plotRMSEApower {semTools}

Plot power curves for RMSEA

Description

Plots power of RMSEA over a range of sample sizes

Usage

plotRMSEApower(rmsea0, rmseaA, df, nlow, nhigh, steps = 1, alpha = 0.05,
  group = 1, ...)

Arguments

rmsea0

Null RMSEA

rmseaA

Alternative RMSEA

df

Model degrees of freedom

nlow

Lower sample size

nhigh

Upper sample size

steps

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

Alpha level used in power calculations

group

The number of group that is used to calculate RMSEA.

...

The additional arguments for the plot function.

Details

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/

Value

Plot of power for RMSEA against a range of sample sizes

Author(s)

Alexander M. Schoemann (East Carolina University; [e-mail])

Kristopher J. Preacher (Vanderbilt University; [e-mail])

Donna L. Coffman (Pennsylvania State University; [e-mail])

References

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. 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. 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. 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. 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. doi:10.3758/s13428-020-01479-0

See Also

• 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

Examples

plotRMSEApower(rmsea0 = .025, rmseaA = .075, df = 23,
               nlow = 100, nhigh = 500, steps = 10)