2014 Annual Educational Conference

1. 2014 Annual Educational Conference Moving a Large Human Service Department from Check-only-one to Check-all-that-apply (CATA) Race and Ethnicity Options

2. What We’ll Cover Today • Introduction • Assumptions, terms • What are we measuring? • Multi-racialism • Saga of our human services department moving from check-only-one to check-all-that-apply • Details, details • Asking the questions • Storing the data • Data analysis and presentation • Questions, discussion

3. Beginning Assumptions, Defining and measuring race, Multi-racialism What are we measuring?

4. Beginning Assumptions • Just about everything about the measurement of race/ethnicity/origin is controversial. • The measurement of race/ethnicity/origin is evolving. • Measurement of race/ethnicity/origin is a technology, process, a function in service of policy. • Race is a political, social and cultural creation. Both the determination of race and its measure have a very unsavory past and a tortured present.

5. Terms… • Phenotype: How closely a person reminds people of one of the big five races or ethnicity. • Race: A grouping of people based on phenotype who originated in an insular geography. • Ethnicity: a cultural grouping within a race. • National origin: The country or continent where you or a close blood relative or perhaps an ancestor was born.

6. Carl Linnaeus, 1735,The Four Races • Americanus: reddish, choleric, and erect; hair black, straight, thick; wide nostrils, canty beard; obstinate, merry, free; paints himself with fine red lines; regulated by custom. • Asiaticus: sallow, melancholy, stiff; hair black; dark eyes; severe, haughty, avaricious; covered with loose garments, ruled by opinion. • Africanus: black, phlegmatic, relaxed; hair black, frizzled; skin silky; nose flat; lips tumid; women without shame, they lactate profusely; crafty, indolent, negligent; anoints himself with grease; governed by caprice. • Europeaeus: white, sanguine, muscular; hair long, flowing; eyes blue; gentle, acute, inventive; covers himself with close vestments; governed by laws.

7. 2010 Census Choices

8. History of Multi-racialism • Rape of female African slaves by white slave owners and other whites, resulting in the “browning” of enslaved Americans. • After emancipation, racial purity emphasis along with Jim Crow; legal separation of the races. • 1890 Census, first “multi-racial” count: white, black, mulatto, quadroon, octoroon. Also Chinese, Japanese or [East] Indian. • Early twentieth century Eugenics movement.

9. Multi-racialism, continued • Loving case, 1947 • “Almighty God created the races white, black, yellow, malay and red, and he placed them on separate continents. And but for the interference with his arrangement there would be no cause for such marriages. The fact that he separated the races shows that he did not intend for the races to mix.” • 1967 – last state miscegenation legislation struck from the books. • Hispanic ethnicity: Hispanic and …

10. What are the facilitators and barriers to implementation of CATA in your organization? Why should your organization measure race? Moving a large human services department to check-all-that-apply

11. What Human Services Needs to Measure • Equal, equitable and appropriate access to services • Is there unequal access to services or service outcomes by people on the “wrong” side of the color line? • Are we serving the needs of immigrant populations? • Rates of service penetration in distinct race/ethnic/origin communities. • Interest in emerging populations we cannot see. • Interest in special needs of race/ethnic/origin populations.

12. 2010: The Stars Align External Motivators Coalition of Communities of Color Report County Health Department Equity Initiative: Equity and Empowerment Lens Provided rationale and language to articulate need IT Services not opposed Relatively new Human Services Department Director and R&E Analyst Division Directors not opposed Internal Motivators

13. 2010 • Creation of a work group: the “Visibility Initiative” • Charter from director to all data staff in all human service divisions • Emphasis on collaborative and incremental • Job One: Mapping of data systems, potential for change • “What we can change, what we can’t change, and know the difference.”

14. 2011- 2012 • Job Two: List of races, ethnicities, and origins • The “granularity” question • Eventually, took Coalition of Communities of Color advise – reflection of our county population African Native American or Alaska Native Asian Native Hawaiian or Pacific Islander Black/African American Slavic Latino/Hispanic White Middle Eastern Decline to Answer

15. 2012-13: Implementation • Department policy statement (not countywide policy) • Once again, collaborative and incremental • Job Three: Training for roll-out • Oregon Hospital Association, nice video • Emphasis on… • Client self-identification • Use of the already-set groups (no “Other”, no write-ins) • When to use “Prefer not to answer” “If we discover inequities, then we’ll be expected to do something about it.”

16. Future of the Visibility Initiative • What do we do with the data? • How do we coordinate with the State of Oregon? • Monitoring and insuring implementation over time • What about other demographic variables?

17. 2014 • National Association of Counties award for innovation

18. Let’s collect some data. Choices: collecting the data

19. Collecting CATA Data • Overview • CATA or yes/no forced choice? • Equal status vs. roll-up? • Asian-American • Chinese • Filipino • Japanese • Korean • Vietnamese • Native Hawaiian • Guamanian or Chamorro • Samoan

20. Collecting CATA Data • Web-based surveys • Wonky alternative: Dropdown multiple selection boxes • Multiple boxes, multiple selection

21. Collecting CATA Data • Web-based surveys • Wonky alternative: slider bars that represent percent of each race/origin

22. Asking CATA Questions • Paper Survey • Considerations • “Real estate” • Confusion if asking both CATA and COO questions • Radio buttons and check boxes

23. Asking CATA Questions • Phone interviews • Too many options! Alternative: • Ask as yes-no questions • Ask for self-report and then extrapolate • In-person interviews • Show the CATA questions and have the interviewee complete or point to the relevant options • Ask for self-report and extrapolate

24. Training of Those Asking CATA Questions • Visibility Initiative put a great deal of time into training and roll-out • Content • Rationale • How we got here • County becoming more diverse • Missing crucial data by using check-only-one

25. Training, continued • Consistency insures better data • Address questions, concerns: • Client resistance or questions • Worker resistance or questions • FAQs • On-line training

26. How many characters do you have to work with? How old and creaky are your data systems? Is there anyone still around with the knowledge to change your system? Choices: Storing the data

27. Storage of CATA Data • Old systems • Possibly less flexible • Possibly only one field • Only one or two digits assigned to the field • Can’t change, or too much trouble to change? • If new or developing system • No problem, as long as there is the political will to do so

28. Storage of CATA Data • Old system • Only one field, opportunity to use many digits • Reduce responses to 1 = checked and 0 = unchecked • Number is as long as number of options • Ex.: Eleven options, three races/origins selected: 00100101000. • Wonky alternate, one field, many digits • Place more common selections early on in the list • Use prime numbers to count number of items (2,3,5,7,11,13,17, …) • Multiply selection by the prime associated with it.

29. Storage of CATA Data • Old data system • Only one field, only about four digits • Assume a maximum number of respondent choices of race/ethnicity/origin (Say, 4 choices) • Label race/eth/orig options 1 to x • Record choices in four digits, e.g., 3125 Key: 1 = African 2 = Asian 3 = Black 4 = Latino 5 = Middle East 6 = Native Am. 7 = Native Hawaiian 8 = Slavic x x x Response above: “1360”

30. What do we need to know? Choices: data analysis and Presentation

31. Overview • Analytic Challenges • “Rolling up” multi-racial respondents into a single race to suit funders • Small cell size for multi-racial respondents • The dangers of rolling up to a single “multi-racial” or “other” response • The “greater than 100%” challenge

32. Analysis of CATA Data • Office of Management and Budget, 2000 • Include those who indicated membership in one of the five single race categories: White, American Indian/Alaskan Native, Black/African American, Native Hawaiian/Other Pacific Islander, and Hispanic/Latino • Include the four most prevalent double race combinations • Native American/White • Asian/White • African Am./White • Native Am./African Am. • Include all other combinations that represent more than one percent of the sample population.

33. Analysis of CATA Data • Multiple group assignment • Each respondent is fully counted in each group checked • Inflates categories • The “More than 100%” challenge • Wonky workaround: Assignment of understood fractions (e.g., .5 Asian and .5 White for respondents checking White and Asian) • No more “multiracial” • Lots of validity concerns

34. Descriptive Presentation of client populations

35. COO vs. CATA Presentation

36. Presentation of CATA Data Simple graphical presentation: Bar within pie Pie within pie Asian/White = 45% Black/AmInd= 10% Black/White = 45%

37. Questions? Comments? Discussion?