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| Goodness of Fit | 2 |
| Structural Equation Models | 2 |
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| Structural Equation Modeling | 4 |
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| Nimmo, Melanie | 1 |
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Peer reviewedHayduk, Leslie; Cummings, Greta; Stratkotter, Rainer; Nimmo, Melanie; Grygoryev, Kostyantyn; Dosman, Donna; Gillespie, Michael; Pazderka-Robinson, Hannah; Boadu, Kwame – Structural Equation Modeling, 2003
Provides an introduction to the structural equation modeling concepts developed by J. Pearl, discussing the concept he calls "d-separation." Explains how d-separation connects to control variables, partial correlations, causal structuring, and even a potential mistake in regression. (SLD)
Descriptors: Causal Models, Correlation, Structural Equation Models, Theories
Peer reviewedWolfle, Lee M. – Structural Equation Modeling, 1999
Researchers interested in linear causal models discovered that a genetic biologist, Sewall Wright, had laid the groundwork for these models over nearly 50 years. This annotated bibliography covers Wright's development of the method of path coefficients. (Author/SLD)
Descriptors: Annotated Bibliographies, Biology, Causal Models, Genetics
Peer reviewedRobles, Jaime – Structural Equation Modeling, 1996
A theoretical and philosophical revision of the concept of fit in structural equation modeling and its relation to a confirmation bias is developed. The neutral character of fit indexes regarding this issue is argued, concluding that protection against confirmation bias relies on model modification strategy and scientist behavior. (SLD)
Descriptors: Causal Models, Goodness of Fit, Mathematical Models, Statistical Bias
Peer reviewedSivo, Stephen A.; Willson, Victor L. – Structural Equation Modeling, 2000
Studied whether moving average or autoregressive moving average models fit two longitudinal data sets previously thought to possess quasi-simplex structures better than the quasi-simplex, one-factor, or autoregressive models. Results of a Monte Carlo study show the importance of evaluating the fit, propriety, and parsimony of models before one…
Descriptors: Causal Models, Error of Measurement, Goodness of Fit, Longitudinal Studies
