CFA - Configural Model

Machine Learning and Modeling
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Hi! I did a three-wave study with repeated measured for each wave.I have two questions:
I am doing a CFA for a configural model (testing temporal invariance for each variable of my model at 3 points in time).

configural_model <- '
  # Time 1
  Bullying_1 =~ a1_bul_1 + a1_bul_2 + a1_bul_3 + a1_bul_4 + a1_bul_5 + a1_bult_6
  
  # Time 2
  Bullying_2 =~ a2_bul_1 + a2_bul_2 + a1_bul_3 + a2_bul_4 + a2_bul_5 + a2_bul_6
  
  # Time 3
  Bullying_3 =~ a3_bul_1 + a3_bul_2 + a3_bul_3 + a1_bul_4 + a3_bul_5 + a3_bul_6
'
# Fit the configural model
fit_configural <- cfa(configural_model, data = df, std.lv = TRUE, estimator = "MLR")

# Summarize the configural model fit
summary(fit_configural, fit.measures = TRUE, standardized = TRUE)

**

Questions

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  1. I am using Lavaan, and my data is non-normal (right skewed) and small (90 obs for each time).
    Should I use MLR or MLM estimator?
    I ask this because I am getting much better results with MLM, although I think MLR is more used.

  2. I have tried testing the configural model with several variables of my model, one at the time, and I always get crappy RMSEA values, even if I get good CFI, TLI or RSMR. I would understand if it was one variable, but with all of them I am finding this weird.

Can please someone help?
Thank you!

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