semTools (version 0.5-3)

nullRMSEA: Calculate the RMSEA of the null model

Description

Calculate the RMSEA of the null (baseline) model

Usage

nullRMSEA(object, scaled = FALSE, silent = FALSE)

Arguments

object

The lavaan model object provided after running the cfa, sem, growth, or lavaan functions.

scaled

If TRUE, the scaled (or robust, if available) RMSEA is returned. Ignored if a robust test statistic was not requested.

silent

If TRUE, do not print anything on the screen.

Value

A value of RMSEA of the null model (a numeric vector) returned invisibly.

Details

RMSEA of the null model is calculated similar to the formula provided in the lavaan package. The standard formula of RMSEA is

$$ RMSEA =\sqrt{\frac{\chi^2}{N \times df} - \frac{1}{N}} \times \sqrt{G} $$

where \(\chi^2\) is the chi-square test statistic value of the target model, \(N\) is the total sample size, \(df\) is the degree of freedom of the hypothesized model, \(G\) is the number of groups. Kenny proposed in his website that

"A reasonable rule of thumb is to examine the RMSEA for the null model and make sure that is no smaller than 0.158. An RMSEA for the model of 0.05 and a TLI of .90, implies that the RMSEA of the null model is 0.158. If the RMSEA for the null model is less than 0.158, an incremental measure of fit may not be that informative."

See also http://davidakenny.net/cm/fit.htm

References

Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods Research, 44(3), 486--507. doi:10.1177/0049124114543236

See Also

• miPowerFit For the modification indices and their power approach for model fit evaluation

• moreFitIndices For other fit indices

Examples

# NOT RUN {
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data = HolzingerSwineford1939)
nullRMSEA(fit)

# }