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It’s Complicated: Understanding Disproportionality

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Disproportionality first came into the public consciousness during the tail end of the civil rights movement in the 1960s (Cruz & Rodl, 2018; Morgan, et al., 2015; Skiba, Artiles, Kozleski, Losen, & Harry, 2016; Skiba, Poloni-Staudinger, Simmons, Renae Feggins-Azziz, & Chung, 2005; Sullivan & Bal, 2013). It is defined as the extent to which participation in certain groups such as gender, race/ethnicity, or socioeconomic status (SES), unjustly affect the likelihood of being labeled as having a disability and placed in special education (Cruz & Rodl, 2018). Because of the Individuals with Disabilities Act (IDEA), there are millions of school children with disabilities who receive Free Appropriate Education (FAPE) across the United States.

Disproportionality affects several groups including but not limited to ethnic/racial, cultural, and language minorities. Students and families may struggle with the acceptability of a disability due to cultural dissonance, or they may have a language barrier preventing them from requesting or accessing the services they need (Morgan et al., 2015). Since millions of children are receiving special education, this issue is important to understand. Not only does it affect students and their families, disproportionality shapes policy. Unfortunately, IDEA only provides funds for overrepresentation and not underrepresentation (Morgan et al., 2015). If overrepresentation is the issue, then there are children receiving services that don’t need it. If it is an issue of underrepresentation, then students are not getting the services they need. Examining the cause of disproportionality might bring greater understanding of the issue.

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Socioeconomic status (SES) and school discipline rates are two prominently studied causes for disproportionality. Some researchers feel SES is the more dominant cause while others feel school suspension rates have a larger impact in placing a child in special education. Morgan et al. (2015) observed overrepresentation occurs due to disadvantages brought on by poverty. Sullivan and Bal (2013) found students with high suspension rates were at greater risk of being identified for special education. Clearly, disproportionality is a multifaceted issue. Whatever the cause, a thorough review of the literature may clarify disproportionality.

Synthesis of the Literature

A further examining of SES and school discipline rates as two possible causes for disproportionality may shed some light on why it is so complex. Both have been studied for years by different researchers. One cause may be more dominant than the other. It is also likely there is no one main cause for disproportionality. The research has shown significant variability in different races and disability categories.

SES as the Cause of Disproportionality

SES is often controlled for when researchers study disproportionality. Free or reduced lunch (FRL) is how SES is determined in some research (Skiba et al., 2005; Sullivan & Bal, 2013). Skiba et al. (2005) found while poverty had a moderate correlation with their district’s rate of special education placement, there was no correlation between African American student enrollment and special education placement in their state of Indiana. Sullivan and Bal (2013) discovered African American students were overrepresented only in the specific learning disability (SLD) category while Latinos were underrepresented while using FRL to control for SES. They also found Asians/Pacific Islanders were underrepresented in special education overall.

Free or reduced lunch is not the only way to control for SES. Some studies have used the child poverty risk index or used individual estimates of poverty. Wiley, Bigham, Kauffman & Bogan (2013) used the child poverty risk index to control for SES. They found higher rates of child poverty had a correlation with African American and Latino students being identified for an emotional disability (ED) at lower rates. In Morgan et al.’s (2015) study, they used individual estimates of poverty and in doing so, noticed SES almost completely reduced race/ethnicity’s effect on disproportionality. ADD MORE

School Discipline Rates as the Cause of Disproportionality

Both Skiba et al. (2005) and Sullivan and Bal (2013) discovered the number of school discipline rates were consistent predictors of a student’s placement in special education. Skiba et al. (2005) think this relationship indicated schools’ inabilities to understand the behaviors of those with different cultures. They also posit districts that do not have as much money and more minority students are strapped for resources. Thus, they have a harder time dealing with those who have learning (LD) or behavioral disorders (BD). Rather than address this issue, schools place these students in special education or give them discipline referrals.

Sullivan and Bal’s (2013) research suggested students with higher number of suspensions were far more likely to have been referred for special education. They also found when controlling for suspension rates, African American students’ risk of special education placement was reduced. Students that had high numbers of suspensions were also at an increased risk for being identified for a specific learning disability (SLD), ED, or other health impairment (OHI). Sullivan and Bal (2013) suggested future research is needed to examine school discipline policies across different groups and contexts.

Critical Analysis of the Literature

While I believe disproportionality does exist, I do not think there is only one cause. The studies in this paper may believe either SES or school disciple rates have a larger effect on disproportionality, but most of them did find correlations with other perceived causes. Sullivan and Bal (2013) even state SES and suspensions were both consistent predictors of special education across all categories in their study. A closer examination of the literature reveals how complex disproportionality can be.

Analysis of SES as the Cause of Disproportionality

Skiba et al. (2005) stress poverty was not a good or reliable predictor of disproportionality across different disability categories, but school disciple rates were. This contradicts Sullivan and Bal’s findings (2013). I agree with Sullivan and Bal (2013) because Cruz and Rodl (2018) did a literature review showcasing several studies where SES did have an impact on special education placement. It is important to note Sullivan and Bal’s (2013) study took place in Wisconsin and Skiba et al. (2005) took place in Indiana (Cruz & Rodl, 2018). Skiba et al. (2016) mentioned disproportionality does vary by states, school district size, and disability categories. It is possible those results were unique to those states or even in those specific school districts. I believe contradictions like this show the variance in disproportionality. I think it is something often acknowledged but not examined deeply enough.

In contrast to Skiba et al. (2005) and Sullivan and Bal (2013), Morgan et al. (2015) and Wiley et al. (2013) did national studies. Morgan et al. (2015) found poverty to explain all variance for all categories. Skiba et al.’s (2016) rebuttal to Morgan et al. (2015) proclaimed they compared a model without a control variable to one with a control variable, including four levels of SES. The odds ratios indicated overrepresentation, especially for African Americans in the uncontrolled model. When the odds ratios were used in the controlled model, the results changed to underrepresentation. Skiba et al. (2016) further asserted few of the SES variables used in Morgan et al.’s (2015) study were significant and none went in predicted directions. I think the constant bickering on this subject is incredibly frustrating. You can nitpick at anyone’s research, that is why there is a limitations section in all research papers. It is literally an open acknowledgement of a study’s flaws.

Wiley et al. (2013) suggested poverty may cause underrepresentation for minority students diagnosed with ED. These results are similar to Morgan et al.’s (20015) results. However, both studies mainly focused on the African American and Latino racial categories. Wiley et al. (2013) only studied ED while Morgan et al. (2015) examined five different disability categories. It is important to note even though they have similar results, Morgan et al.’s data had strong representation of the ED and LD categories. Significant data in OHI and intellectual disabilities (ID) was found to be lacking (Skiba et al., 2016) The results of these studies could suggest there is a strong correlation of underrepresentation of Latinos and African Americans in the ED category, but they neglect to include other racial groups and do not have strong representation of other disability categories. I believe disproportionality is not an issue between just two races or a few disability categories. We need to examine all groups that could be affected by disproportionality, so we can determine where the resources for combating it should go. I don’t think it is fair so few groups and disability categories are constantly studied. Disproportionality can affect everyone.

Analysis of School Discipline Rates as the Cause of Disproportionality

Skiba et al. (2005) assumed schools that are poorer and have more minority students don’t have enough resources to handle children with LD or BD. Their study was published in 2005. IDEA was amended in 2004 to make sure equal access to education was provided for all. Local education agencies (LEA) are legally supposed to provide funds to children who are part of overrepresented groups (Morgan et al., 2015). If Skiba et al. (2005) felt funds were not being provided to schools in their district that needed assistance, then their LEA was breaking the law. I also feel Skiba et al. (2005) are being prejudiced in their thinking. Where I live, much of the area is considered lower income. Not once have I ever felt the schools with less money ever struggled to provided equal or fair representation in the classroom. Assuming poor schools can’t handle their students just adds to the problem. I feel this line of thinking contributes to disproportionality persisting.

Sullivan and Bal (2013) suggested more research is needed to fully understand if school discipline rates affect disproportionality. I agree because I tried searching for more articles that specifically studied this. I did find several research articles mentioning male African American students in special education were more likely to be suspended. But I had a hard time looking for articles about increased suspension and expulsion rates correlating with special education placement. The only ones I was able to access were the two used in this study. I feel this is an oversight in the field. Since there are so many articles about those in special education receiving higher school discipline rates, I believe it is likely there is correlation reflecting school discipline rates show higher rates of special education placement.

Use of Different Data Sets and Different Analysis Systems

When examining the literature’s research results, there is a lack of consistency with data sets. Some data sets were at a local level, using district data like Skiba et al. (2005) and Sullivan and Bal (2013). Others used national data sets such as Morgan et al. (2015) and Wiley et al. (2013). I feel national data sets like the ones used in Morgan et al.’s (2015) and Wiley et al.’s research could be used to examine disproportionality on a wider scale. If only looking at disproportionality on a local level, then schoolwide or district level data will suffice. Ideally, I feel one data set that accurately reflects all races and disability categories across the country would be the best.

The methods used to evaluate the data have also varied from study to study. Cruz and Rodl (2018) conducted a thorough literature review that indicated several different types of analysis systems from various research articles. Risk indices, risk ratios, odds ratios, and logistic regression models were a few of the analysis systems used in the studies for this paper. All the research articles in this literature review used different combinations of analysis systems. I think this might be able to explain the variability often see in the results of disproportionality studies. I think agreeing on a set of analysis systems would decrease the amount of contradictory research.

Disproportionality is an issue with several causes and variability. I think the next question to ask is “Is there a way to understand it better?”. I believe it is possible and I have suggestions. Following them could increase better understanding of the topic and help everyone to learn how much disproportionality can affect people in their everyday lives.

Practical Implications

Universal Framework

A suggestion made by Cuz and Rodl (2018) is having a common set of data and systems of analysis because it will be helpful in achieving cohesiveness when studies calculate their results. Many of the same researchers study disproportionality. I recommend they organize a conference and seriously discuss using a universal data set and agree on systems of analysis to use in future studies. I think it would be advantageous to come up with a completely new system of analysis. They should form breakout rooms at the conference and discuss what they want the analysis system to entail. I feel agreement in this area is necessary to stop the constant critiques other researchers have with each other regarding how they analyze their data.

More Qualitative and Longitudinal Studies

Some researchers have mentioned a need for more qualitative studies (Morgan et al., 2015). It would be interesting to see how disproportionality affects people on a more personal level. We often see numbers, rates, and percentages – ignoring there is a face behind those numbers. It is a good way to include more racial groups and disability categories. A quick search on EBSCOhost amongst all databases using the keywords “disproportionality”, “qualitative” and “special education” yielded less than ten results. This is an area in which further research can be beneficial. I think we need to get comprehensive studies to see if disproportionality truly does vary by school districts, states, races, and disability categories. I do not think we have the full scope and scale of disproportionality with the current research.

Another common suggestion amongst researchers is to conduct more longitudinal studies (Cruz & Rodl, 2018). The logistics of a longitudinal and qualitative study might be daunting, it will also take a long time, but it will give researchers a more individualized and closer look at how disproportionality affects people. They might come to find disproportionality does not affect people at all, or they might find causes they never discovered before. I think more longitudinal and qualitative studies could give researchers a new perspective.

Disproportionality is a hard issue to parse, but it is not impossible. Both SES and school discipline can affect it. Also, it is important to note disproportionality varies within racial groups, disability categories, school districts, and states. This could be due to the different data sets and systems of analysis used in the field. Having a universal framework in which all future research follows could produce more consistent results. It could also be worthwhile to see more longitudinal and qualitative studies to understand things on a national and personal level. These could be the useful in tackling the complexities inherent in disproportionality.


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