Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART [0.03%]
嵌入式自适应干预在智能试验中的统计效率方法比较研究
Timothy Lycurgus,Amy Kilbourne,Daniel Almirall
Timothy Lycurgus
Sequential, multiple assignment randomized trials (SMARTs), which assist in the optimization of adaptive interventions, are growing in popularity in education and behavioral sciences. This is unsurprising, as adaptive interventions reflect ...
The Rank-2PL IRT Models for Forced-Choice Questionnaires: Maximum Marginal Likelihood Estimation with an EM Algorithm [0.03%]
强迫选择问卷的 rank-2PL 项目反应理论模型及其边际最大概似估计的EM算法
Jianbin Fu,Xuan Tan,Patrick C Kyllonen
Jianbin Fu
Mixed-Effects Location Scale Models for Joint Modeling School Value-Added Effects on the Mean and Variance of Student Achievement [0.03%]
均值和方差学校增值效应的混合效果位置尺度联合模型
George Leckie,Richard Parker,Harvey Goldstein et al.
George Leckie et al.
School value-added models are widely applied to study, monitor, and hold schools to account for school differences in student learning. The traditional model is a mixed-effects linear regression of student current achievement on student pri...
Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data [0.03%]
嵌套分类数据的贝叶斯多水平潜类别多重插补模型
Davide Vidotto,Jeroen K Vermunt,Katrijn van Deun
Davide Vidotto
With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of va...
Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level [0.03%]
数据项缺失情况下的正常理论两阶段极大似然估计量
Victoria Savalei,Mijke Rhemtulla
Victoria Savalei
In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use par...
Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models [0.03%]
Johnson-Neyman类型程序在广义线性(混合效应)模型中的显著性区域确定方法研究
Ann A Lazar,Gary O Zerbe
Ann A Lazar
Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the "significance region...
Sensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions [0.03%]
基于替代识别假设的因果效应的敏感性分析与界估计
Booil Jo,Amiram D Vinokur
Booil Jo
When identification of causal effects relies on untestable assumptions regarding nonidentified parameters, sensitivity of causal effect estimates is often questioned. For proper interpretation of causal effect estimates in this situation, d...
Bias Mechanisms in Intention-to-Treat Analysis With Data Subject to Treatment Noncompliance and Missing Outcomes [0.03%]
依从性不佳且结局数据缺失情况下的意向性分析偏差机制
Booil Jo
Booil Jo
An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of conside...
Uncertainty in Rank Estimation: Implications for Value-Added Modeling Accountability Systems [0.03%]
排名估计的不确定性对增值模型-accountability系统的影响
J R Lockwood,Thomas A Louis,Daniel F McCaffrey
J R Lockwood
Accountability for public education often requires estimating and ranking the quality of individual teachers or schools on the basis of student test scores. Although the properties of estimators of teacher-or-school effects are well establi...
Daniel F McCaffrey,J R Lockwood,Daniel Koretz et al.
Daniel F McCaffrey et al.
The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations on these models are being applied in the research literature, and policy makers ...