Relative Performance of Rescaling and Resampling Approaches to Model Chi Square and Parameter Standard Error Estimation in Structural Equation Modeling
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| Izdano u: | ERIC, Resources in Education (RIE) (Apr 1998), p. 1-39 |
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| Online pristup: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 62542435 | ||
| 003 | UK-CbPIL | ||
| 035 | |a 62542435 | ||
| 045 | 2 | |b d19980401 |b d19980430 | |
| 084 | |a ED420711 | ||
| 100 | 1 | |a Nevitt, Johnathan | |
| 245 | 1 | |a Relative Performance of Rescaling and Resampling Approaches to Model Chi Square and Parameter Standard Error Estimation in Structural Equation Modeling | |
| 260 | |c Apr 1998 | ||
| 513 | |a Report Speech/Lecture | ||
| 520 | 3 | |a Though common structural equation modeling (SEM) methods are predicated upon the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to use distribution-free estimation methods. Fortunately, promising alternatives are being integrated into popular software packages. For estimating model chi square values and parameter standard errors, EQS (P. Bentler, 1996) combats the effects of nonnormality by rescaling these statistics. AMOS (J. Arbuckle, 1997), on the other hand, offers bootstrap resampling approaches to accurate model chi square and standard error estimation. The current study is a Monte Carlo investigation of these two methods under varied conditions of nonnormality, sample size, and model misspecification. Accuracy of the chi square statistic is evaluated in terms of model rejection rates, while accuracy of standard error estimates takes the form of bias and variability of the estimates themselves. An appendix provides data for the paper's figures. (Contains 2 tables, 5 figures, and 31 references.) (SLD) | |
| 653 | |a Chi Square | ||
| 653 | |a Computer Software | ||
| 653 | |a Error of Measurement | ||
| 653 | |a Estimation (Mathematics) | ||
| 653 | |a Monte Carlo Methods | ||
| 653 | |a Research Methodology | ||
| 653 | |a Sample Size | ||
| 653 | |a Sampling | ||
| 653 | |a Scaling | ||
| 653 | |a Structural Equation Models | ||
| 700 | 1 | |a Hancock, Gregory R. | |
| 773 | 0 | |t ERIC, Resources in Education (RIE) |g (Apr 1998), p. 1-39 | |
| 786 | 0 | |d ProQuest |t ERIC | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/62542435/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://eric.ed.gov/?id=ED420711 |