1. Racial Disproportionality in Identification of Behavioral Disorders: A Longitudinal Analysis of Contextual Factors
AERA Annual Meting
April 12, 2016

2. Present Study Research Question
Examined the cross-sectional and longitudinal trends of in the identification of emotional or behavioral disorders (EBD) referrals in the state of Wisconsin
Included student- and school-level factors
Research Question
To what extent is the individual risk for EBD identification predicted by individual, process, and school factors during the 2008 school year?
Are minoritized students more likely to be assigned EBD status?

3. Disproportionality in US
African American and Native American students are 2-3 times as likely to be placed in special education for EBD (Donovan & Cross, 2002).
African American, Native American, and Latino students receive exclusionary discipline actions more frequently (Office for Civil Rights, 2014).
Results in racial segregation, stigma, negative academic outcomes in schools, lack of access to general education curriculum, drop out, and involvement in juvenile justice system (APA, 2008; Gregory, Skiba, & Noguera, 2010; U.S. Department of Education, 2014).

4. Literature Review
A paucity of research on why and how disproportionality exists in local states in response to individual student- and school-level variables (APA, 2008; Skiba et al., 2014; Sullivan et al., 2013).
The majority of disproportionality studies:
used cross-sectional data
relied on the national data sets (e.g., ECLS) that do not allow inferences about the role of individual factors and often lack sufficient numbers of cases of minority students of interest
lacked robust statistical controls for confounding variables (Morgan et al., 2015)
analyzed the identification of EBD and school discipline separately most often employed single-level models analyzing either student-level or school-level variables (Hibel et al., 2010)

5. Methods

6. Sample: Archival data obtained through institutional agreement
Student data
Demographics
Parent education
Reading & Math Achievement
Removal
Attendance
Special education status
School data
Enrollment
Student demographics
Achievement
Special education rates
Suspension rates
Retention rates
Teacher and principal demographics
Teacher qualifications …
Analyses
Descriptive Analysis
Multi-level logistic regression: Estimate both school- and student-level predictors Model the labeling process

7. Sample & Measures
All PK-12 public school students from Wisconsin ( )
Outcome: Student labeled with EBD
Process Measures:
Lagged achievement, attendance, discipline (OSS)
Student Measures:
Race, gender, grade, FRL, ELL
Organizational Capacity:
% SPED staff, % faculty with SPED cert, % with an advanced degree, same gender principal, same race principal

8. Analytic Approach Model 0: Model 1: Model 2: Model 3:
Cross-sectional, no process variables
Model 1:
Cross-sectional, with lagged process variables
Model 2:
Longitudinal, with lagged process variables
Model 3:
longitudinal, with lagged process variables, and restricted to only students who were EBD=0 at t0

9. Results

10. Results – logistic Regression
Model 0a 0b 1a 1b 2a 2b 3a 3b
Asian
-1.23
-1.15
-1.10
-0.92
-1.62
-1.42
-0.55
-0.36
Hispanic
-0.27
-0.07
-0.59
-0.15
-0.03
0.03
-0.50
0.05
Black
0.34
0.70
-0.67
0.21
0.69
0.81
-0.97
0.00
Native
0.41
0.51
0.29
0.83
0.89
-0.11
0.30
Male
1.32
1.10
1.07
1.15
1.13
0.64
0.61
FRL
1.35
0.84
0.16
0.17
0.77
ELL
-1.46
-1.48
-1.36
-1.40
-0.76
-0.79
-1.53
Process x
Org
EBD students
all
Change only
time
cross-sectional
longitudinal
Statistically significant overrepresentation, p<0.05
Statistically significant underrepresentation, p<0.05
Not statistically significant

11. Discussion
When examining proportionality, the data matter; the models matter.
Need to include variables that reflect the process
Need to include variables that reflect the context
Including existing EBD students with students who obtain EBD labels obfuscates inferences regarding the labeling process.
We find:
Little evidence of racial bias
Gender, poverty, and language are all associated with EBD labeling
Process variables are highly significant (statistically & substantively)
Context variables are highly significant (statistically & substantively)