This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…

Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…

We present new estimates of the importance of teachers in early grades for later grade outcomes, but unlike the existing literature that examines teacher "fade-out," we directly compare the contribution of early-grade teachers to later year outcomes against the contributions of later year teachers to the same later year outcomes. Where…

Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary "within" persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift…

Longitudinal models typically emphasize between-person predictors of change but ignore how growth varies "within" persons because each person contributes only one data point at each time. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally…

When researchers code behavior that is undetectable or falls outside of the validated ordinal scale, the resultant outcomes often suffer from informative missingness. Incorrect analysis of such data can lead to biased arguments around efficacy and effectiveness in the context of experimental and intervention research. Here, we detail a new…

Analyses that reveal how treatment effects vary allow researchers, practitioners, and policymakers to better understand the efficacy of educational interventions. In practice, however, standard statistical methods for addressing Heterogeneous Treatment Effects (HTE) fail to address the HTE that may exist within outcome measures. In this study, we…

Many states and school districts in the United States use standardized test scores to compute annual measures of student achievement progress and then use school-level averages of these growth measures for various reporting and diagnostic purposes. These aggregate growth measures can vary consequentially from year to year for the same school,…

Demands for scientific knowledge of what works in educational policy and practice has driven interest in quantitative investigations of educational outcomes, and randomized controlled trials (RCTs) have proliferated under these conditions. In educational settings, even when individuals are randomized, both experimental and control students are…

Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…

Value-added assessment methods have been criticized by researchers and policy makers for a number of reasons. One issue includes the sensitivity of model results across different outcome measures. This study examined the utility of incorporating multivariate latent variable approaches within a traditional value-added framework. We evaluated the…

Seventy-three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non-monotonic dependence of performance as a function of the distance between the beginning and the end-states of the problem, (b) very high levels of performance when the…

Research Findings: Quality early science education is important for addressing the low science achievement, compared to international peers, of elementary students in the United States. Teachers' beliefs about their skills in a content area, that is, their content self-efficacy is important because it has implications for teaching practice and…

The present study employed data simulation techniques to investigate the 1-year stability of alternative classification schemes for identifying children with reading disabilities. Classification schemes investigated include low performance, unexpected low performance, dual-discrepancy, and a rudimentary form of constellation model of reading…

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…