Enduring Equity Questions: A Sequence Analysis of Citations in Response to Racial Inequity via the Individuals with Disabilities Education Act

Abstract

In this article, we document patterns of local education agency (LEA) disproportionality citations within one U.S. state spanning an 8-year time period immediately following the 2004 reauthorization of the Individuals with Disabilities Education Act (IDEA) to understand the following: (a) how patterns of disproportionality citations manifest over time in suburban locales, and (b) how often LEAs experience and subsequently are able to successfully address a citation for racial inequity in special education outcomes. We descriptively investigated sequence patterns across suburban locales within one state—New York State (NYS), which serves as a representative case for our analysis. We focus on suburban locales because prior research has documented that a school district’s location (e.g., suburban) relates to the time frame within which a school district was able to address a disproportionality citation. In addition, locale-specific characteristics such as segregation in schools and communities and sociodemographic conditions influence the occurrence of racial disproportionality. Our primary questions driving the descriptive inquiry were as follows: How does variation in sequence patterns for IDEA disproportionality citations manifest within and between suburban locales in NYS? What are the policy implications of these patterns? We conclude with specific recommendations for improving IDEA racial equity policy.

Keywords

policy law/legal issues cultural diversity/multicultural issues

The monitoring of racial disparities in the placement, classification, and discipline of students with disabilities is a critical component of the Individuals with Disabilities Education Act (IDEA)—one of the most significant pieces of legislation affecting students with disabilities in U.S. schools to date. With each reauthorization of IDEA (1997 and 2004), U.S. legislators included mechanisms to address racial disproportionality in special education outcomes. The 1997 mechanisms were ineffective and were subsequently improved upon in 2004 when the Office of Special Education Programs (OSEP) created 20 state performance plan (SPP) indicators that states and districts must use to monitor special education outcomes (Albrecht et al., 2012). Of the 20 indicators, three are related to disproportionality: Indicators 4, 9, and 10, and they measure disparities in suspensions, classifications, and placement of students with disabilities by race.

Indicator 4 has two components (4A and 4B). Indicator 4A refers to the percentage of districts that have a significant discrepancy in the rate of suspensions and expulsions of greater than 10 days in a school year for children with Individualized Education Programs (IEPs). Indicator 4B refers to the percentage of districts that have (a) a significant discrepancy, by race or ethnicity, in the rate of suspensions and expulsions of greater than 10 days in a school year for children with IEPs, and (b) policies, procedures, or practices that contribute to the significant discrepancy and do not comply with requirements relating to the development and implementation of IEPs, the use of positive behavioral interventions and supports, and procedural safeguards (U.S. DoE, OSEP, 2017). Indicator 9 refers to the percentage of districts with disproportionate representation of racial and ethnic groups in special education and related services that is the result of inappropriate identification (U.S. Department of Education (DOE), OSEP, 2017). Indicator 10 refers to the percentage of districts with disproportionate representation of racial and ethnic groups in specific disability categories that is the result of inappropriate identification (U.S. DoE, OSEP, 2017). If a local education agency (LEA) or school district is found to be disproportionate under state definitions of either Indicator 4, 9, or 10, it will receive a citation for disproportionality from their state education agency (SEA).

In addition to the SPP Indicators, there are two separate definitions of disproportionality outlined in IDEA. Individuals with Disabilities Education Act Section 618(d) requires states to collect and examine data to determine whether significant disproportionality is occurring in a state and school district. Significant disproportionality requires that each state sets a numerical threshold which alerts to disproportionate outcomes. The concept relates to overrepresentation. It does not consider whether or not compliance with IDEA is achieved, and it does not require a qualitative inquiry into IDEA compliance at the local level. On the contrary, IDEA Section 616(a)(3)(C) requires states to identify local districts with disproportionate representation, which is the result of inappropriate identification of students with disabilities for special education services. It includes issues of both over- and underrepresentation. Under this definition, states must review their IDEA-related policies if found to have significant disproportionality, but confusingly, “an LEA [district] identified with significant disproportionality is not necessarily out of compliance with IDEA” (U.S. DoE, OSEP, 2017, p. 18).

The twofold definition of racial disproportionality creates a confusing policy landscape for SEAs and LEAs to manage (Skiba, 2013). It is exacerbated by the latitude given to states to define disproportionality, develop a formula for identifying disproportionality, and identify a numerical threshold that alerts to the issue (Albrecht et al., 2012; Voulgarides et al., 2017). What has resulted is that there is considerable variability in how SEAs measure and address racial inequity in special education. The U.S. Government Accountability Office (U.S. GAO, 2013) found that some states identify racial inequity very quickly with a lower numerical threshold, whereas others have less rigorous criteria to address the inequity.

Given the complex and confusing policy space surrounding the measurement and tracking of racial disproportionality and the equity implications of the issue, it is imperative that research inform IDEA racial equity policy remedies. It is important to understand how the current policy structure relates to patterns of racial inequity in special education so that new approaches can be incorporated into future IDEA reauthorizations.

Purpose and Scope

In this article, we document patterns of LEA disproportionality citations within one state spanning an 8-year time period immediately following the 2004 reauthorization of IDEA to understand the following: (a) how patterns of disproportionality citations manifest over time in suburban locales, and (b) how often LEAs experience and subsequently are able to successfully address a citation for racial inequity in special education outcomes. We descriptively investigated sequence patterns across suburban locales within one state—New York State (NYS), which serves as a representative case for our analysis. It has the proportionally highest percentage of students in public schools served under IDEA in the United States, at 15%, and there was a greater than 10% increase in the number of students served under the IDEA, Part B, between 2000–2001 and 2015–2016 (Snyder et al., 2019). In addition, during the time period of the study, NYS set a rigorous bar for identifying and addressing disproportionality. Therefore, we hypothesized that the large number of students serviced under IDEA coupled with the rigorous approach for addressing disproportionality provided a unique case for understanding how often suburban locales experienced citations for disproportionality.

We focus on suburban locales because prior research has documented that a school district’s location (e.g., suburban) relates to the time frame within which a school district was able to address a disproportionality citation (Voulgarides et al., 2013). In addition, locale-specific characteristics such as segregation in schools and communities (e.g., Eitle, 2002) and sociodemographic conditions (e.g., Fish, 2019) influence the occurrence of racial disproportionality.

In the following sections, we describe trends associated with racial disproportionality in special education outcomes to contextualize our analysis. We then provide a descriptive sequence analysis of citation patterns within suburban locales across Indicators 4, 9, and 10 within NYS, immediately following the 2004 reauthorization of IDEA up to the 2011–2012 school year. We conclude with specific recommendations for improving IDEA racial equity policy. Our primary questions driving the descriptive inquiry were as follows: How does variation in sequence patterns for IDEA disproportionality citations manifest within and between suburban locales in NYS? And, what are the policy implications of these patterns?

What Is Racial Disproportionality?

Racial disproportionality refers to the unequal representation of students by race in special education outcomes whereby two patterns are associated with the inequity—underrepresentation and overrepresentation. Each dimension of the inequity, over- and underrepresentation, points to significant educational opportunity gaps (Carter & Welner, 2013) that restrict student access to high-quality educational services and supports.

The well-documented significant disproportionate representation (i.e., over- or under-representation) of Black, Latinx, Alaskan Native, and Native American students in special education classifications, placements, and suspensions in the United States has been largely uncontested for decades (e.g., National Research Council, 2002) and the associated patterns of inequity are alarming. For instance, despite representing only 1% of all students enrolled in public schools during the 2017–18 school year, 17.5% of American Indian/Alaskan Native students were identified as having a disability under IDEA. Disproportionate representation also occurred among students identifying as having two or more races and Latinx. In contrast, White students represent approximately 48% of students enrolled in the public school system but only account for 14% of students served under IDEA (Hussar et al., 2020). Relatedly, students of color with disabilities are disproportionately subject to exclusionary disciplinary practices (U.S. DoE Office of Civil Rights, 2019). Students with disabilities are also more than twice as likely to receive an out-of-school suspension (13%) than students without disabilities (6%) and the disparities are compounded by race and gender. The U.S. DoE Office of Civil Rights (2014) Report shows that more than 25% of boys of color with disabilities and nearly 20% of girls of color with disabilities received an out-of-school suspension as compared with 12% of White boys with disabilities and 6% of White girls with disabilities.

The harmful effects of racial imbalances in special education place students of color in triple jeopardy—the increased likelihood of a student being misclassified as having a disability, coupled with the greater likelihood of being placed in the restrictive educational settings, and finally a greater likelihood a student will receive poor-quality educational services within those settings (Losen & Orfield, 2002). Furthermore, special education labels carry a considerable level of social stigma (Wagner et al., 2007), and special education placements can be permanent, with limited access to a full curriculum resulting in students having a lower likelihood of being eligible for admissions to a postsecondary institution (Fierros & Conroy, 2002; Harry & Klingner, 2014). Relatedly, graduation rates from special education are often low and limit future educational attainment and occupational opportunities (Wells et al., 2003). As a result, students of color labeled with a disability are often denied access to equitable educational opportunity through special education labels.

The triple jeopardy of special education is also exacerbated by exclusionary disciplinary policies and practices. First, removing students from the classroom in K–12 schooling increases the likelihood of academic disengagement, chronic absenteeism, academic failure, and additional school disciplinary problems (Mitchell & Bradshaw, 2013). Students who receive exclusionary discipline are also more likely to be pushed out of school (Brownstein, 2010; Fenning & Rose, 2007), engage in law-breaking behavior, and use illegal substances (Balfanz et al., 2015; Fabelo et al., 2011). Rutherford and colleagues (2002) found that students with disabilities are disproportionately represented in the correctional system and that youth classified in the high-incidence disability categories (e.g., learning disabilities and emotional disturbances) are four times more likely to enter the correctional system than their nonclassified peers, potentially contributing to the school-to-prison pipeline (Kim et al., 2010).

Despite widespread acknowledgment of racial inequities in special education and the associated negative outcomes, over the past several years, scholars have challenged the commonly held assumption that Black, Latinx, and Indigenous students are the groups most affected by racial disproportionality. Several studies found that when academic and behavioral factors are controlled for, Black and Latinx students appear to be underrepresented in special education and Native American and Alaskan Native students are not overrepresented in special education outcomes (e.g., Hibel et al., 2010; Morgan et al., 2012, 2015). These findings have proven to be extremely controversial as racial inequities in special education outcomes have been defined as a major civil rights concern for decades (e.g., Artiles, 2019; Skiba et al., 2008).

The debate about the magnitude and direction of racial disparities in special education outcomes has also spurred discussion about the appropriateness of IDEA policy to effectively address the inequity. Morgan et al. (2015) found there is underrepresentation of students of color in special education and subsequently asserted “current federal educational legislation and policymaking designed to minimize overidentification of minorities in special education [IDEA SPP Indicators] may be misdirected” (p. 288). At the federal level, the Department of Education tried to tighten the policy landscape around disproportionality and announced regulatory changes that further refined the policy approach for addressing and identifying the inequity during the Obama administration. The proposed regulations required a standardized approach for identifying significant racial disproportionality, in direct response to the U.S. GAO (2013) report on differential disproportionality monitoring across the United States. The final rollout of the proposed regulations was set to begin in the 2018–2019 school year, and they were to be completed by 2020. However, the U.S. DoE under the Trump administration delayed the proposed regulations. Nevertheless, in 2019, a federal judge upheld the Obama-era changes. States, districts, and schools were once again required to standardize how disproportionality was measured and examine locally occurring root causes of disproportionality.¹ The implementation and roll out remain complex, especially within the context of the ongoing global COVID-19 pandemic.

As research on the subject continues to unearth the complexity of the inequity and the policy landscape for monitoring disproportionality continues to evolve, it is clear that disproportionality remains a critical civil rights and equity concern in the field of special education. It is also evident that racial inequity in special education outcomes is a complex issue requiring that context be taken into account (e.g., Tefera & Fischman, 2020). Therefore, it is important for both researchers and policymakers to understand how LEAs experience citations, how often they are cited, and how often they are able to exit a citation status. With this knowledge, we can better understand the patterns associated with disproportionality citations, to inform future IDEA reauthorizations and remedies to address racial inequity in special education outcomes.

Method

For this study, we descriptively analyzed the IDEA disproportionality citation sequence patterns for all school districts in NYS using a restricted data set provided by the New York State Department of Education (NYSED), which documented districts that were cited across Indicators 4, 9, or 10 between 2004–2005 and 2011–2012 based on state policy described below (n = 638). We also relied upon National Center for Education Statistics (NCES) Urban-Centric Locale Codes to disaggregate the citation data by locale (U.S. Department of Education, n.d.). The NCES locale framework is composed of four basic types (City, Suburban, Town, and Rural) that each contains three subtypes and relies on standard urban and rural definitions developed by the U.S. Census Bureau (U.S. Department of Education, National Center for Education Statistics, Common Core of Data, n.d). These subtypes are differentiated by size (in the case of City and Suburban assignments) and proximity (in the case of Town and Rural assignments).

Among the 638 districts in the sample, 3.6% were designated as city, 33.7% as suburban, 6.9% as town, and 45.8% were designated as rural. Our sample reflects the proportions across NYS, with the exception of the city locale, as New York City school districts were not included in the data set due to their uniqueness. The median enrollment for city districts was 6,932 students, with a range of 40,114, and the median enrollment for suburban districts was 3,609 students, with a range of 26,023. For town districts, the median enrollment was 1,684 students with a range of 7,243, and finally, for rural districts, the median enrollment was 949 with a range of 9,947.

To examine the contextual variation across suburban locales during the study period, we merged publicly available yearly NYS Report Cards, which provided data on enrollment, demographics, attendance, suspension rates, and dropout rates, with the citation data. School-level data on enrollment were used to calculate the Dissimilarity Index, which corresponds to the extent to which two groups are evenly distributed across schools in a district. This index can be interpreted as the proportion of minoritized students that would have to change schools to be evenly distributed across the district where zero is no segregation and one is complete segregation. It is necessary to have at least two schools in a district to calculate segregation indices; therefore, we were unable to calculate this measure for districts with only one school, less than 3% of districts. Finally, district-level poverty estimates were obtained from the Small Area Income and Poverty Estimates (SAIPE) data set, made available through the U.S. Census Bureau.

We performed a sequence analysis to understand how variations in IDEA disproportionality citations manifested within and between suburban locales in NYS. The unit of analysis was school districts (N = 638). The event of interest in the sequence analysis was a citation for disproportionality under Indicators 4, 9, or 10 and the time variable was school year. We began the sequence analysis after the 2004 reauthorization of IDEA because the 2004 reauthorization had specific policy mandates that SEAs rapidly adopted during the time frame of the study. Albeit descriptive, a sequence analysis provides a unique methodological approach for understanding the policy implications of IDEA citations within a specific locales.

We conducted three descriptive analyses related to sequence patterns. First, we identified citation patterns for disproportionality across the state. Second, we examined citation trends and the frequency of these citations by suburban locale. Third, we analyzed the frequency of citations within suburban locales by specific designation for that locale (e.g., suburban—large vs. suburban—small). The results and significance of these analyses are described in the findings section.

Disproportionality in NYS

During the time frame of the study, from 2004 to 2012, racial disproportionality in NYS was primarily measured by a risk index or ratio and a relative risk ratio. NYS used a risk index to specify the rate at which students with a disability, per racial/ethnic group, experience a particular outcome such as a suspension, classification, or placement in special education within an LEA. A relative risk ratio was used to understand the relationship between groups of students within an LEA that experienced a particular outcome. The relative risk ratio identifies a specific racial/ethnic group’s risk of a particular outcome as compared with the risk of all other students in that LEA. The relative risk ratio is a useful measure because it does not vary with district or state composition or with identification rates in a state or district (Westat, 2005). Since the 2004 reauthorization of IDEA, most SEAs have transitioned to using the relative risk ratio for identifying disproportionality (Albrecht et al., 2012), further justifying NYS as a representative case for this analysis.

In addition, during the time frame of the study, NYS implemented a rigorous compliance review process when an LEA was cited under Indicators 4, 9, or 10. Table 1 outlines the general compliance review process in NYS and the average risk ratio per Indicator during the study time frame. It is important to note that during the time frame of the study, NYS made small adjustments to the way disproportionality was measured and how the IDEA compliance process unfolded. But, overall, the state education department retained a low threshold for identifying the inequity and a rigorous compliance review process for LEAs after they were cited.

Table 1.

New York State State Performance Plan (SPP) Indicators 4, 9, and 10 Compliance Review.

SPP Indicator	LEAs examine IDEA policy compliance related to:
4	● Use of positive behavioral supports
(Relative Risk Threshold = 2.0)	● Use of behavioral supports and interventions prior to suspensions with IEPs
	● Development and use of behavioral intervention plans (BIPs)
	● Evidence of manifestation determination (MD) hearings occurring prior to a suspension or expulsion
	● Evidence that proper interim alternative educational settings were provided when a student is suspended
9	● Schoolwide prereferral interventions
(Relative Risk Threshold = 2.5)	● Committee on Special Education (CSE) referral processes
	● Evaluation of practices for students with disabilities
	● Evaluation of LEA eligibility determinants for special education
10	● How students are evaluated for special education services
(Relative Risk Threshold 10a = 4.0)	● The content of student IEPs
(Relative Risk Threshold 10b = 2.0)	● How the CSE recommends students for classification and placement in special education

Note. IDEA = Individuals with Disabilities Education Act; LEA = local education agency; IEP = Individualized Education Program.

The table was compiled from technical assistance documents provided by the New York State Education Department at regional trainings for school districts cited for racial disproportionality, spanning across the 2012–2017 school years. Information in the table was also compiled from the following archived web pages:

Findings

Citation Sequence Patterns Across the State

More than a third (36%) of the 638 districts (n = 231) were cited at least once during the study period. Table 3 shows that the most frequent pattern among districts receiving any citation was a citation for 1 year only (n = 109). The second most common sequence pattern was the receipt of a citation for two consecutive years (n = 22). Still, 73 districts fluctuated on and off of citation during the study period. The sequence patterns described in Table 2 include an amalgam of all Indicators—4, 9, and 10.

Table 2.

Sequence Patterns for Citations Across New York State.

Sequence	Frequency (n)	%
Never cited (00000000)	407	63.79
Cited once (e.g., 01000000; 00000100)	109	17.08
Cited for two consecutive years (e.g., 00001100; 00000110)	22	3.45
Fluctuated on and off (e.g., 00001010; 00011010)	73	11.44
Other (e.g., 11111111; 00011111)	27	4.23
Total	638	100.00

Note. A “0” indicates no citation in a particular year, while a “1” indicates evidence of a citation.

The frequency in citation patterns may be related to the low threshold for identifying the inequity set by NYS. Furthermore, there are nearly as many once-cited districts (n = 109) as there are districts with fluctuating citation status during the time frame of the study (n = 122), indicating that it may be difficult for districts to effectively remedy the inequity. Because NYS did not increase its relative risk ratio and maintained a rigorous process for addressing IDEA noncompliance when LEAs were cited during the time frame of the study, the reemergence of a citation in districts over time suggests that the root causes of disproportionality may not have been actually addressed with each locale. The assertion aligns with research that indicates the sources and causes of disproportionality are extremely complex and multifaceted (e.g., Skiba et al., 2008; Voulgarides et al., 2021).

Given the complex mechanisms behind racially disproportionate outcomes in special education, it is reasonable to assume that IDEA compliance may be an insufficient mechanism for addressing the inequity. Qualitative research has supported evidence of this dynamic as symbolic acts of IDEA compliance do not actually address the root causes of disproportionality at the local level (Voulgarides, 2018). Pointing to a similar dynamic, Albrecht et al. (2012) found that while nationally LEAs were able to achieve IDEA compliance after being cited for either Indicator 9 or 10, their risk ratios still remained high. Albrecht and colleagues (2012) argue,

It seems likely then that SEAs, under pressure to comply with regulations mandating zero [IDEA] noncompliance in one year, will focus only on possible IDEA violations and ignore all other numerous possible inappropriate causes that could not be described as noncompliance with the IDEA (22).

The limitations of the SPP Indicators to reduce numerical disproportionality through IDEA compliance could be behind the fact that so many districts were cited and recited within the state during the study time frame.

A more nuanced look at citation sequence patterns across the state shows that Indicators 4A and 4B were the most frequent citations amongst the SPP Indicators. There were 126 instances of 4A and 115 of 4B between 2004 and 2011. Citations for Indicator 10B were less common during the study period (n = 35), but overall, Indicator 10 was also a high-frequency Indicator with a total of 146 citations over the time frame of the study. These trends parallel nationwide trends related to the high percentage of LEAs cited for Indicator 10 (see Albrecht et al., 2012) and the increasing number of LEAs experiencing a high number of students of color with a disability being suspended across the United States (U.S. GAO, 2018).

There was also an increase in cited districts in 2007, 2008, and 2010 in NYS. The increase in cited districts during the 2007 and 2008 school years may be related to the fact that IDEA measurement was refined the year prior. During the 2005–2006 school year, OSEP/Westat (2005) provided detailed recommendations to states on how disproportionality should be measured, and there was a general uptick in nationwide citations after these recommendations were released (see Albrecht et al., 2012). However, across NYS, there was a gradual decrease in cited districts from 2007 forward, which may be related to the SEA and LEAs becoming more accustomed to the disproportionality policy landscape. Educators may have found ways to manage the citation by developing symbolic forms of compliance that signaled compliance with IDEA mandates but did not actually address racial inequity (Voulgarides, 2018). Albrecht et al. (2012) noted that nationally fewer LEAs were cited over time.

Given the multiple problems that have been identified in the federally defined process of disproportionality identification, a plausible alternate hypothesis [to the reduction in LEAs being cited for disproportionality] is that the “numerical improvement” represented in the APR/SPP reports is not a reflection of real improvement in racial and ethnic disparities, but rather an indicator of the extent to which OSEP’s current system of monitoring and enforcement has failed to find continuing disproportionality in special education at the local level (22).

Albrecht et al. (2012) provide a poignant critique about how the current monitoring mechanisms, rigorously applied by NYS, may be misguided in both their approach and scope and offer a probable reason as to why districts can reduce their relative risk ratios without actually reducing discriminatory practices.

Variation by Context: Examining Citation Patterns by Suburban Locale

To ensure that aggregate statewide trends in citation patterns do not mask how locally occurring inequities arise, we refined the sequence analysis to consider suburban locales. The locale analysis is an extension of Voulgarides et al.’s (2013) study which used Event History Analysis (Tekle & Vermunt, 2012) to understand how the probability of a citation for disproportionality across placement, identification, and suspension through IDEA relates to district demographics. They found that a greater percentage of enrollment of students of color, higher district-level poverty rates, and a larger student body were associated with an increased likelihood of receiving a citation for disproportionality. The authors also found that suburban locales evidence the most unstable citation patterns for disproportionality over time as compared with rural, town, and urban locales. Although the occurrence of a citation was proportionally less likely in suburban school districts than city and town districts, the authors found that suburban districts were more likely than urban and rural districts to receive a repeated citation, and they also had the most active citation histories since the reauthorization of IDEA in 2004. According to our analysis, suburban districts in NYS, as compared with rural, town, and urban districts, on average, had the lowest statewide suspension rates and rates of poverty while also having the largest number of teachers with at least a master’s degree, and the lowest dropout rates. Given the unique characteristics of suburban locales, we believe it imperative to further examine citation patterns at a more granular level to better understand the nature of these findings.

Citation patterns by locale

In Table 3, we describe how citation patterns are manifested by locale. Table 3 indicates approximately half of suburban were cited during the study period. Table 3 also shows that suburban and city districts were the most likely to have active, fluctuating, and recurring citation patterns—another justification as to why we focus on suburban school districts in this article.

Table 3.

Sequence Patterns for Citations Across Each Locale.

Sequence	City	Suburb	Town	Rural
Sequence	n (% within locale)	n (% within locale)	n (% within locale)	n (% within locale)
Never cited (00000000)	3 (13.04%)	122 (56.74%)	62 (57.41%)	220 (75.34%)
Cited once (e.g., 01000000; 00000100)	4 (17.39%)	37 (17.21%)	23 (21.30%)	45 (15.41%)
Cited for two consecutive years (e.g., 00001100; 00000110)	1 (4.35%)	7 (3.26%)	5 (4.63%)	9 (3.08%)
Fluctuated on and off (e.g., 00001010; 00011010)	11 (47.83%)	35 (16.28%)	14 (12.96%)	14 (4.79%)
Other (e.g., 11111111; 00011111)	4 (17.39%)	14 (6.51%)	4 (3.70%)	4 (1.37%)
Total	23 (100%)	215 (100%)	108 (100%)	292 (100%)

We subsequently examined how demographic patterns relate to locale to investigate how diversity within a locale contributes to patterns of receipt of a citation. This is a critical point to focus on because, theoretically, all students—White, Black, Latinx, Native American, and forth—should have an equal probability of being placed, classified, or suspended. Studies have shown that demographic and community variables such as enrollment characteristics, educational resources, community variables, and other student outcomes, such as discipline patterns and dropout rates, relate to special education outcomes (e.g., Skiba et al., 2005; Sullivan & Bal, 2013). Studies have also shown that a range of school- and district-level factors influence a student’s likelihood of being placed in special education. Fish (2019) found that an increase in the population of White students in a school increases the risk of classification for students of color. As the proportion of White students increased within a school, students of color were at a higher risk of being classified with a lower status disability; however, the opposite was true for White students. Thus, a student’s race and the racial composition of a student’s school are related to special education classifications and it appears that the social context of a student’s school can either increase or decrease their risk of a disability classification.

Suburban locales

We found that suburban cited districts generally had greater proportions of students of color, as evidenced in Figure 1. Overall, the mean enrollment of Latinx students increased every year across suburban locales, consistent with national trends related to Latinx, but the Latinx population increased the most in cited districts, whereas the mean enrollment of White students slightly decreased across the locale throughout the study time frame. However, on average, White enrollment remained the largest among never cited districts. The proportion of Black students, although never exceeding 40% in NYS, is substantially greater in cited city and suburban district locales compared with noncited city and suburban locales, by nearly a difference of 20%. The findings provide initial evidence that Black enrollment in an LEA appears to increase the likelihood that a district will receive a citation.

Figure 1.

Variation in racial/ethnic demographics by year and citation status: Ever-cited versus never-cited.

The analysis indicates that the presence of students of color in a district appears to relate to a citation as districts with a large population of White students were less likely to be cited for disproportionality.

We also analyzed the frequency of citations within suburban locales to determine where the greatest variation and occurrence of disproportionality could be found. We found there is greater variation within suburban locales rather than between them. Table 4 describes the frequency of cited suburban districts in the study. We do not differentiate between citation type in this analysis because the sample sizes were too small, threatening our ability to protect district anonymity. However, future research using a national data set or additional states could remedy this issue.

Table 4.

Frequency of Never-Cited Versus Cited Within Suburban Locales Between 2004–2005 and 2010–2011.

Urban-centric locale from NCES	Total number of districts in locale designation in NYS	% of districts in locale designation within NYS	Number of districts never cited within the locale designation	Number of districts cited at least once within the locale designation	% cited within locale designation
Suburb	215	33.40	122	93	43.23
Large	195	30.56	107	88	45.13
Mid-size	11	1.72	8	3	27.27
Small	9	1.41	7	2	22.22

Note. NCES = National Center for Education Statistics; NYS = New York State.

Table 4 provides evidence that citations are relatively frequent in suburban—large districts. The data show that 45.1% of suburban—large districts were cited at least once, whereas 54.8% were never cited during the study time frame. Within this data set, there were 33 districts with fluctuating citation patterns, or repeated citations over time, and which comprise approximately 17% of the suburban—large districts. There were also 42 districts that had both multiple and consecutive citations within suburban—large districts locales.

The findings for suburban—large districts appear to be significant given that suburban—midsize and small districts have a much smaller frequency of citations. In suburban—midsize districts, with a sample size of 11, 72.7% were never cited and there were only two school districts with fluctuating citation patterns and no districts with consecutive citations. In suburban—small school districts, also with a sample size of 11, 77.8% of the districts were not cited (Table 3), there were no districts with fluctuating sequence patterns, and there was only one district with consecutive citations.

Using a dissimilarity index, which is one of the most commonly used measures of unevenness (Stroub & Richards, 2017), we also documented the extent to which two racial groups were evenly distributed within a locale. The index provides a measure for understanding what proportion of a particular student group in schools would have to change to be evenly distributed across a district. If the dissimilarity index is equal to zero, there is no segregation; however, if the dissimilarity index is one, there is complete segregation of two groups. Figure 2 indicates that the amount of segregation within suburban districts is large, but it does not vary substantially between cited and noncited districts.

Figure 2.

Variation in the Index of Dissimilarity by locale, year, and citation status: Ever-cited versus never-cited.

We hypothesize that the increasing enrollment of Latinx and Black students within suburban school districts in NYS increases the likelihood that these LEAs will receive a citation for disproportionality due to characteristics unique to these diversifying districts. Our hypothesis aligns with research that shows suburban school districts have been steadily diversifying (e.g., Frankenberg & Orfield, 2012) and that suburban districts near large cities, similar to those designated as suburban large in our study, have become increasingly residentially and racially segregated (Diem et al., 2014). In addition, there is a growing body of literature that links segregation to racial disproportionality (e.g., Eitle, 2002; White et al., 2019).

Discussion

In this article, we descriptively investigated IDEA SPP Indicator citation patterns within suburban locales within NYS. Between the 2004 and 2011 school years, NYS relied upon a rigorous approach for identifying and addressing disproportionality and, not surprisingly, the state experienced a significant number of citations. Indicator 4 and Indicator 10 were the most commonly experienced citations during the time frame of the study, which mirrors national patterns.

We found that suburban locales were the most likely to receive a citation and fluctuate on and off citation status during our study. The locale-specific analyses also indicated that whiteness, or proportion of the student population within an LEA that is White, appears to have a negative relationship with the likelihood of an LEA receiving a citation for racial disproportionality in NYS. Last, to assure that student demographics did not obscure variation within and between districts, we further examined how the amount of segregation within an LEA varied over the study time frame. We found that suburban locales appear to have high levels of segregation in both cited and noncited districts, but levels of segregation do not vary by cited versus noncited status.

Thus, by investigating SPP Indicator sequence patterns across specific-locale designations, we provide evidence that there is important variation across contexts, and this variation is likely obscured in analyses of national data. This is an important point because the current policy approach for addressing the inequity is not tailored toward locales. Below, we discuss several implications and recommendations for IDEA based on our descriptive analyses.

Implication 1: The locale type and racial composition of an LEA relates to the frequency in citations ever received. Thus, efforts to address disproportionality at the policy level must consider how contextual characteristics within locales and variations between locales relate to racial inequity in special education outcomes.

Recommendation 1: Future research should examine citation patterns across locales in across states. The comparative analysis would allow for greater generalizability of the findings. It would also provide useful data for lawmakers to consider when refining IDEA racial equity policy. Relatedly, SEAs should develop databases that link IDEA SPP Indicator citation data over time to sociodemographic characteristics. These databases could be analyzed within SEAs and at the federal level to provide context and insight into where IDEA remedies are working, or not, and why.

Implication 2: Individuals with Disabilities Education Act compliance should not be the primary mechanism used to address disproportionality. The root causes of the inequity are too complex and diverse. The policy approach should be amended not only to account for the diverse array of root causes that contribute to the issue, but also to how district factors and locale relate to patterns of racial inequity in special education.

Recommendation 2: The SPP Indicators are an important part of IDEA racial equity policy. They are an early warning sign of locally occurring inequities. Individuals with Disabilities Education Act compliance reviews should also remain a part of the policy approach. However, as proposed by the Obama administration in 2016, the SPP Indicator thresholds should be standardized across SEAs and a racial inequity “root cause” inquiry should be coupled with IDEA compliance reviews. The dual focus on IDEA compliance and root cause inquiry would allow for nuanced and contextually specific understandings of racial inequity in special education outcomes to emerge. It would also require LEAs to amend special education policies and procedures as they relate to broader patterns of locally occurring inequities and structural inequalities (see Fergus, 2016, for an example on how to do this).

Implication 3: Suburban—large districts need particular care and attention when crafting IDEA remedies to address disproportionality. These districts not only experience recurring and fluctuating citation patterns, but they also are characterized by significant racial and residential segregation, whether cited or not. Locale and racial and residential segregation must be accounted for in IDEA policy remedies designed to address disproportionality.

Recommendation 3: Individuals with Disabilities Education Act racial equity policy must consider how the size and location of a district, the resources available in various locales, historical and current patterns of racial segregation, patterns of sociodemographic change across locales, and so forth affect the likelihood of a district being cited, re-cited, or remaining cited for racial inequity in special education over time. Research should be funded and pursued that allows for comparative inquiry of citation patterns across SEAs and LEAs. The work would provide lawmakers with insight into the policy mechanisms that allow for locale-specific policy responses to be developed that do not impinge upon state autonomy.

Implication 4: The whiteness of a district’s student body relates to the frequency within which a district is cited, or not, for racial inequity in special education outcomes. The proportion of White students should not be a factor in whether or not racial inequity occurs. The trend needs to be addressed in policy to assure that (a) discriminatory practices are not occurring even without a formal citation, and (b) deficit views of students of color are leading to the increased likelihood of a citation, rather than actual need for special education services.

Recommendation 4: It is critical that IDEA racial equity work moves beyond technical policy mandates and IDEA compliance. The SEA and LEA representatives must work together to examine the assumptions and beliefs behind why patterns of racial equity manifest locally and across an SEA and use these insights to develop targeted policy approaches at the local level. Thus, federal IDEA racial equity policy should include a requirement that SEA and LEA representatives meet annually and work collaboratively to examine patterns of LEA root causes (Recommendation 2) and citation patterns over time (Recommendation 1). The collaborative work could be used to generate contextually appropriate policy remedies to address racial inequity in special education.

Limitations

To our knowledge, this is the first study to examine disproportionality in special education using sequence analysis. The descriptive relationships we have identified between and within locales cannot be assumed to be causal as the study was observational. In addition, the sequence analysis does not isolate the specific contextual characteristics that increase the likelihood of a citation. It is also limited in scope, as only one state’s citation patterns were examined, but hopefully, the results spur additional research using this unique methodological approach.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Catherine Kramarczuk Voulgarides

Notes

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