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Structural Equation Modeling | 11 |
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Brito, Carlos | 1 |
Dolan, Conor V. | 1 |
Dudgeon, Paul | 1 |
Green, Samuel B. | 1 |
Hancock, Gregory R. | 1 |
Hau, Kit-Tai | 1 |
Hershberger, Scott L. | 1 |
Hox, Joop | 1 |
Lensvelt-Mulders, Gerty | 1 |
Marsh, Herbert W. | 1 |
McDonald, Roderick P. | 1 |
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Journal Articles | 11 |
Reports - Descriptive | 11 |
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Higher Education | 2 |
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Brito, Carlos; Pearl, Judea – Structural Equation Modeling, 2002
Established a new criterion for the identification of recursive linear models in which some errors are correlated. Shows that identification is assured as long as error correlation does not exist between a cause and its direct effect; no restrictions are imposed on errors associated with indirect causes. (SLD)
Descriptors: Correlation, Error of Measurement, Structural Equation Models

Green, Samuel B.; Hershberger, Scott L. – Structural Equation Modeling, 2000
Proposes true score models that can account for correlated errors and their effect on coefficient alpha. These models allow random measurement errors on earlier items to affect directly or indirectly the scores on later items. Conditions under which coefficient alpha may yield spuriously high estimates or reliability are discussed. (SLD)
Descriptors: Correlation, Error of Measurement, Reliability, True Scores

Hancock, Gregory R.; Nevitt, Jonathan – Structural Equation Modeling, 1999
Explains why, when one is using a bootstrapping approach for generating empirical standard errors for parameters of interest, the researchers must choose to fix an indicator path rather than the latent variable variance for the empirical standard errors to be generated properly. (SLD)
Descriptors: Error of Measurement, Identification, Structural Equation Models
McDonald, Roderick P. – Structural Equation Modeling, 2004
Improper structures arising from the estimation of parameters in structural equation models (SEMs) are commonly an indication that the model is incorrectly specified. The use of boundary solutions cannot in general be recommended. Partly on the basis of theory given by Van Driel, and partly by example, suggestions are made for using the data as…
Descriptors: Structural Equation Models, Evaluation Methods, Error of Measurement, Evaluation Research
Raykov, Tenko – Structural Equation Modeling, 2004
A widely and readily applicable covariance structure modeling approach is outlined that allows point and interval estimation of scale reliability with fixed components. The procedure employs only linear constraints introduced in a congeneric model, which after reparameterization permit expression of composite reliability as a function of…
Descriptors: Measures (Individuals), Intervals, Error of Measurement, Structural Equation Models

Steiger, 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
Wang, Jichuan – Structural Equation Modeling, 2004
In addition to assessing the rate of change in outcome measures, it may be useful to test the significance of outcome changes during specific time periods within an entire observation period under study. While discussing the delta method and bootstrapping, this study demonstrates how to use these 2 methods to estimate the standard errors of the…
Descriptors: Longitudinal Studies, Error of Measurement, Measures (Individuals), Comparative Analysis
Dolan, 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
Hox, Joop; Lensvelt-Mulders, Gerty – Structural Equation Modeling, 2004
This article describes a technique to analyze randomized response data using available structural equation modeling (SEM) software. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. The basic feature of all randomized response methods is that the data are deliberately…
Descriptors: Structural Equation Models, Item Response Theory, Evaluation Research, Evaluation Methods
Dudgeon, Paul – Structural Equation Modeling, 2004
This article considers the implications for other noncentrality parameter-based statistics from Steiger's (1998) multiple sample adjustment to the root mean square error of approximation (RMSEA) measure. When a structural equation model is fitted simultaneously in more than 1 sample, it is shown that the calculation of the noncentrality parameter…
Descriptors: Statistical Analysis, Monte Carlo Methods, Structural Equation Models, Error of Measurement
Marsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin – Structural Equation Modeling, 2004
Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…
Descriptors: Statistical Significance, Structural Equation Models, Evaluation Methods, Evaluation Research