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

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

“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.”

“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.”

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

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?

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)

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)

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

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)

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

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

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

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

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

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

Syntax Model statement is the same as GLM

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

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

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

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

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

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

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

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

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

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

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

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?

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

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, 417-423. 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), 635-654.

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