Description Usage Arguments Details Value References Examples
The first step of testing measurement invariance (MI) in multiple-group SEM analysis. The null hypothesis is tested using the method of Lagrange multipliers
1 | eqMI.covtest(..., lamb0 = NULL)
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... |
The same arguments as for any lavaan model. See |
lamb0 |
initial coefficients of Lagrange multiplier. If not pre-specified, 0.01 will be used. |
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.
The likelihood ratio statistic, degrees of freedom, and p-value of the test.
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/.
1 2 3 4 5 6 7 8 9 10 | 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")
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