semTools (version 0.5-6)

findRMSEApower: Find the statistical power based on population RMSEA

Description

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)

Usage

findRMSEApower(rmsea0, rmseaA, df, n, alpha = 0.05, group = 1)

Arguments

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.

Details

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/.

References

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. 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. 10.3758/s13428-020-01479-0

See Also

• 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

Examples

# NOT RUN {
findRMSEApower(rmsea0 = .05, rmseaA = .08, df = 20, n = 200)

# }