# findRMSEApower: Find the statistical power based on population RMSEA In semTools: Useful Tools for Structural Equation Modeling

## 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

 `1` ```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).

## Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

## 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. doi: 10.1037/1082-989X.1.2.130

• `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

 `1` ```findRMSEApower(rmsea0 = .05, rmseaA = .08, df = 20, n = 200) ```

semTools documentation built on Jan. 13, 2021, 8:09 p.m.