Descriptor
Source
| Structural Equation Modeling | 3 |
Author
| Dolan, Conor V. | 1 |
| Enders, Craig K. | 1 |
| Molenaar, Peter C. M. | 1 |
| Peugh, James L. | 1 |
| Steiger, James H. | 1 |
| Wicherts, Jelte M. | 1 |
Publication Type
| Journal Articles | 3 |
| Reports - Descriptive | 2 |
| Reports - Evaluative | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewedSteiger, James H. – Structural Equation Modeling, 2000
Discusses two criticisms raised by L. Hayduk and D. Glaser of the most commonly used point estimate of the Root Mean Square Error (RMSEA) and points out misconceptions in their discussion. Although there are apparent flaws in their arguments, the RMSEA is open to question for several other reasons. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Factor Analysis, Hypothesis Testing
Direct linkDolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M. – Structural Equation Modeling, 2004
We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…
Descriptors: Error of Measurement, Factor Analysis, Regression (Statistics), Evaluation Methods
Enders, Craig K.; Peugh, James L. – Structural Equation Modeling, 2004
Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…
Descriptors: Inferences, Structural Equation Models, Factor Analysis, Error of Measurement
