eqMI.covtest: Test the equality of two covariance matrices in population

Description Usage Arguments Details Value References Examples

View source: R/eqMI.covtest.R

Description

The first step of testing measurement invariance (MI) in multiple-group SEM analysis. The null hypothesis is tested using the method of Lagrange multipliers

Usage

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eqMI.covtest(..., lamb0 = NULL)

Arguments

...

The same arguments as for any lavaan model. See lavaan::sem for more information.

lamb0

initial coefficients of Lagrangian multiplier. If not pre-specified, 0.01 will be used.

Details

The eqMI.covtest function is the first step to test MI. Under null hypothesis testing (NHT), a non-significant statistic is generally an overall endorsement of MI. If the null hypothesis is rejected then one may proceed to test other aspects of MI.

Value

The likelihood ratio statistic, degrees of freedom, and p-value of the test.

References

Yuan, K. H., & Chan, W. (2016). Measurement invariance via multigroup SEM: Issues and solutions with chi-square-difference tests. Psychological methods, 21(3), 405-426.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.

Examples

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data(HolzingerSwineford)
semmodel<-'
L1 =~ V1 + V2 + V3
L2 =~ V4 + V5 + V6
L3 =~ V7 + V8
L4 =~ V9 + V10 + V11
'
cov.test <- eqMI.covtest(model = semmodel,
                         data = HolzingerSwineford,
                         group="school")

gabriellajg/equaltestMI documentation built on Feb. 22, 2018, 5:03 a.m.