Hey everyone, I’m stuck with a weird issue while doing confirmatory factor analysis using the lavaan package in R. I’ve set up my model (let’s call it model_x) with various factors and their indicators. It looks fine at first glance.
But when I try to run the CFA, I get this error:
Error in lavaan::lavaan(model = model_x, data = my_data, std.lv = TRUE, model.type = 'cfa'):
lavaan ERROR: missing observed variables in dataset: ..constant..
What’s throwing me off is this ‘…constant…’ thing. I can’t figure out what it means or where it’s coming from. My data seems okay, and I’ve double-checked all the variable names.
Has anyone run into this before? Any ideas on what might be causing it or how to fix it? I’d really appreciate some help here. Thanks!
yo exploringforest, that error is annoying. check ur data for any constant or near-constant variables. also, re-assess ur model syntax; extra intercepts might be the culprit. hope it helps, man!
Hey ExploringForest! 
That ‘…constant…’ error is definitely a head-scratcher! I’ve run into something similar before, and it can be super frustrating. Have you checked if you’re accidentally including any intercept terms in your model specification? Sometimes lavaan tries to add a constant term automatically, which might be causing this weird error.
Another thing to consider - are all your variables properly scaled? I once had a similar issue when one of my variables was essentially constant (very low variance), and lavaan threw a fit about it.
Just brainstorming here, but could you maybe share a snippet of your model specification? It might help spot any sneaky issues. Also, have you tried running a simpler version of your model to see if the error persists?
Keep us posted on what you find! CFA can be tricky, but I’m sure we can figure this out together. 
I have seen this ‘…constant…’ error come up before while running CFA in lavaan. It often happens because of issues with the model specification or when some variables in the dataset have very little variance or are almost perfectly collinear, causing the software to introduce a constant term unexpectedly.
I suggest rechecking that every variable used truly exists in your data and that none of them are inadvertently constant. Also, inspect your model syntax carefully to ensure no extra intercept terms are included by accident. Sometimes, using options such as auto.var = TRUE can help the software handle variables that may be near-constant.
Adjusting these aspects may resolve the error you’re encountering.