# eqMI.covtest: Test the equality of two covariance matrices in population In gabriellajg/equaltestMI: Measurement Invariance via Equivalence Testing and Projection Method

## 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

 `1` ```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

 ``` 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") ```

gabriellajg/equaltestMI documentation built on Oct. 2, 2018, 8:11 a.m.