Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…

Background/Context: Observational studies often employ regression discontinuity (RD) designs and multiple control-group designs to explore the causal quantities of interest. RD designs assess policy and program effectiveness by assigning subjects to treatment based on whether they exceed a pre-defined cutoff. RD designs are classified into two…

With an educational issue that has caught the attention of many countries in the world (study load), a population of 8th graders from a typical Chinese metropolitan city (40,536 from 118 schools), and an advanced statistical strategy (multilevel piecewise regression), we examined whether there was a turning point in terms of the effects of study…

Background/Context: Some observational studies involve multiple layers of treatment selection, specifically in the context of the extended time accommodation (ETA) for English language learners (ELLs). In ETA settings, the first selection occurs due to the eligibility rule, where students whose ELL English proficiency is below a certain threshold…

This study explored 1634 Chinese eighth-grade students' domain- and task-specific self-efficacy and its relationship to their problem-posing performance. In particular, the linear regression model, generalized additive model (GAM), and piecewise regression model (PRM) were used to detail the linear and non-linear relationships between these…

Scenario-based writing assessment has two salient characteristics by design: a lead-in/essay scaffolding structure and a unified scenario/topic throughout. In this study, we examine whether the scenario-based assessment design would impact students' essay scores compared to its alternative conditions, which intentionally broke the scaffolding…

The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for…

Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…

This study examined the effect of similar vs. dissimilar proficiency distributions on uniform DIF detection on a statewide eighth grade mathematics assessment. Results from the similar- and dissimilar-ability reference groups with an SWD focal group were compared for four models: logistic regression, hierarchical generalized linear model (HGLM),…

In a South Carolina middle school, students were performing poorly on the state assessment in reading. Eighth grade students were not passing the Palmetto Assessment of State Standard (PASS) assessment administered each spring. The purpose of this study was to examine whether student performance on the Measure of Academic Progress (MAP) reading…

We report results of 2 studies examining the relation between decoding and reading comprehension. Based on our analysis of prominent reading theories such as the Simple View of Reading (Gough & Tunmer, 1986), the Lexical Quality Hypothesis (Perfetti & Hart, 2002) and the Self-Teaching Hypothesis (Share, 1995), we propose the Decoding…

Policies aiming to improve educational outcomes are typically based on academic testing data. However, such data only reflect the performance of students who completed the tests. It is possible that students who were absent have shared characteristics. The proportion of students absent out of all eligible school students and whether they have…

Background/Context: Regression discontinuity (RD) designs are used for policy and program evaluation where subjects' eligibility into a program or policy is determined by whether an assignment variable (i.e., running variable) exceeds a pre-defined cutoff. Under a standard RD design with a continuous assignment variable, the average treatment…

The purpose of this study was to investigate exam scores' predicting Transition from Primary to Secondary Education (TEOG) exam scores. The research data were obtained from the records of 1035 students studying at the first term of eighth grade in 2015-2016 academic year in e-school system. The research was on relational screening model. Linear…

This study examines the power of cognitive and noncognitive variables to predict students' performance in algebra. We investigated students' prior year's assessment scores and demographic characteristics to predict eighth-grade algebra scores. Using California statewide assessment data, we explored predictive factors in three regression models.…