Publication Date
In 2025 | 1 |
Since 2024 | 8 |
Descriptor
Source
Author
Ahmed Haddadi | 1 |
Alexander von Eye | 1 |
Christine DiStefano | 1 |
Chuanguo Ma | 1 |
Hiroto Namihira | 1 |
Jacob W. Wainman | 1 |
James Ohisei Uanhoro | 1 |
Jia Wu | 1 |
Jon-Paul Paolino | 1 |
Karen Nylund-Gibson | 1 |
Lisa L. Walsh | 1 |
More ▼ |
Publication Type
Reports - Descriptive | 8 |
Journal Articles | 7 |
Books | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Elementary Education | 1 |
Elementary Secondary Education | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Tenko Raykov; Ahmed Haddadi; Christine DiStefano; Mohammed Alqabbaa – Educational and Psychological Measurement, 2025
This note is concerned with the study of temporal development in several indices reflecting clustering effects in multilevel designs that are frequently utilized in educational and behavioral research. A latent variable method-based approach is outlined, which can be used to point and interval estimate the growth or decline in important functions…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Educational Research, Statistical Inference
Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
James Ohisei Uanhoro – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean…
Descriptors: Bayesian Statistics, Structural Equation Models, Correlation, Monte Carlo Methods
Alexander von Eye; Wolfgang Wiedermann – Merrill-Palmer Quarterly: A Peer Relations Journal, 2024
In this article, we pursue two points of discussion. First, a new illustration is presented of the person-oriented tenet according to which it can be hazardous to generalize to the individual results that are based on the analysis of aggregated data. Second, it is illustrated that taking into account serial dependence information can result in not…
Descriptors: Research Methodology, Generalizability Theory, Generalization, Multivariate Analysis
Jon-Paul Paolino – Teaching Statistics: An International Journal for Teachers, 2024
This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging…
Descriptors: Statistics Education, Factor Analysis, Teaching Methods, Introductory Courses
Tara Slominski; Oluwatobi O. Odeleye; Jacob W. Wainman; Lisa L. Walsh; Karen Nylund-Gibson; Marsha Ing – CBE - Life Sciences Education, 2024
Mixture modeling is a latent variable (i.e., a variable that cannot be measured directly) approach to quantitatively represent unobserved subpopulations within an overall population. It includes a range of cross-sectional (such as latent class [LCA] or latent profile analysis) and longitudinal (such as latent transition analysis) analyses and is…
Descriptors: Educational Research, Multivariate Analysis, Research Methodology, Hierarchical Linear Modeling
Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
Hiroto Namihira – IGI Global, 2024
Academic scholars face a difficult challenge when attempting to grasp the intricate world of mathematics. The complexity of mathematical concepts often lies hidden beneath layers of formulas and procedures, obscuring their true essence. Traditional educational resources often fall short in conveying the profound meaning behind these concepts,…
Descriptors: Information Technology, Visual Aids, Mathematics Education, Technology Uses in Education