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Su, Kun; Henson, Robert A. – Journal of Educational and Behavioral Statistics, 2023
This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of…
Descriptors: Classification, Models, Science Tests, Physics
Wang, Chun; Lu, Jing – Journal of Educational and Behavioral Statistics, 2021
In cognitive diagnostic assessment, multiple fine-grained attributes are measured simultaneously. Attribute hierarchies are considered important structural features of cognitive diagnostic models (CDMs) that provide useful information about the nature of attributes. Templin and Bradshaw first introduced a hierarchical diagnostic classification…
Descriptors: Cognitive Measurement, Models, Vertical Organization, Classification
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
Shear, Benjamin R.; Reardon, Sean F. – Journal of Educational and Behavioral Statistics, 2021
This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in…
Descriptors: Statistical Analysis, Computation, Regression (Statistics), Sample Size
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Suk, Youmi; Kim, Jee-Seon; Kang, Hyunseung – Journal of Educational and Behavioral Statistics, 2021
There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…
Descriptors: Artificial Intelligence, Statistical Analysis, Statistical Inference, Classification
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Moerbeek, Mirjam; Safarkhani, Maryam – Journal of Educational and Behavioral Statistics, 2018
Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…
Descriptors: Randomized Controlled Trials, Classification, Research Methodology, Military Personnel
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Han, Bing; Dalal, Siddhartha R.; McCaffrey, Daniel F. – Journal of Educational and Behavioral Statistics, 2012
There is widespread interest in using various statistical inference tools as a part of the evaluations for individual teachers and schools. Evaluation systems typically involve classifying hundreds or even thousands of teachers or schools according to their estimated performance. Many current evaluations are largely based on individual estimates…
Descriptors: Statistical Inference, Error of Measurement, Classification, Statistical Analysis
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Tomizawa, Sadao – Journal of Educational and Behavioral Statistics, 1995
An extended marginal homogeneity model is introduced for the analysis of square contingency tables with the same ordinal row and column classifications. The model hypothesizes that the ratio in each column of table delta will exhibit an exponential function of the column. (SLD)
Descriptors: Classification, Equations (Mathematics), Mathematical Models, Statistical Analysis