Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the use of scoring missing data as incorrect. These methods were examined under the missing completely at random, missing at random, and missing not at random frameworks, as well as other testing conditions. Comparisons were made to baseline conditions where no missing data were present. The results showed that imputation and the full information maximum likelihood methods outperformed incorrect scoring methods in terms of average bias, average root mean square error, and correlation between estimated and true thetas.

Although the use of technology in the K12 classroom has been shown to have a positive impact, research on the use of open education resources (OER) is relatively limited, especially research focusing on low‐achieving students. The present study examines the relationship between usage of Algebra Nation, a self‐guided system that provided instructional videos and practice problems, and the performance of students who had failed the state‐administered Algebra I end‐of‐course (EOC) assessment the previous year. Indicators of usage of Algebra Nation consisted of logins, video views, and practice questions answered. Path analyses and logistic regressions were used to evaluate relationships between usage indicators and algebra scores, controlling for number of absences, free/reduced lunch eligibility, Hispanic/Latino origin, race, and gender. The results indicate that higher levels of logins, video views, and practice questions answered were related to higher scores when the students re‐took the assessment. Logins and practice questions were also related to increases in odds of passing the Algebra I EOC assessment, but not video views. The results suggest that there may be benefits to technology use in the form of an OER adopted by students and teachers on an informal basis and link self‐regulated learning strategies to student achievement.

No abstract

Background: Propensity score analysis (PSA) is a popular method to remove selection bias due to covariates in quasi-experimental designs, but it requires handling of missing data on covariates before propensity scores are estimated. Multiple imputation (MI) and single imputation (SI) are approaches to handle missing data in PSA. Objectives: The objectives of this study are to review MI-within, MI-across, and SI approaches to handle missing data on covariates prior to PSA, investigate the robustness of MI-across and SI with a Monte Carlo simulation study, and demonstrate the analysis of missing data and PSA with a step-by-step illustrative example. Research design: The Monte Carlo simulation study compared strategies to impute missing data in continuous and categorical covariates for estimation of propensity scores. Manipulated conditions included sample size, the number of covariates, the size of the treatment effect, missing data mechanism, and percentage of missing data. Imputation strategies included MI-across and SI by joint modeling or multivariate imputation by chained equations (MICE). Results: The results indicated that the MI-across method performed well, and SI also performed adequately with smaller percentages of missing data. The illustrative example demonstrated MI and SI, propensity score estimation, calculation of propensity score weights, covariate balance evaluation, estimation of the average treatment effect on the treated, and sensitivity analysis using data from the National Longitudinal Survey of Youth.