The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Testing Negative Error Variances: Is a Heywood Case a Symptom of Misspecification?
Abstract
Heywood cases, or negative variance estimates, are a common occurrence in factor analysis and latent variable structural equation models. Though they have several potential causes, structural misspecification is among the most important. This article explains how structural misspecification can lead to a Heywood case in the population, and provides several ways to test whether a negative error variance is a symptom of structural misspecification. The authors consider z-tests based on a variety of standard errors, confidence intervals, bootstrap resampling, and likelihood-ratio-type tests. In their discussion of z-tests, the authors demonstrate which of the standard errors are consistent under different kinds of misspecification. They also introduce new tests based on the scaled chi-square difference: the test on the boundary and the signed root of the scaled chi-square difference. A simulation study assesses the performance of these tests. The authors find that signed root tests and z-tests based on the empirical sandwich and the empirical bootstrap standard errors perform best in detecting negative error variances. They outperform z-tests based on the information matrix or distribution-robust standard errors.
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc-rcessc.v1i1.25 · Cited by 38,891
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology · doi:10.2307/2072165 · Cited by 17,442
Self-citationStructural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,277
Self-citationTesting Structural Equation Models.
Mueller, C. W., Bollen, K. A., & Long, J. S. (1994) · Contemporary Sociology · doi:10.2307/2074955 · Cited by 14,120
Self-citationA Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
Practical Methods of Optimization
Fletcher, R. (2000) · Wiley eBooks · doi:10.1002/9781118723203 · Cited by 7,605
Estimation and Inference in Econometrics.
Chambers, M. J., Davidson, R., & MacKinnon, J. G. (1994) · The Economic Journal · doi:10.2307/2234656 · Cited by 5,413
Cited by
Pendergast, L. L., von der Embse, N., Kilgus, S. P., & Eklund, K. R. (2017) · Journal of School Psychology · doi:10.1016/j.jsp.2016.11.002 · Cited by 229
Bayesian Meta-Analysis with Weakly Informative Prior Distributions
Williams, D. R., Rast, P., & Bürkner, P. C. (2018) · doi:10.31234/osf.io/7tbrm · Cited by 124
Judd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., van Noort, B., Penttilä, J., Grimmer, Y., Insensee, C., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001228117 · Cited by 105
Ozechowski, T. J. (2014) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0035889 · Cited by 42
Metadata
- Authors
- Kolenikov, S., & Bollen, K. A.
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 2012-02
- Volume
- 41
- Issue
- 1
- Pages
- 124-167
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- OpenAlex
- W1965984991
- Cited by
- 253
- References
- 97
- FWCI
- 13.13
- Type
- journal article
- Language
- en
- Open access
- closed
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