# findRMSEAsamplesize: Find the minimum sample size for a given statistical power... In semTools: Useful Tools for Structural Equation Modeling

## Description

Find the minimum sample size for a specified statistical power based on population RMSEA. This function can be applied for both test of close fit and test of not-close fit (MacCallum, Browne, & Suguwara, 1996)

## Usage

 `1` ```findRMSEAsamplesize(rmsea0, rmseaA, df, power = 0.8, alpha = 0.05, group = 1) ```

## Arguments

 `rmsea0` Null RMSEA `rmseaA` Alternative RMSEA `df` Model degrees of freedom `power` Desired statistical power to reject misspecified model (test of close fit) or retain good model (test of not-close fit) `alpha` Alpha level used in power calculations `group` The number of group that is used to calculate RMSEA.

## Details

This function find the minimum sample size for a specified power based on an iterative routine. The sample size keep increasing until the calculated power from `findRMSEApower` function is just over the specified power. If `group` is greater than 1, the resulting sample size is the sample size per group.

## 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

• `findRMSEApower` to find the statistical power based on population RMSEA given a sample size

## Examples

 `1` ```findRMSEAsamplesize(rmsea0 = .05, rmseaA = .08, df = 20, power = 0.80) ```

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