1. IEPI – Participate | Collaborate | Innovate

2. Far North Data Disaggregation Workshop September 2017
Identifying Equity Gaps: Data Disaggregation and Disproportionate Impact in Context
Far North Data Disaggregation Workshop September 2017

3. Overview of Session
Help foster a conceptual understanding of data disaggregation and disproportionate impact
Examine the difference between aggregated and disaggregated data
Examine three distinct disproportionate impact methods
Using the Disproportionate Impact Calculator
Session activity (Scorecard pre-work)
Brief introduction to multivariate data disaggregation

4. Data Disaggregation Process
Identification of Outcome
Outcome Data for All Groups Combined
Age
Gender
Ethnicity
Examples: Course Success Rates, Transfer Rate
Aggregated Data
Disaggregated Data

5. Aggregated Data
The first step to identifying equity gaps is to disaggregate the data
Example of Aggregated Data
Success Rate (%)
69,400
46,010
66.30%

6. Disaggregated Data(Cont’d)
Example of Disaggregated Data
Success Rate (%)
African-American 2,547 1, %
Asian 9,834 7, %
Hispanic 35,055 22, %

7. Disproportionate Impact
“Disproportionate impact is a condition where some students’ access to key resources and supports and ultimately their academic success may be hampered by inequitable practices, policies and approaches to student support.” (California Community College Chancellor’s Office, 2013)  
Differences in outcomes among subgroups of students is commonly referred to as Disproportionate Impact (DI)
Example: Differences in access between subgroups groups may suggest that one group has greater access to support services than others
any differences in access and/or completion rates between subgroups groups may suggest that one group has greater access to support services and/or is completing degrees at a higher rate than the other. In this case, the group experiencing less access and/or a lower degree completion is said to be disproportionately impacted.

8. Determining Disproportionate Impact
How do we identify disproportionate impact in disaggregated data?
Three Methods:
80% Index
Proportionality Index
Point Gap Index
Each method has its advantages and disadvantages
The California Community Colleges Chancellor’s Office recommends the percentage point gap method.

9. 80% Index Ethnic Group Cohort Count Outcome Count Success Rate (%)
African-American
2,547
1,388
54.50%
74.79%
American Indian
213
144
67.61%
92.78%
Asian
9,834
7,166
72.87%
100%
Hispanic
35,055
22,304
63.63%
87.32%
Multi-Ethnicity
2,261
1,468
64.93%
89.10%
Pacific Islander
286
153
53.50%
73.42%
White
16,696
11,878
71.14%
97.63%
Unknown
2,508
1,509
60.17%
82.57%
Total
69,400
46,010
66.30%
Source: Fullerton College’s Student Equity Plan

10. 80% Index Advantages: Clear benchmark for identifying DI
Effective between-group approach
Disadvantages
Rigid 80% cutoff can curtail discussion
May be subject to sampling size error if sample size very small

11. Proportionality Index
Ethnic Group
Cohort
Success Outcome
Proportionality Index
Count
Percent
African-American
2,547
3.67%
1,388
3.02%
0.82
American Indian
213
0.31%
144
1.02
Asian
9,834
14.17%
7,166
15.57%
1.10
Hispanic
35,055
50.51%
22,304
48.48%
0.96
Multi-Ethnicity
2,261
3.26%
1,468
3.19%
0.98
Pacific Islander
286
0.41%
153
0.33%
0.81
White
16,696
24.06%
11,878
25.82%
1.07
Unknown
2,508
3.61%
1,509
3.28%
0.91
Total
69,400
100%
46,010
1.00
Source: Fullerton College’s Student Equity Plan

12. Proportionality Index
Advantages:
Effective for assessing equitable representation
Effective within-group approach
Recommended benchmark is 0.86 (i.e., values of 0.85 or lower are reflective of DI; Sosa, 2016)
Disadvantages
No universally accepted benchmark

13. Success Rate (Per Group) Success Rate (Overall)
Point Gap Index
Ethnic Group
Cohort Outcome
Outcome Count
Success Rate (Per Group)
Success Rate (Overall)
Point Gap Index
African- American
2,547
1,388
54.50%
66.30%
-11.8
American Indian
213
144
67.61%
+1.3
Asian
9,834
7,166
72.87%
+6.6
Hispanic
35,055
22,304
63.63%
-2.7
Multi-Ethnicity
2,261
1,468
64.93%
+1.4
Pacific Islander
286
153
53.50%
-12.8
White
16,696
11,878
71.14%
+4.8
Unknown
2,508
1,509
60.17%
-6.1
Total
69,400
46,010
Source: Fullerton College’s Student Equity Plan

14. Point Gap Index Advantages:
Easy to calculate (subtraction-based method)
Practical findings
CCCCO recommended threshold is 2.99 (i.e., values of 3.00 or higher may be reflective of DI)
Disadvantages
DI of most well-represented group may be obscured
No universally accepted benchmark

15. Disproportionate Impact Calculator

16. Session Activity: Site Specific Group Work
Small group activity
Student Success Scorecard Data: Review​ ​disaggregated​ ​trends​ ​in​ ​​transfer-level​​ ​math and​ ​English​ ​attainment​ ​​​from ​Scorecard​
Identify strategies that may serve to mitigate any observed DI

17. UNDERSTANDING COMPLEX STUDENT IDENTITIES – MULTIVARIATE DISAGGREGATION

18. Disaggregated Data (Bivariate)
Mean Success Rates by Foster Youth Status at College of Marin (FA 12 and FA 13)
Mean Success Rate (%)
Pg 14 of MCCD equity plan
Bivariate because it disaggregates success rate by foster youth status.

19. Disaggregated Data (Bivariate)
Mean Success Rates by Gender at College of Marin (FA 12 and FA 13)
Mean Success Rate (%)
Pg 14 of MCCD equity plan

20. Disaggregated Data (Multivariate) – Interaction Present
Mean Success Rates by Foster Youth Status and Gender at College of Marin (FA 12 and FA 13)
Mean Success Rate (%)
Pg 14 of MCCD equity plan

21. Disaggregated Data (Multivariate) Hypothetical Data Illustrating No Interaction
Hypothetical Data: Mean Success Rates by Foster Youth Status and Gender
Mean Success Rate (%)
Dummy data indicating no interaction
Jared: Not really a note, I just really like the FY by gender interaction example. It really is a good visual demonstration.

22. Overview of Multivariate Disaggregation
Multivariate disaggregation can show interactions between demographic characteristics
Example: Does the impact that foster youth status have on success rates depend upon students’ gender?
Better approximation of real-world student
Students simultaneously belong to multiple demographic groups

23. Overview of Multivariate Disaggregation
Where there are two demographic variables, there are two possible individual effects (or main effects) and there is one possible interaction.
Addresses three questions:
Does disproportionate impact (DI) exist with respect to first demographic characteristic (e.g., foster youth status)?
Does disproportionate impact (DI) exist with respect to second demographic characteristic (e.g., gender)?
Does disproportionate impact (DI) exist with respect to the joint influence of both demographic characteristic (e.g., foster youth status and gender)?
Better approximation of real-world student lives and experiences

24. Multivariate Disaggregation – Tabular Format
Mean Success Rates by Foster Youth Status and Gender Along with Corresponding DI Index Information
Foster Youth Status
Male
Female
Difference
80% Index
Point Gap Index
Yes
42.26%
62.75%
20.49%
36.55%
12.79 No
68.01%
75.50%
7.49%
100%
0.21
Overall
67.54%
75.24%
7.70%
Source: College of Marin’s Student Equity Plan ( )

25. Conclusions
Identification of equity gaps does not end with data findings
Broad-based institutional dialogue is critical
Consider employing more than one DI method to identify potential equity gaps
The DI calculator offers users a quick way to compute all of the aforementioned indices along with corresponding equity numbers
Multivariate disaggregation allows one to identify DI for each demographic characteristic AND to identify a possible interaction between two or more characteristics
Enhanced real-world validity