View source: R/powerAnalysisRMSEA.R
findRMSEApower | R Documentation |
Find the proportion of the samples from the sampling distribution of RMSEA in the alternative hypothesis rejected by the cutoff dervied from the sampling distribution of RMSEA in the null hypothesis. This function can be applied for both test of close fit and test of not-close fit (MacCallum, Browne, & Suguwara, 1996)
findRMSEApower(rmsea0, rmseaA, df, n, alpha = 0.05, group = 1)
rmsea0 |
Null RMSEA |
rmseaA |
Alternative RMSEA |
df |
Model degrees of freedom |
n |
Sample size of a dataset |
alpha |
Alpha level used in power calculations |
group |
The number of group that is used to calculate RMSEA. |
This function find the proportion of sampling distribution derived from the
alternative RMSEA that is in the critical region derived from the sampling
distribution of the null RMSEA. If rmseaA
is greater than
rmsea0
, the test of close fit is used and the critical region is in
the right hand side of the null sampling distribution. On the other hand, if
rmseaA
is less than rmsea0
, the test of not-close fit is used
and the critical region is in the left hand side of the null sampling
distribution (MacCallum, Browne, & Suguwara, 1996).
There is also a Shiny app called "power4SEM" that provides a graphical user interface for this functionality (Jak et al., in press). It can be accessed at https://sjak.shinyapps.io/power4SEM/.
Sunthud Pornprasertmanit (psunthud@gmail.com)
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. doi: 10.1037/1082-989X.1.2.130
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. doi: 10.3758/s13428-020-01479-0
plotRMSEApower
to plot the statistical power based on
population RMSEA given the sample size
plotRMSEAdist
to visualize the RMSEA distributions
findRMSEAsamplesize
to find the minium sample size for
a given statistical power based on population RMSEA
findRMSEApower(rmsea0 = .05, rmseaA = .08, df = 20, n = 200)
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