The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Monte Carlo Experiments: Design and Implementation
Abstract
The use of Monte Carlo simulations for the empirical assessment of statistical estimators is becoming more common in structural equation modeling research. Yet, there is little guidance for the researcher interested in using the technique. In this article we illustrate both the design and implementation of Monte Carlo simulations. We present 9 steps in planning and performing a Monte Carlo analysis: (1) developing a theoretically derived research question of interest, (2) creating a valid model, (3) designing specific experimental conditions, (4) choosing values of population parameters, (5) choosing an appropriate software package, (6) executing the simulations, (7) file storage, (8) troubleshooting and verification, and (9) summarizing results. Throughout the article, we use as a running example a Monte Carlo simulation that we performed to illustrate many of the relevant points with concrete information and detail.
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Cites
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Curran, P. J., West, S. G., & Finch, J. F. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.16 · Cited by 5,512
Self-citationSimulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356 · cited 2×
“Although there are a few books on the technique (e.g., Mooney, 1997; Rubinstein, 1981; Smith, 1973), none directly relates the method to structural equation models.”
MethodA comparison of some methodologies for the factor analysis of non‐normal Likert variables
Muthén, B., & Kaplan, D. (1985) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1985.tb00832.x · Cited by 1,778
Cited by
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach
Kock, N. (2015) · International Journal of e-Collaboration · doi:10.4018/ijec.2015100101 · Cited by 10,215
Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009) · MIS Quarterly · doi:10.2307/20650284 · Cited by 4,944
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013) · Educational and Psychological Measurement · doi:10.1177/0013164413495237 · Cited by 3,931
Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014) · Organizational Research Methods · doi:10.1177/1094428114526928 · Cited by 3,332
Metadata
- Authors
- Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2001-04
- Volume
- 8
- Issue
- 2
- Pages
- 287-312
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2030637738
- Cited by
- 344
- References
- 4
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- closed
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