Go Beyond Compliance: Use Individualized Education Programs to Answer Strategic Questions and Improve Programs.

PURPOSE: The most significant document to ensure effective and compliant design, implementation, monitoring, and enforcement of a program of special education services in the United States is the Individualized Education Program (IEP). Although IEPs have been used to document procedural compliance with the Individuals with Disabilities Education Act (IDEA) for individual students, IEPs also provide extensive data that can and should be used by a variety of stakeholders including speech-language pathologists (SLPs), school administrators, and state education agencies to design targeted professional development and collectively improve programs, processes, and outcomes in special education. METHOD: We summarize existing literature on the use of IEP data and describe opportunities to use IEP data to analyze individual student service patterns and SLP practice patterns. Aggregated IEP data also provide a robust view of district-wide and state trends in eligibility rates and least restrictive environment settings. Information on current and potential IEP data uses, reflection questions for substantive compliance, and lessons learned from a large-scale analysis of IEP data are provided. These lessons include potential software adjustments to enhance usability as a data source for substantive compliance; program improvement; and monitoring individual, school-wide, and district-wide outcomes. CONCLUSIONS: IEP data are a rich data source of information that may be used to (a) identify trends; (b) assist schools, districts, and states with ensuring substantive compliance with IDEA; (c) examine service equity and efficacy; (d) identify professional development needs; and (e) identify promising practices and provide opportunities to use real-time data to improve models and address public policy.

Factors Predictive of Being Bullies or Victims of Bullies in US Elementary Schools.

We analyzed a population-representative cohort (N=13,611; Mage at kindergarten, first, and second grade = 67.5, 79.5, and 91.5 months, respectively) to identify kindergarten to second grade factors predictive of being bullies or victims during third to fifth grade. We did so by estimating a block recursive structural equation model (SEM) with three sets of predictors. These were: (a) individual and school socio-demographics; (b) family distress and harsh parenting; and (c) individual behavior and achievement. Relations between each of the included variables and the bullying outcomes were simultaneously estimated within the SEM. Thus, each variable served as a control for estimating the effects of the other variables. We used robust standard errors to account for student clustering within schools. Results indicated that externalizing problem behavior strongly predicted being a bully ([ES] = .56, p<.001) and a victim (ES=.29, p<.001). We observed a negative relation between being Hispanic and being a victim (ES = -.10, p<.001) and a positive relation between being Black and being a bully (ES = .11, p<.001). We also observed statistically significant relations between a family's socioeconomic status and being a bully (ES = -.08, p<.001) as well as school poverty and being a victim (ES = .07, p<.001). The results advance the field's limited understanding of risk and protective factors for bullying perpetration or victimization during elementary school and provide additional empirical support for assisting young children already exhibiting externalizing problem behaviors.

Longitudinal Trajectories of Reading and Mathematics Achievement for Students With Learning Disabilities.

We examined to what extent subgroups of students identified with learning disabilities (LDs; N = 630) in the Early Childhood Longitudinal Study, Kindergarten Class of 1998 to 1999 (ECLS-K): 1998 national longitudinal study displayed heterogeneity in longitudinal profiles of reading and mathematics achievement from first to eighth grades. Multivariate growth mixture modeling yielded four classes of combined reading and mathematics trajectories for students with LD. The largest class of students with LD (Class 2, 54.3%) showed mean T-scores for both achievement domains that averaged about 1 SD below the mean, with modest decline over time. Almost a quarter of the sample (Class 1, 22.3%) displayed mean T-scores in both achievement areas near the peer-normed average; these students were mostly White, from higher socioeconomic status (SES) backgrounds, and had experienced earlier identification as having an LD as well as shorter duration of LD service. Classifying heterogeneity in longitudinal trajectories of both achievement areas shows promise to better understand the educational needs of students identified with an LD.

Sociodemographic Disparities in Attention-Deficit/Hyperactivity Disorder Overdiagnosis and Overtreatment During Elementary School.

Attention-deficit/hyperactivity disorder (ADHD) overdiagnosis and overtreatment unnecessarily exposes children to potential harm and contributes to provider and community skepticism toward those with moderate or severe symptoms and significant impairments, resulting in less supportive care. Yet, which sociodemographic groups of children are overdiagnosed and overtreated for ADHD is poorly understood. We conducted descriptive and logistic regression analyses of a population-based subsample of 1,070 U.S. elementary schoolchildren who had displayed above-average levels of independently assessed behavioral, academic, or executive functioning the year prior to their initial ADHD diagnoses and who did not have prior diagnostic histories. Among these children, (a) 27% of White children versus 19% of non-White children were later diagnosed with ADHD and (b) 20% of White children versus 14% of non-White children were later using medication. In adjusted analyses, White children are more likely to later be diagnosed (odds ratio [OR] range = 1.70-2.62) and using medication (OR range = 1.70-2.37) among those whose prior behavioral, academic, and executive functioning suggested that they were unlikely to have ADHD.

Which Students With Disabilities are Placed Primarily Outside of U.S. Elementary School General Education Classrooms?

Students with disabilities (SWD) who are Black or Hispanic have been reported to be more likely to be placed primarily outside of general education classrooms while attending U.S. schools. Federal law and regulation require monitoring of special education placement based on race or ethnicity. Yet, whether and to what extent racial or ethnic disparities in placement are explained by bias or by other explanatory factors is currently unclear. We evaluated for racial and ethnic bias in special education placement by analyzing longitudinal data from two independent samples of SWD (N values range 590-1,130) attending U.S. elementary schools. We statistically controlled for plausibly exogenous sociodemographic, academic, and behavioral risk factors measured in kindergarten in analyses of the students as they attended first, third, and fifth grades between the 1999-2000 and 2015-2016 school years. Of the resulting 12 Black or Hispanic grade-year-specific tests, 11 (i.e., 92%) indicated that controls for kindergarten explanatory factors-particularly significant academic difficulties-fully explained the risk initially attributable to race or ethnicity. We observed little evidence that bias explains racial or ethnic disparities in special education placement in U.S. elementary schools.

Disparities in Family-Centered Care Among US Children and Youth with Special Healthcare Needs.

Cross-sectional analyses of 4 nationally representative samples indicate disparities in family-centered care occur among US children and youth with special healthcare needs by race and ethnicity, family income and composition, insurance coverage, and healthcare setting. Measured confounds including children's health and impairment severity do not explain the disparities.

Which Children are Frequently Victimized in U.S. Elementary Schools? Population-based Estimates.

We analyzed a population-based cohort of 11,780 U.S. kindergarten children to identify risk and protective factors predictive of frequent verbal, social, reputational, and/or physical bullying victimization during the upper elementary grades. We also stratified the analyses by biological sex. Both girls and boys displaying kindergarten externalizing problem behaviors were at consistently higher risk of frequent victimization during 3rd-5th grade (for the combined sample of boys and girls, verbal odds ratio [OR] = 1.82, social OR = 1.60, reputational OR = 1.85, physical OR = 1.67, total OR = 1.93). Hispanic children relative to non-Hispanic White children and those from higher income families were the most strongly and consistently protected from victimization. Boys were more likely to be physically bullied but less likely to be verbally, socially or reputationally bullied than girls. Other variables including disability, cognitively stimulating parenting, academic achievement, and internalizing behavior problems had statistically significant but less consistent and generally weaker relations with frequent victimization.

Risk and Protective Factors for Frequent Electronic Device Use of Online Technologies.

A sample of 10,460 U.S. elementary schoolchildren was analyzed to identify early predictors of frequent use of online technologies (i.e., messaging, online gaming, and social networking). Children (Mage  = 67.44 months) at greater risk displayed more externalizing problem behaviors in kindergarten (messaging OR = 1.11; online gaming OR = 1.21; social networking OR = 1.12) or were Black (messaging OR = 1.65; online gaming OR = 1.64; social networking OR = 1.68). Children from higher-income families were at lower risk (online gaming OR = 0.89; social networking OR = 0.89). Boys were more frequent users of online gaming (OR = 3.35) but less frequent users of messaging (OR = 0.62) and social networking (OR = 0.80). Protective factors included specific parenting behaviors.

How Stable Is Early Academic Performance? Using Cluster Analysis to Classify Low Achievement and EF.

We explored whether and how cognitive measures of executive function (EF) can be used to help classify academic performance in Kindergarten and first grade using nonparametric cluster analysis. We found that EF measures were useful in classifying low-reading performance in both grades, but mathematics performance could be grouped into low, average, and high groups without the use of EF tasks. Membership in the high-performing groups was more stable through first grade than membership in the low or average groups, and certain Kindergarten EF tasks differentially predicted first-grade reading and mathematics cluster membership. Our results suggest a stronger link between EF deficits and low performance than between EF strengths and high performance. We highlight the importance of simultaneously using academic and cognitive skills to classify achievement, particularly since existing classification schemes have been largely based on arbitrary cutoffs using limited academic measures.