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Shi, Dingjing; Tong, Xin – Sociological Methods & Research, 2022
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…
Descriptors: Bayesian Statistics, Monte Carlo Methods, Computer Software, Causal Models
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
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…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Pavel Chernyavskiy; Traci S. Kutaka; Carson Keeter; Julie Sarama; Douglas Clements – Grantee Submission, 2024
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…
Descriptors: Bayesian Statistics, Mathematics Instruction, Learning Trajectories, Item Response Theory
Lockwood, J. R.; Castellano, Katherine E.; McCaffrey, Daniel F. – Journal of Educational and Behavioral Statistics, 2022
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,…
Descriptors: Accuracy, Prediction, Programming Languages, Standardized Tests
Sundararajan, NarayanKripa; Adesope, Olusola; Cavagnetto, Andy – Journal of STEM Education: Innovations and Research, 2017
To develop and nurture critical thinking, students must have opportunities to observe and practice critical thinking in the classroom. In this parallel mixed method classroom study, we investigate the role of collaborative concept mapping in the development of kindergarten learners' critical thinking skills of analysis and interpretation over a…
Descriptors: Kindergarten, Concept Mapping, Thinking Skills, Critical Thinking
Kaplan, David; Chen, Jianshen – Society for Research on Educational Effectiveness, 2013
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…
Descriptors: Bayesian Statistics, Models, Probability, Monte Carlo Methods
O'Connor, Erin E.; McCormick, Meghan P.; Cappella, Elise; McClowry, Sandee G. – Society for Research on Educational Effectiveness, 2014
Not all children begin kindergarten ready to learn. Young children who exhibit dysregulated or disruptive behavior in the classroom have fewer opportunities to learn and consequently achieve lower levels of academic skills (Arnold et al., 2006; Raver, Garner, & Smith-Donald, 2007). A growing body of literature has examined how children's…
Descriptors: Young Children, Behavior Problems, Student Behavior, At Risk Students