plotRMSEApower(rmsea0, rmseaA, df, nlow, nhigh, steps=1, alpha=.05, group=1, ...)
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
MacCallum, R. C., Lee, T., & Browne, M. W. (2010). The issue of isopower in power analysis for tests of structural equation models. Structural Equation Modeling, 17, 23-41.
Preacher, K. J., Cai, L., & MacCallum, R. C. (2007). Alternatives to traditional model comparison strategies for covariance structure models. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 33-62). Mahwah, NJ: Lawrence Erlbaum Associates.
Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling, 5, 411-419.
Steiger, J. H., & Lind, J. C. (1980, June). Statistically based tests for the number of factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.
plotRMSEAdist
to visualize the RMSEA distributions
findRMSEApower
to find the statistical power based on population RMSEA given a sample size
findRMSEAsamplesize
to find the minium sample size for a given statistical power based on population RMSEA
plotRMSEApower(.025, .075, 23, 100, 500, 10)
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