PA.RMSEA {GAIPE}

Sample size planning by power analysis on RMSEA

Description

Performs sample size planning by power analysis on RMSEA.

Usage

PA.RMSEA(df, method = c("exact.fit", "close.fit", "not.close.fit"),
H0rmsea, HArmsea, power = 0.8, alpha = 0.05)

Arguments

df

model degrees of freedom.

method

a character string specifying the hypothesis test for power analysis, must be one of "exact.fit", "close.fit", or "not.close.fit"(default).

H0rmsea

RMSEA for the null hypothesis.

HArmsea

RMSEA for the alternative hypothesis.

power

desired power value.

alpha

Type I error rate.

Value

Return the necessary sample size that achieves the desired power.

Author(s)

Tzu-Yao Lin

References

Hancock, G. R., & Freeman, M. J. (2001). Power and sample size for the root mean square error of approximation test of not close fit in structural equation modeling. Educational and Psychological Measurement, 61(5), 741-758. doi: 10.1177/00131640121971491

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

Examples

PA.RMSEA(df=30,method="not.close.fit",H0rmsea=.05,HArmsea=.02,power=.8,alpha=.05)

# Reproducing Table 8 in Hancock and Freeman (2001) #

# DF=c(seq(5,100,5),seq(110,200,10),225,250)
# POWER=c(seq(.5,.99,.05),.99)
# out=matrix(NA,length(DF),length(POWER))
# for(i in 1:length(DF)){
#   for(j in 1:length(POWER)){
#     out[i,j]=PA.RMSEA(df=DF[i],method="not.close.fit",
#     H0rmsea=.05,HArmsea=.02,power=POWER[j],alpha=.05)
#   }
# }
# colnames(out)=paste("Pi=",POWER,"",sep="")
# rownames(out)=paste("df=",DF,"",sep="")
# out