Hello,
I am currently conducting a Multigroup Confirmatory Factor Analysis (MGCFA) with ordered data and have come across various materials online. However, I am seeking comprehensive guidelines on performing these analyses using lavaan and semTools.
My model is relatively simple:
MgCfa01 <- '
Fact01 =~ AmasS01 + AmasS02 + AmasS03 + AmasS04 + AmasS05 + AmasS06 + AmasS08 + AmasS09
'
ModMGCFA01 <- sem(model = MgCfa01, data = x,
std.lv = TRUE, group = "sex", ordered = c("AmasS01", "AmasS02", "AmasS03", "AmasS04", "AmasS05", "AmasS06", "AmasS07", "AmasS08", "AmasS09"))
ModMGCFA02 <- sem(model = MgCfa01, data = x,
std.lv = TRUE, group = "sex", group.equal = c("loadings"), ordered = c("AmasS01", "AmasS02", "AmasS03", "AmasS04", "AmasS05", "AmasS06", "AmasS07", "AmasS08", "AmasS09"))
ModMGCFA03 <- sem(model = MgCfa01, data = x,
std.lv = TRUE, group = "sex", group.equal = c("loadings", "thresholds"), ordered = c("AmasS01", "AmasS02", "AmasS03", "AmasS04", "AmasS05", "AmasS06", "AmasS07", "AmasS08", "AmasS09"))
I have a few questions that I hope you can assist me with:
- Does lavaan automatically select the appropriate estimator for ordered data? If not, which estimator would you recommend using?
- Can I only test for configural invariance, weak invariance (loadings), and strong invariance (loadings and thresholds) with ordered data?
- Which fit indices should be reported (scalar, robust)?
- Is there a built-in method for comparing two or more models, for example, using semTools?
- Which fit indices would you recommend reporting (e.g., CFI, TLI, RMSEA, and SRMR)?
- When comparing the Comparative Fit Index (CFI), for two models should I compare the robust or the scalar CFI?
- Are there any tutorials or guidelines online that you would recommend? I would be happy to cite any relevant tutorial if available.
- I had some missing pattern in one item and I decided to remove it, but I was wondering if there is any way to also include this item (error: lavaan->lav_samplestats_step1(): some categories of variable `AmasS07' are empty in group 1)
Thank you very much for any help you can provide.
David