To examine whether special education racial risk ratios reported by U.S. school districts are explained by district-level confounds, particularly, racial achievement gaps, we analyzed merged data ( N = 1,952 districts for Black–White comparisons; N = 2,571 districts for Hispanic–White comparisons) from the U.S. Department of Education’s Office of Civil Rights, Stanford Educational Data Archive, and Common Core data sets. Regression analysis results indicated that Black– and Hispanic–White district risk ratios were strongly related to Black– and Hispanic–White district achievement gaps. These results reconcile findings from district-level data with those from student-level data and support the finding that, when compared to otherwise similar White students by controlling for group differences in achievement, non-White students are on average underrepresented in special education. That is, non-White overrepresentation in special education in most districts is explained by racial achievement gaps in these districts. Residuals from the regressions provide a more accurate way to monitor for outlier districts than the current practice required in federal regulations of using unadjusted risk ratios.

A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputationThe foundation for this paper was created during a 'hackathon' session occurring on 23 June 2021, at the annual virtual meeting of the Society for Improving Psychological Science. We invited anyone interested in the topic to attend, welcoming both experts and those with little experience addressing missing data in their research, specifically welcoming participation from those who were not sure how to address the missing data they experienced.Decisional guidelines for analyzing the type and extent of missing data were then crowdsourced and curated during this hackathon, resulting in a missing data and multiple imputation decision tree (Woods et al., 2021, available at https://doi.org/10.31234/osf.io/mdw5r) and a companion infographic (Woods & Schmidt, 2021, available at https://miro.com/app/board/o9_J18JGJQk=/). We also created multiple imputation coding templates for several prominent software languages (Stata, Mplus, R, SPSS, SAS and Blimp). All hackathon materials and coding templates are available at https://osf.io/j3f8m/.

Adequately addressing missing data is a pervasive issue in the social sciences. Failure to correctly address missing data can lead to biased or inefficient estimation of parameters, confidence intervals, and significance tests. Multiple imputation is a statistical technique for handling missing data that involves using existing data to generate multiple datasets of plausible values for missing data that each incorporate random components to reflect their uncertainty. Each dataset is analyzed individually and identically, and parameter estimates are pooled into one set of estimates, variances, and confidence intervals. Although this technique is widely used, there is little consensus on what constitutes best practices in multiple imputation, including with regard to assessing the extent of missing data bias and reporting multiple imputation procedures in publications. This decision tree was crowdsourced at the 2021 annual meeting of the Society for the Improvement of Psychological Science (SIPS) and revised thereafter. This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full imputation maximum likelihood (FIML).

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 classification as 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 classified LD.

Whether students of color are more or less likely to be identified as having disabilities than similarly situated students who are White in U.S. states with histories of de jure and de facto racial segregation is currently unknown. Unadjusted analyses of large samples of students attending elementary and middle schools in the U.S. South yielded little evidence of minority overrepresentation in special education. In analyses adjusted for strong confounds (e.g., family income, student-level achievement), students of color were less likely than White students to be identified as having disabilities. Underidentification was evident (a) for the U.S. South in aggregate, (b) across 11 Southern states that we separately examined, (c) in cross-sectional samples assessed in 2003 and 2015, and (d) for specific disability conditions. Black and Hispanic students attending schools in the U.S. South have been and continue to be less likely to be identified as having disabilities than otherwise similar White students.