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Steffen Erickson – Society for Research on Educational Effectiveness, 2024
Background: Structural Equation Modeling (SEM) is a powerful and broadly utilized statistical framework. Researchers employ these models to dissect relationships into direct, indirect, and total effects (Bollen, 1989). These models unpack the "black box" issues within cause-and-effect studies by examining the underlying theoretical…
Descriptors: Structural Equation Models, Causal Models, Research Methodology, Error of Measurement
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Fan, Weihua; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2012
This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…
Descriptors: Robustness (Statistics), Hypothesis Testing, Monte Carlo Methods, Simulation
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Price, Larry R. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Descriptors: Sample Size, Time, Bayesian Statistics, Structural Equation Models
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Raykov, Tenko; Penev, Spiridon – Structural Equation Modeling: A Multidisciplinary Journal, 2010
A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…
Descriptors: Reliability, Evaluation, Models, Intervals
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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
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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
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Neale, Michael C.; And Others – Multivariate Behavioral Research, 1994
In studies of relatives, conventional multiple regression may not be appropriate because observations are not independent. Obtaining estimates of regression coefficients and correct standard errors from these populations through a structural equation modeling framework is discussed and illustrated with data from twins. (SLD)
Descriptors: Analysis of Covariance, Causal Models, Data Collection, Error of Measurement