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It is important to understand how longitudinal patterns of special education placement differ from cross-sectional incidence estimates in order to improve measurement precision and better target assistance to students with disabilities. This study used latent class growth analysis in a national-level data set to classify four trajectories of special education service receipt from kindergarten to eighth grade (Never, Persistent, Delayed, and Discontinued) and to predict which kindergarteners follow these trajectories of service receipt ( N = 3,970). This study is among the first to identify which kindergarteners with disabilities may experience persistent special education services, which may exit special education, and what patterns of sociodemographic, achievement, and behavior covariates distinguish these groups. Results both align with prior work and offer a fresh perspective for researchers and policymakers as to how placement changes across schooling and for whom. 

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 imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications.