chisqSmallN  R Documentation 
Calculate smallN corrections for chi^2 modelfit test statistic to adjust for small sample size (relative to model size).
chisqSmallN(fit0, fit1 = NULL, smallN.method = if (is.null(fit1)) c("swain", "yuan.2015") else "yuan.2005", ..., omit.imps = c("no.conv", "no.se"))
fit0, fit1 
lavaan object(s) provided after running the

smallN.method 

... 
Additional arguments to the 
omit.imps 

Four finitesample adjustments to the chisquared statistic are currently available, all of which are described in Shi et al. (2018). These all assume normally distributed data, and may not work well with severely nonnormal data. Deng et al. (2018, section 4) review proposed smallN adjustments that do not assume normality, which rarely show promise, so they are not implemented here. This function currently will apply smallN adjustments to scaled test statistics with a warning that they do not perform well (Deng et al., 2018).
A list
of numeric
vectors: one for the originally
requested statistic(s), along with one per requested smallN.method
.
All include the the (un)adjusted test statistic, its df, and the
p value for the test under the null hypothesis that the model fits
perfectly (or that the 2 models have equivalent fit).
The adjusted chisquared statistic(s) also include(s) the scaling factor
for the smallN adjustment.
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Deng, L., Yang, M., & Marcoulides, K. M. (2018). Structural equation modeling with many variables: A systematic review of issues and developments. Frontiers in Psychology, 9, 580. doi: 10.3389/fpsyg.2018.00580
Shi, D., Lee, T., & Terry, R. A. (2018). Revisiting the model size effect in structural equation modeling. Structural Equation Modeling, 25(1), 21–40. doi: 10.1080/10705511.2017.1369088
HS.model < ' visual =~ x1 + b1*x2 + x3 textual =~ x4 + b2*x5 + x6 speed =~ x7 + b3*x8 + x9 ' fit1 < cfa(HS.model, data = HolzingerSwineford1939[1:50,]) ## test a single model (implicitly compared to a saturated model) chisqSmallN(fit1) ## fit a more constrained model fit0 < cfa(HS.model, data = HolzingerSwineford1939[1:50,], orthogonal = TRUE) ## compare 2 models chisqSmallN(fit1, fit0)
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