Chancellor’s Office Data Resources for Student Success October 7, 2015 Ryan Fuller, Research Specialist, Research, Analysis and Accountability, California Community Colleges Chancellor’s Office   Tom Leigh, Research Specialist, Research, Analysis and Accountability, California Community Colleges Chancellor’s Office

Datamart http://datamart.cccco.edu/

What’s in Datamart Student Counts/Enrollments (by gender, age, ethnicity) Headcount Enrollment Status FTES Unit load Education status Citizenship status Distance Education

What’s in Datamart Outcomes Retention/Success Awards (degree/certificates) Grade distribution Basic Skills completion Earnings (salary surfer) Student Success Scorecard metrics Transfers (public & private)

What’s in Datamart Courses Credit & Non-credit sections Basic Skills sections Course details Academic Calendar

What’s in Datamart Student Services Financial Aid Matriculation Special Population data (Foster Youth, First Generation etc.) DSPS CalWorks EOPS Assessment types

What’s in Datamart Faculty & Staff Demographic reports

Disproportionate Impact Disproportionate impact occurs when “the percentage of persons from a particular racial, ethnic, gender, age or disability group who are directed to a particular service or placement based on an assessment instrument, method, or procedure is significantly different from the representation of that group in the population of persons being assessed, and that discrepancy is not justified by empirical evidence demonstrating that the assessment instrument, method or procedure is a valid and reliable predictor of performance in the relevant educational setting.” [Title 5 Section 55502(d)]

80 % Rule Gap Analysis Proportionality Measuring Disproportionate Impact: 80 % Rule Gap Analysis Proportionality Outlined in detail in the 2015 Student Equity Plan Template

Table 1. 2008-09 Cohort Six-Year Transfer Rate Ethnicity Cohort Count Percent African-American 7,490 6.06 2,566 5.51 American Indian/Alaskan Native 1,079 0.87 314 0.67 Asian 21,674 17.54 10,765 23.10 Hispanic 43,329 35.06 12,662 27.17 Multi-Ethnicity 29 0.02 12 0.03 Pacific Islander 1,303 1.05 452 0.97 White 48,671 39.39 19,828 42.55 Smaller sample sizes less reliable. Do not analyze for samples less than ten.

80% Rule The methodology is based on the Equal Employment Opportunity Commission (EEOC) 80% Rule, outlined in the 1978 Uniform Guidelines on Employee Selection Procedures, and was use in Title VII enforcement by the U.S. Equal Opportunity Commission, Department of Labor, and the Department of Justice.

80% Rule The 80% Rule methodology compares the percentage of each disaggregated subgroup attaining an outcome to the percentage attained by a reference subgroup. Any disaggregated group that is included in a desired outcome at less than 80% when compared to a reference group is considered to have suffered an adverse – or disproportionate - impact.

American Indian/Alaskan Native 1,079 314 0.29 0.59 Asian 21,674 10,765 Table 3. 80 % Rule With Highest Performing Reference Group By Ethnicity Ethnicity Cohort Transfer Percent Transfer 80 Percent Index African-American 7,490 2,566 0.34 0.69 American Indian/Alaskan Native 1,079 314 0.29 0.59 Asian 21,674 10,765 0.50 1.00 Hispanic 43,329 12,662 Multi-Ethnicity 29 12 0.41 0.83 Pacific Islander 1,303 452 0.35 0.70 White 48,671 19,828 0.82 Statewide 123,575 46,599 0.38 Sometimes difficult to select the best reference group.

Table 2. 80 % Rule With Largest Reference Group By Ethnicity Ethnicity Cohort Transfer Percent Transfer 80 Percent Index African-American 7,490 2,566 0.34 0.84 American Indian/Alaskan Native 1,079 314 0.29 0.71 Asian 21,674 10,765 0.50 1.22 Hispanic 43,329 12,662 0.72 Multi-Ethnicity 29 12 0.41 1.02 Pacific Islander 1,303 452 0.35 0.85 White 48,671 19,828 1.00 Statewide 123,575 46,599 0.38 Problematic if one disaggregated subgroup comprises a large portion of the sample.

Table 2. 80 % Rule With Statewide Reference Group By Ethnicity Cohort Transfer Percent Transfer 80 Percent Index African-American 7,490 2,566 0.34 0.91 American Indian/Alaskan Native 1,079 314 0.29 0.77 Asian 21,674 10,765 0.50 1.32 Hispanic 43,329 12,662 Multi-Ethnicity 29 12 0.41 1.10 Pacific Islander 1,303 452 0.35 0.92 White 48,671 19,828 1.08 Statewide 123,575 46,599 0.38 1.00 Sometimes use overall rate as reference group. (Produces the same values as proportionality.)

Gap Analysis The percentage point gap methodology compares the percent of students in a disaggregated subgroup who succeed in an outcome with the percent of all students who succeed in the same outcome. Percentage point gap measurements are calculated by subtracting the all student success rate from the success rate of a disaggregated subgroup in the same outcome. According to this methodology, a ‘-3 percentage point gap or greater’ is evidence of a disproportionate impact.

Table 3. Percentage Point Gap By Ethnicity Ethnicity Cohort Transfer Percent Transferred Gap African-American 7,490 2,566 0.34 -0.03 American Indian/Alaskan Native 1,079 314 0.29 -0.09 Asian 21,674 10,765 0.50 0.12 Hispanic 43,329 12,662 -0.08 Multi-Ethnicity 29 12 0.41 0.04 Pacific Islander 1,303 452 0.35 White 48,671 19,828 0.03 Statewide 123,575 46,599 0.38 Imperial Hispanic 89.54%

Proportionality Index The proportionality index reflects differences in percentages for race/ethnicity subgroups between two conditions (milestones or group inclusion). The formula for proportionality is the percentage in the outcome group divided by the percentage in the original cohort (outcome percentage/cohort percentage).

Proportionality Index Interpretation 1.0 Proportions of subgroups are equal. Less Than 1.0 Subgroup is less prevalent in the outcome group. More Than 1.0 Subgroup is more prevalent in the outcome group. Bensimon and Malcom-Piqueux (2014) specified a cutoff of 0.85 to identify performance below equity when proportionality is used as a performance measure.

Table 4. Transfer Proportionality By Ethnicity Cohort Transfer Proportionality Count Percent African-American 7,490 6.06 2,566 5.51 0.91 American Indian/Alaskan Native 1,079 0.87 314 0.67 0.77 Asian 21,674 17.54 10,765 23.10 1.32 Hispanic 43,329 35.06 12,662 27.17 Multi-Ethnicity 29 0.02 12 0.03 1.10 Pacific Islander 1,303 1.05 452 0.97 0.92 White Non-Hispanic 48,671 39.39 19,828 42.55 1.08 Statewide 123,575 100.00 46,599  

REFERENCES Bensimon, E.M., & Malcom-Piquex, L. (2014, March). Assessing Hispanic-Servingness at HSIs. Presented at The Academic Success of Hispanics Conference, American Association of Hispanics in Higher Education.