Thank you for your guidance, and my apologies for not being clear about the issue I was facing. I did not understand why my model was dropping many records when I included several dichotomous variables that had missing data. I incorrectly believed that fiml would handle missing data for dichotomous variables, but as I've learned from various readings this is not the case. Following your advice, I spent some time looking at multiple imputation procedures, referencing various exchanges in this google group. By using Amelia and identifying certain variables as dichotomous, I now have multiple imputed data sets to analyze. My models are running without losing any records. I am now struggling with getting model fit statistics based on the analysis using imputed data sets. Any references you can recommend would be much appreciated.
I've run the following and get a long message along with a few chi-square related statistics. I cannot seem to retrieve RMSEA, SRMR, TFI, CLI or any other model fit statistics besides the ones below after multiple tries based on what I've found online.
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust correction can only be applied to pooled chi-squared statistic, not F statistic. "asymptotic" was switched to TRUE.
Robust corrections are made by pooling the naive chi-squared statistic across 100 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
chisq df pvalue
155.546 206.000 0.996
npar ntotal chisq.scaled
28.000 1062.000 217.717
df.scaled pvalue.scaled chisq.scaling.factor
206.000 0.274 1.537
Here's the rest of the R code I used to get the above results:
RR.model7 <- '
i =~ 1*RR10 + 1*RR11 + 1*RR12 + 1*RR13 + 1*RR14 + 1*RR15 + 1*RR16 + 1*RR17 + 1*RR18
s =~ 0*RR10 + 1*RR11 + 2*RR12 + 3*RR13 + 4*RR14 + 5*RR15 + 6*RR16 + 7*RR17 + 8*RR18
s2 =~ 0*RR10 + 2*RR11 + 4*RR12 + 9*RR13 + 16*RR14 + 25*RR15 + 36*RR16 + 49*RR17 + 64*RR18
i ~ Public + Size + FT + Female + AA + LAT + SR
#time varying covariates
# residual variances
RRdata.amelia <- amelia(RR.data, m = 100, p2s = FALSE, ords = c(17:29))
RR.data.amelia.imps <- RRdata.amelia$imputations
RR.data.amelia.impsRR.fit7 <- growth.mi (RR.model7, data = RR.data.amelia.imps, ordered=c(17:29))
Thank you once again for your guidance!