NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Doran, Harold – Journal of Educational and Behavioral Statistics, 2023
This article is concerned with a subset of numerically stable and scalable algorithms useful to support computationally complex psychometric models in the era of machine learning and massive data. The subset selected here is a core set of numerical methods that should be familiar to computational psychometricians and considers whitening transforms…
Descriptors: Scaling, Algorithms, Psychometrics, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Leckie, George – Journal of Educational and Behavioral Statistics, 2018
The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…
Descriptors: Value Added Models, Reliability, Statistical Bias, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Raykov, Tenko; Marcoulides, George A.; Tong, Bing – Educational and Psychological Measurement, 2016
A latent variable modeling procedure is discussed that can be used to test if two or more homogeneous multicomponent instruments with distinct components are measuring the same underlying construct. The method is widely applicable in scale construction and development research and can also be of special interest in construct validation studies.…
Descriptors: Models, Statistical Analysis, Measurement Techniques, Factor Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Peugh, James L. – Journal of Early Adolescence, 2014
Applied early adolescent researchers often sample students (Level 1) from within classrooms (Level 2) that are nested within schools (Level 3), resulting in data that requires multilevel modeling analysis to avoid Type 1 errors. Although several articles have been published to assist researchers with analyzing sample data nested at two levels, few…
Descriptors: Early Adolescents, Research, Hierarchical Linear Modeling, Data Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Edwards, Robert V. – Chemical Engineering Education, 2010
An experiment is described that illustrates how chemical engineering correlations are created. Balls of different diameters and different specific gravities (all less than one) are dropped from several heights into a pool of water, and the maximum depth reached by the ball is measured. This data is used to estimate the coefficients for a…
Descriptors: Chemical Engineering, Laboratory Experiments, Science Experiments, Science Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Zhang, Guangjian; Browne, Michael W. – Psychometrika, 2007
The composite direct product (CDP) model is a multiplicative model for multitrait-multimethod (MTMM) designs. It is extended to incomplete MTMM correlation matrices where some trait-method combinations are not available. Rules for omitting trait-method combinations without resulting in an indeterminate model are also suggested. Maximum likelihood…
Descriptors: Multitrait Multimethod Techniques, Correlation, Computation, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Lui, Kung-Jong; Cumberland, William G. – Psychometrika, 2004
When the underlying responses are on an ordinal scale, gamma is one of the most frequently used indices to measure the strength of association between two ordered variables. However, except for a brief mention on the use of the traditional interval estimator based on Wald's statistic, discussion of interval estimation of the gamma is limited.…
Descriptors: Intervals, Sample Size, Maximum Likelihood Statistics, Monte Carlo Methods
Peer reviewed Peer reviewed
Direct linkDirect link
Vermunt, Jeroen K. – Multivariate Behavioral Research, 2005
A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent…
Descriptors: Predictor Variables, Correlation, Maximum Likelihood Statistics, Error of Measurement