Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 6 |
Descriptor
Computation | 6 |
Structural Equation Models | 6 |
Error of Measurement | 3 |
Goodness of Fit | 3 |
Bayesian Statistics | 2 |
Calculus | 2 |
Comparative Analysis | 2 |
Equations (Mathematics) | 2 |
Factor Analysis | 2 |
Models | 2 |
Problems | 2 |
More ▼ |
Author
Cai, Li | 1 |
Edwards, Michael C. | 1 |
Ferrer, Emilio | 1 |
Folmer, Henk | 1 |
MacCallum, Robert C. | 1 |
Mooijaart, Ab | 1 |
Oud, Johan H. L. | 1 |
Pan, Tianshu | 1 |
Rindskopf, David | 1 |
Satorra, Albert | 1 |
Steele, Joel S. | 1 |
More ▼ |
Publication Type
Journal Articles | 6 |
Opinion Papers | 6 |
Education Level
Higher Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Mooijaart, Ab; Satorra, Albert – Psychometrika, 2012
Starting with Kenny and Judd ("Psychol. Bull." 96:201-210, 1984) several methods have been introduced for analyzing models with interaction terms. In all these methods more information from the data than just means and covariances is required. In this paper we also use more than just first- and second-order moments; however, we are aiming to…
Descriptors: Structural Equation Models, Computation, Goodness of Fit, Statistical Analysis
Rindskopf, David – Psychological Methods, 2012
Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Computation
MacCallum, Robert C.; Edwards, Michael C.; Cai, Li – Psychological Methods, 2012
Muthen and Asparouhov (2012) have proposed and demonstrated an approach to model specification and estimation in structural equation modeling (SEM) using Bayesian methods. Their contribution builds on previous work in this area by (a) focusing on the translation of conventional SEM models into a Bayesian framework wherein parameters fixed at zero…
Descriptors: Structural Equation Models, Bayesian Statistics, Computation, Expertise
Oud, Johan H. L.; Folmer, Henk – Multivariate Behavioral Research, 2011
This article addresses modeling oscillation in continuous time. It criticizes Steele and Ferrer's article "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011), particularly the approximate estimation procedure applied. This procedure is the latent version of the local linear approximation procedure…
Descriptors: Structural Equation Models, Computation, Calculus, Simulation
Pan, Tianshu; Yin, Yue – Psychological Methods, 2012
In the discussion of mean square difference (MSD) and standard error of measurement (SEM), Barchard (2012) concluded that the MSD between 2 sets of test scores is greater than 2(SEM)[superscript 2] and SEM underestimates the score difference between 2 tests when the 2 tests are not parallel. This conclusion has limitations for 2 reasons. First,…
Descriptors: Error of Measurement, Geometric Concepts, Tests, Structural Equation Models
Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
This article presents our response to Oud and Folmer's "Modeling Oscillation, Approximately or Exactly?" (2011), which criticizes aspects of our article, "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011). In this response, we present a conceptual explanation of the derivative-based estimation…
Descriptors: Calculus, Responses, Simulation, Models