1. Beyond the general linear model: Using a mixed modeling approach to test the effects of “gender blind” selection policies
Amber K. Lupo, M.A.
University of Texas at El Paso
Quantitative Presentation

2. Background Many organizations adopt a “gender blind” policy
Many organizations also state that they are committed to increasing diversity

3. “Gender Blind”
“Purdue University promulgates policies and programs to ensure that all persons have equal access to its employment opportunities and educational programs, services and activities. The principal objective of this policy is to provide fair and consistent treatment for all students and employees of the University.”

4. “Gender Aware”
“Purdue is committed to increasing the recruitment, selection and promotion of faculty and staff at the University who are racial or ethnic minorities, women, persons with disabilities and veterans. The University also is committed to policies and programs that increase the diversity of the student body.”

5. Color Blind vs Multicultural Ideologies
Priming color blindness leads to greater implicit and explicit race bias (Richeson & Nussbam, 2004)
Priming multiculturalism leads to stronger, but more accurate, outgroup stereotypes (Wolsko, Park, Judd, & Wittenbrink, 2000)
Gender blind ≈ color blind

6. Research Question
What are the effects of a “gender blind” vs “gender aware” policy on evaluations of men and women?
How does a purported “gender blind” vs “gender aware” UTEP policy affect evaluations of undergraduate scholarship applicants?

7. 2 X 2 X 2 X 3 mixed design Policy Type Gender blind OR Gender aware
Program Type
Nursing
AND
Engineering
Applicant gender
(2 levels, randomized)
Applicant gender
(2 levels, randomized)
Qualification strength
(3 levels, randomized)
Qualification strength
(3 levels, randomized)

9. Dependent Variables
Recommendation of applicant for scholarship (1 – 10)
How deserving is the applicant (1 – 10)
Percent of tuition to be paid (0 – 100%)
How much did you consider gender in decision (1 – 4)

10. Hypothesis
Compared to men, women applicants with ambiguous qualifications will face more discrimination under a gender-blind vs gender-aware policy

11. Between-Subjects Effect
Policy Type
Between-Subjects Effect
Gender blind OR
Gender aware
Program Type
Within-Subjects Effect
Nursing
AND
Engineering
Applicant gender
(2 levels, randomized)
Applicant gender
(2 levels, randomized)
Incomplete repeated measures
Subjects assigned to 2 of 12 possible conditions
Qualification strength
(3 levels, randomized)
Qualification strength
(3 levels, randomized)

12. General Linear Modeling (GLM) vs. Mixed Modeling
Mixed Models -->
Combination of fixed and random effects
Nested factors
Repeated measures

13. Repeated Measures: Correlated Within-Subjects Errors
Repeated measures ANOVA assumes sphericity
Corrections for violations alter df, not model fit
Mixed models allow estimation of the variance-covariance matrix
Model comparison

14. Repeated Measures: Missing Data
GLM uses least squares estimation  observations with missing data discarded
Mixed models use a maximum likelihood estimation (REML)  observations with missing data are included

15. Repeated Measures: Subjects as Random, Nested Effects
Mixed models can include subject or person as a random effect in the model
Test appropriate variance-covariance structure

16. Proc Mixed (SAS)
Data prep
Wide format vs Long format

19. Syntax
ALL effects go on class statement (includes repeated effects)

20. Syntax
Model statement is the same as GLM

21. Syntax
Repeated statement specifies structure of within subject errors Subject = name of your subject identifier

22. Syntax
Type specifies the type of variance-covariance matrix (default - variance components) - Model comparison (chi-square difference test)

23. Syntax “Subject” is a random effect
No need for separate “random” statement with repeated measures (usually; Liu, Cao, Chen, & Zagar, 2007)

24. Results Analyses for Ambiguous condition only UTEP undergrads
N = 287; 62% women

25. Predictors: Policy (Gender blind or Gender aware), Program (Nursing or Engineering), and Applicant Gender
Across all conditions, no differences in self-reported ratings of how much gender was considered in decisions, all p’s > .06
But yet effects of applicant gender found for all evaluation DVs

26. Applicant Gender
Male Female
Difference
(CI) d
Recommendation1
7.90 (0.18)
6.63 (0.18)
1.26
(1.80; 0.72)
0.48
Deserving2
8.01 (0.19)
6.71 (0.19)
1.30
(1.85; 0.74)
0.47
Percent Tuition Paid3
75.42% (2.26)
60.78% (2.19)
14.65
(21.04; 8.26)
0.46
1. F(1,21) = 23.90, p < .0001
2.F(1,21) = 23.69, p < .0001
3. F(1,19) = 23.00, p = .0001

27. Combined 3 DVs into an overall evaluation score (α = 0.92)
Analyses for men and women, separately

28. For women: Main effect of Applicant Gender, F(1,11) = 20.61, p = .0008
Male Female
Difference
(CI) d
Overall Evaluation
7.69 (o.24)
6.17 (0.24)
1.52
(2.26; 0.78)
0.55

29. For men: Main effect of Applicant Gender, F(1,8) = 5.77, p = .05
Male Female
Difference
(CI) d
Overall Evaluation
7.85 (.30)
6.88 (.27)
0.97
(1.90; 0.04)
0.38

30. Policy*Program*Applicant Gender interaction, F(1,16) = 5.61, p = .04
Gender Blind
Gender Aware
Male Female
Engineering Engineering
Male Female
Engineering Engineering
Overall Evaluation
7.89 (0.53)a
5.71 (0.48)b
7.91 (0.72) ac
7.81 (0.57) ac
t(16) = 2.54, p = .03
t(16) = 0.12, p = .91

31. Gender Blind Gender Aware Male Female Nursing Nursing Male Female
Overall Evaluation
7.38 (0.59)a
7.46 (0.51)ab
8.23 (0.55) ac
6.55 (0.59) abc
t(16) = .10, p = .93
t(16) = 2.08, p = .06

32. Conclusions
Policies aimed at ignoring gender to promote equality may hurt women
Policies aimed at highlighting diversity may help, but are not sufficient
Overall, men appear to be v alued more than women, even among women
So, what do we do?

33. Future Directions Direct comparisons of men and women
How do participants justify their decisions?

34. References
Hamer, R. M., & Simpson, P. M. (2015). Mixed-up mixed models: Things that look like they should work but don’t, and things that look like the y shouldn’t work but do. In Proceedings of the Twenty-Fifth Annual SAS Users Group International Conference [Online].
Liu, C., Cao, D., Chen, P., & Zagar, T. (2007). RANDOM and REPEATED statements – How to use them to model the covariance structure in Proc Mixed. In SAS Conference Proceedings: Midwest SAS Users Group.
Richeson, J. A., & Nussbam, R. J. (2004). The impact of multiculturalism versus color-blindness on racial bias. Journal of Experimental Social Psychology, 40,
Wolfinger, R., & Chang, M. (1998). Comparing the SAS GLM and MIXED procedures for repeated measures.
Wolsko, C., Park, B. Judd, C. M., & Wittenbrink, B. (2000).Framing interethnic ideology: Effects of multicultural and color-blind perspectives on judgments of groups and individuals. Journal of Personality and Social Psychology, 78(4),

35. Questions/comments