No AIC/BIC for one-factor models?

[G.J. Melendez-Torres]

Here is a possibly stupid question I haven't been able to figure out.  When running the measurement invariance command on a one-factor model with a WLSMV estimator and five groups (because all indicators are ordinal), the resultant scaled chi-square difference test table doesn't generate an AIC or BIC value for any of the invariance models.  I could obviously use CFI and RMSEA for similar purposes but it would be helpful to have AIC/BIC values.  I'm not sure it matters but my code is below:

happy <- 'factor =~ VAR_1 + VAR_2 + VAR_3 + VAR_4 + VAR_5 + VAR_6 + VAR_7'

measurementInvariance(happy, data=master_data, estimator="WLSMV", strict=TRUE, group="grade",

                      ordered=c("VAR_1", "VAR_2", "VAR_3", "VAR_4", "VAR_5", "VAR_6", "VAR_7"))

Any help or explanation as to what kind of surely basic issue I'm missing here would be gratefully received.

[Steve Miller]

AIC and BIC are based on the likelihood function.   However, you're not using likelihood based estimation with WLSMV and therefore, these are not produced.

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[G.J. Melendez-Torres]

...this was my first thought yet the problem remains when I use the MLR (or even the ML) estimator.

[Terrence Jorgensen]

 yet the problem remains when I use the MLR (or even the ML) estimator.

Are you still declaring the same variables as ordered?  lavaan 0.6-3 returns an error when you request ML estimation for categorical outcomes.

HS9 <- HolzingerSwineford1939[,c("x1","x2","x3","x4","x5",
                                 "x6","x7","x8","x9")]
HSbinary <- as.data.frame( lapply(HS9, cut, 2, labels=FALSE) )
fit1 <- cfa(HS.model, data = HS9, ordered = paste0("x", 1:9), estimator = "mlr")

Terrence D. Jorgensen

Postdoctoral Researcher, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen

[G.J. Melendez-Torres]

Hi, Terrence--thanks for this.  No, I treat all manifest indicators as continuous when I run with ML, viz.:

happy <- 'factor =~ VAR_1 + VAR_2 + VAR_3 + VAR_4 + VAR_5 + VAR_6 + VAR_7'

measurementInvariance(happy, data=master_data, estimator="ML", strict=TRUE, group="grade")

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[Terrence Jorgensen]

 yet the problem remains when I use the MLR (or even the ML) estimator.

Well, using the latest versions of lavaan (0.6-3) and semTools (0.5-1), both AIC and BIC show up in the "Chi Square Difference Test" part of the output.  If you want to see them in the "Fit measures" part, you have to request them to override the default c("cfi","rmsea")

HW.model <- ' g =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 '
measurementInvariance(model = HW.model, data = HolzingerSwineford1939,
                      group = "school", fit.measures = c("aic","bic"))