Plots power of RMSEA over a range of sample sizes
plotRMSEApower(rmsea0, rmseaA, df, nlow, nhigh, steps = 1, alpha = 0.05,
group = 1, ...)Plot of power for RMSEA against a range of sample sizes
Null RMSEA
Alternative RMSEA
Model degrees of freedom
Lower sample size
Upper sample size
Increase in sample size for each iteration. Smaller values of steps will lead to more precise plots. However, smaller step sizes means a longer run time.
Alpha level used in power calculations
The number of group that is used to calculate RMSEA.
The additional arguments for the plot function.
Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu)
Kristopher J. Preacher (Vanderbilt University; kris.preacher@vanderbilt.edu)
Donna L. Coffman (Pennsylvania State University; dlc30@psu.edu)
This function creates plot of power for RMSEA against a range of sample sizes. The plot places sample size on the horizontal axis and power on the vertical axis. The user should indicate the lower and upper values for sample size and the sample size between each estimate ("step size") We strongly urge the user to read the sources below (see References) before proceeding. A web version of this function is available at: http://quantpsy.org/rmsea/rmseaplot.htm. This function is also implemented in the web application "power4SEM": https://sjak.shinyapps.io/power4SEM/
MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11(1), 19--35. tools:::Rd_expr_doi("10.1037/1082-989X.11.1.19")
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130--149. tools:::Rd_expr_doi("10.1037/1082-989X.1.2.130")
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(1), 23--41. tools:::Rd_expr_doi("10.1080/10705510903438906")
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(4), 411--419. tools:::Rd_expr_doi("10.1080/10705519809540115")
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.
Jak, S., Jorgensen, T. D., Verdam, M. G., Oort, F. J., & Elffers, L. (2021). Analytical power calculations for structural equation modeling: A tutorial and Shiny app. Behavior Research Methods, 53, 1385--1406. tools:::Rd_expr_doi("10.3758/s13428-020-01479-0")
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(rmsea0 = .025, rmseaA = .075, df = 23,
nlow = 100, nhigh = 500, steps = 10)
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