missed at least 10 days or more in at least one school. The denominator was the count of the total number of school years.

Expulsion and suspension rates: Until recently, expulsion and suspension reporting was not tied to the individual student records reported to KSDE. The expulsion and suspension rates used here are derived from school and district aggregate reports. The influence of discipline measures over effectiveness rates would have been more precisely measured if these measures had been derived from individual student records. But, even as a general district measure, these rates were significant predictors of depressed effectiveness rates. The numerator was the total district count of expulsion and suspension events from 2012 through 2015. The denominator was the total audited enrollment count of students across the same time span, with duplicate students across years included.

Included students: To improve the accuracy of the regression model, individual students were selected for inclusion in the calculations of the independent variables if they attended Kansas schools with the five high-school cohorts included in the calculation of the effectiveness rates. For example, for reporting effectiveness rates in 2017, the five most recently available cohorts were the high-school graduating classes of 2011 through 2015. Some among the classes of 2014 and 2015 have longitudinal records reaching back to fifth grade in 2007 and 2008, respectively. All the classmates attending Kansas schools in the same years and grades as the students in the cohorts were included in the calculations of the independent variables except for expulsion and suspension and the percentage of new teachers.

The independent variables that had no significant effect on district effectiveness rates were: (5) the percentage of teachers new to their buildings, (6) the proportion of years students’ were identified as having a disability, and (7) the proportion of students’ years in which they were classified as English Learners.

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