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Wang, Yan; Kim, Eunsook; Joo, Seang-Hwane; Chun, Seokjoon; Alamri, Abeer; Lee, Philseok; Stark, Stephen – Journal of Experimental Education, 2022
Multilevel latent class analysis (MLCA) has been increasingly used to investigate unobserved population heterogeneity while taking into account data dependency. Nonparametric MLCA has gained much popularity due to the advantage of classifying both individuals and clusters into latent classes. This study demonstrated the need to relax the…
Descriptors: Nonparametric Statistics, Hierarchical Linear Modeling, Monte Carlo Methods, Simulation
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Delafontaine, Jolien; Chen, Changsheng; Park, Jung Yeon; Van den Noortgate, Wim – Large-scale Assessments in Education, 2022
In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or "Q-matrix") designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international…
Descriptors: Q Methodology, Matrices, Cognitive Measurement, Diagnostic Tests
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Dirlik, Ezgi Mor – International Journal of Progressive Education, 2019
Item response theory (IRT) has so many advantages than its precedent Classical Test Theory (CTT) such as non-changing item parameters, ability parameter estimations free from the items. However, in order to get these advantages, some assumptions should be met and they are; unidimensionality, normality and local independence. However, it is not…
Descriptors: Comparative Analysis, Nonparametric Statistics, Item Response Theory, Models
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Arenson, Ethan A.; Karabatsos, George – Grantee Submission, 2017
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Descriptors: Bayesian Statistics, Item Response Theory, Nonparametric Statistics, Models
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Si, Yajuan; Reiter, Jerome P. – Journal of Educational and Behavioral Statistics, 2013
In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian,…
Descriptors: Nonparametric Statistics, Bayesian Statistics, Measurement, Evaluation Methods
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Laswadi; Kusumah, Yaya S.; Darwis, Sutawanir; Afgani, Jarnawi D. – European Journal of Science and Mathematics Education, 2016
Conceptual understanding (CU) and procedural fluency (PF) are two important mathematical competencies required by students. CU helps students organizing their knowledge into a coherent whole, and PF helps them to find the right solution of a problem. In order to enhance CU and PF, students need learning experiences in constructing knowledge and…
Descriptors: Junior High School Students, Pretests Posttests, Control Groups, Mathematical Concepts
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De Witte, Kristof; Van Klaveren, Chris – Education Economics, 2014
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Descriptors: Nonparametric Statistics, Models, Teaching Styles, Individualized Instruction