Last updated: 2019-10-30

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Purpose of this file:

  1. Summarize convergence
  2. Summarize proper solution rates
  3. Creating simple descriptive statistics across conditions
  4. Generating tables of those stats

The output is mostly just a lot of latex ready tables. Not all of these tables are included in the final publication, but we wanted to be as precise as possible with respect to the summary of the fit statistics.

Packages and Set-Up

##Chunk iptions
knitr::opts_chunk$set(out.width = "225%")

#setwd('C:/Users/noahp/Dropbox/MCFA Thesis/Code Results')

## Packages
## General Packages
library(tidyverse)
-- Attaching packages --------------------------------------------------------- tidyverse 1.2.1 --
v ggplot2 3.2.0     v purrr   0.3.2
v tibble  2.1.1     v dplyr   0.8.1
v tidyr   0.8.3     v stringr 1.4.0
v readr   1.3.1     v forcats 0.4.0
-- Conflicts ------------------------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
# Formatting and Tables
library(kableExtra)

Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':

    group_rows
library(xtable)
# For plotting
library(ggplot2)
theme_set(theme_bw())
# Data manipulating
library(dplyr)

Data Management

sim_results <- as_tibble(read.table('data/compiled_fit_results.txt', header=T,sep='\t'))

## Next, turn condition into a factor for plotting
sim_results$Condition <- as.factor(sim_results$Condition)

## Next, since TLI is non-normed, any value greater than 1 needs to be rescaled to 1.
sim_results$TLI <- ifelse(sim_results$TLI > 1, 1, sim_results$TLI)
sim_results$TLI <- ifelse(sim_results$TLI < 0, 0, sim_results$TLI)
## Next, summarize the results of the chi-square test of model fit. This is done simply by comparing the p-value to alpha (0.05) and indicating whether the model was flagged as fitting or not.
# Note: if  p < 0.05 then this variable is flagged as 0, and 1 otherwise
sim_results$Chi2_pvalue_decision <- ifelse(sim_results$chisqu_pvalue < 0.05, 0, 1)
# 0 = rejected that these data fit this model
# 1 = failed to reject that these data fit this model

Adding Labels to Conditions

Currently, each condition is kind of like a hidden id that we don’t know what the actual factor is. So, first thing isto create meaningful labels for us to use. Remember, the 72 conditions for the this study were

  1. Level-1 sample size (ss_l1 or n1) (5, 10, 30)
  2. Level-2 sample size (ss_l2 or n2) (30, 50, 100, 200)
  3. Observed indicator ICC (.1, .3, .5)
  4. Latent variable ICC (.1, .5)
## level-1 Sample size
ss_l1 <- c(5, 10, 30) ## 6 conditions each
ss_l2 <- c(30, 50, 100, 200) ## 18 condition each
icc_ov <- c(.1, .3, .5) ## 2 conditions each
icc_lv <- c(.1, .5) ## every other condition
nCon <- 72 # number of conditions
nRep <- 500 # number of replications per condition
nMod <- 12 ## numberof estimated models per conditions
## Total number of rows: 432,000
ss_l2 <- c(rep(ss_l2[1], 18*nRep*nMod), rep(ss_l2[2], 18*nRep*nMod), 
           rep(ss_l2[3], 18*nRep*nMod), rep(ss_l2[4], 18*nRep*nMod))
ss_l1 <- rep(c(rep(ss_l1[1],6*nRep*nMod), rep(ss_l1[2],6*nRep*nMod), rep(ss_l1[3],6*nRep*nMod)), 4)
icc_ov <- rep(c(rep(icc_ov[1], 2*nRep*nMod), rep(icc_ov[2], 2*nRep*nMod), rep(icc_ov[3], 2*nRep*nMod)), 12)
icc_lv <- rep(c(rep(icc_lv[1], nRep*nMod), rep(icc_lv[2], nRep*nMod)), 36)
## Force these vectors to be column vectors
ss_l1 <- matrix(ss_l1, ncol=1)
ss_l2 <- matrix(ss_l2, ncol=1)
icc_ov <- matrix(icc_ov, ncol=1)
icc_lv <- matrix(icc_lv, ncol=1)
## Add the labels to the results data frame
sim_results <- sim_results[order(sim_results$Condition),]
sim_results <- cbind(sim_results, ss_l1, ss_l2, icc_ov, icc_lv)

## Force the conditions to be factors
sim_results$ss_l1 <- as.factor(sim_results$ss_l1)
sim_results$ss_l2 <- as.factor(sim_results$ss_l2)
sim_results$icc_ov <- as.factor(sim_results$icc_ov)
sim_results$icc_lv <- as.factor(sim_results$icc_lv)
sim_results$Model <- factor(sim_results$Model, levels = c('C','M1','M2','M12'), ordered = T)
## Set up iterators for remainder of script
mods <- c('C', 'M1', 'M2', 'M12')
ests <- c('MLR', 'ULSMV', 'WLSMV')

For the descriptive statistics, I will use dplyr. From here I can easily create matrices that store the results so that I can easily print out the results for summarizing the results. Each will be printed out as a html table and a xtable (latex ready) table.

Convergence Summary

Convergence will be broken out by Model (C, M1, M2, M12) and estimator (MLR, WLSMV, ULSMV). So, there will 12 smallish tables piecemail tables. Next, one very large table of all the conditions will be exported in latex ready format.

Convergence by Model and Estimator

## first table summary table
c <- sim_results %>%
  group_by(Model, Estimator) %>%
  summarise(Converge=mean(Converge))
# Next make the columns the estimator factor
c <- cbind(c[ c$Model == 'C', 'Converge'], 
           c[ c$Model == 'M1', 'Converge'], 
           c[ c$Model == 'M2', 'Converge'], 
           c[ c$Model == 'M12', 'Converge'])
rownames(c) <- c('MLR', 'ULSMV', 'WLSMV')
colnames(c) <- c('C' ,'M1' ,'M2', 'M12')
## Print results in a nice looking table in HTML
kable(c, format='html') %>%
    kable_styling(full_width = T)%>%
  add_header_above(c(' '= 1, 'Model Specification'=4))
Model Specification
C M1 M2 M12
MLR 0.9998056 0.9819444 0.9998056 0.9997778
ULSMV 0.9989722 0.9737500 0.9882500 0.9853056
WLSMV 0.9997778 0.9645000 0.9997500 0.9919444 
## Print out in tex
print(xtable(c, digits = 3), booktabs = T, include.rownames = T)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:47 2019
\begin{table}[ht]
\centering
\begin{tabular}{rrrrr}
  \toprule
 & C & M1 & M2 & M12 \\ 
  \midrule
MLR & 1.000 & 0.982 & 1.000 & 1.000 \\ 
  ULSMV & 0.999 & 0.974 & 0.988 & 0.985 \\ 
  WLSMV & 1.000 & 0.965 & 1.000 & 0.992 \\ 
   \bottomrule
\end{tabular}
\end{table}

Model, Estimator, and Sample Sizes

## first table summary table
c <- sim_results %>%
  group_by(Model, Estimator, ss_l1, ss_l2) %>%
  summarise(Converge=mean(Converge))
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Model == 'C', c('Estimator', 'ss_l1', 'ss_l2', 'Converge')], 
           c[ c$Model == 'M1', 'Converge'], 
           c[ c$Model == 'M2', 'Converge'], 
           c[ c$Model == 'M12', 'Converge'])
colnames(c1) <- c('Estimator', 'SS Level-1', 'SS Level-2', 'C' ,'M1' ,'M2', 'M12')

## Print results in a nice looking table in HTML
kable(c1, format='html') %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 3, 'Model Specification'=4))
Model Specification
Estimator SS Level-1 SS Level-2 C M1 M2 M12
MLR 5 30 1.0000000 0.9820000 0.9996667 0.9996667
MLR 5 50 1.0000000 0.9860000 1.0000000 1.0000000
MLR 5 100 1.0000000 0.9820000 1.0000000 1.0000000
MLR 5 200 0.9996667 0.9860000 1.0000000 1.0000000
MLR 10 30 1.0000000 0.9816667 1.0000000 0.9993333
MLR 10 50 1.0000000 0.9783333 0.9996667 1.0000000
MLR 10 100 1.0000000 0.9800000 1.0000000 1.0000000
MLR 10 200 0.9996667 0.9873333 1.0000000 1.0000000
MLR 30 30 1.0000000 0.9713333 1.0000000 0.9990000
MLR 30 50 0.9996667 0.9766667 0.9993333 0.9993333
MLR 30 100 0.9993333 0.9846667 0.9996667 1.0000000
MLR 30 200 0.9993333 0.9873333 0.9993333 1.0000000
ULSMV 5 30 0.9896667 0.9283333 0.9576667 0.9436667
ULSMV 5 50 0.9996667 0.9576667 0.9820000 0.9723333
ULSMV 5 100 1.0000000 0.9743333 0.9926667 0.9896667
ULSMV 5 200 1.0000000 0.9846667 0.9993333 0.9993333
ULSMV 10 30 0.9990000 0.9603333 0.9756667 0.9700000
ULSMV 10 50 0.9996667 0.9686667 0.9883333 0.9883333
ULSMV 10 100 1.0000000 0.9826667 0.9956667 0.9946667
ULSMV 10 200 1.0000000 0.9880000 0.9996667 0.9996667
ULSMV 30 30 0.9996667 0.9740000 0.9806667 0.9806667
ULSMV 30 50 1.0000000 0.9840000 0.9920000 0.9893333
ULSMV 30 100 1.0000000 0.9896667 0.9953333 0.9963333
ULSMV 30 200 1.0000000 0.9926667 1.0000000 0.9996667
WLSMV 5 30 0.9976667 0.9036667 0.9973333 0.9513333
WLSMV 5 50 1.0000000 0.9413333 1.0000000 0.9786667
WLSMV 5 100 1.0000000 0.9706667 1.0000000 0.9983333
WLSMV 5 200 1.0000000 0.9806667 1.0000000 1.0000000
WLSMV 10 30 1.0000000 0.9446667 1.0000000 0.9830000
WLSMV 10 50 0.9996667 0.9626667 0.9996667 0.9983333
WLSMV 10 100 1.0000000 0.9746667 1.0000000 1.0000000
WLSMV 10 200 1.0000000 0.9860000 1.0000000 1.0000000
WLSMV 30 30 1.0000000 0.9653333 1.0000000 0.9936667
WLSMV 30 50 1.0000000 0.9750000 1.0000000 1.0000000
WLSMV 30 100 1.0000000 0.9846667 1.0000000 1.0000000
WLSMV 30 200 1.0000000 0.9846667 1.0000000 1.0000000 
## Print out in tex
print(xtable(c1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:47 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllrrrr}
  \toprule
Estimator & SS Level-1 & SS Level-2 & C & M1 & M2 & M12 \\ 
  \midrule
MLR & 5 & 30 & 1.000 & 0.982 & 1.000 & 1.000 \\ 
  MLR & 5 & 50 & 1.000 & 0.986 & 1.000 & 1.000 \\ 
  MLR & 5 & 100 & 1.000 & 0.982 & 1.000 & 1.000 \\ 
  MLR & 5 & 200 & 1.000 & 0.986 & 1.000 & 1.000 \\ 
  MLR & 10 & 30 & 1.000 & 0.982 & 1.000 & 0.999 \\ 
  MLR & 10 & 50 & 1.000 & 0.978 & 1.000 & 1.000 \\ 
  MLR & 10 & 100 & 1.000 & 0.980 & 1.000 & 1.000 \\ 
  MLR & 10 & 200 & 1.000 & 0.987 & 1.000 & 1.000 \\ 
  MLR & 30 & 30 & 1.000 & 0.971 & 1.000 & 0.999 \\ 
  MLR & 30 & 50 & 1.000 & 0.977 & 0.999 & 0.999 \\ 
  MLR & 30 & 100 & 0.999 & 0.985 & 1.000 & 1.000 \\ 
  MLR & 30 & 200 & 0.999 & 0.987 & 0.999 & 1.000 \\ 
  ULSMV & 5 & 30 & 0.990 & 0.928 & 0.958 & 0.944 \\ 
  ULSMV & 5 & 50 & 1.000 & 0.958 & 0.982 & 0.972 \\ 
  ULSMV & 5 & 100 & 1.000 & 0.974 & 0.993 & 0.990 \\ 
  ULSMV & 5 & 200 & 1.000 & 0.985 & 0.999 & 0.999 \\ 
  ULSMV & 10 & 30 & 0.999 & 0.960 & 0.976 & 0.970 \\ 
  ULSMV & 10 & 50 & 1.000 & 0.969 & 0.988 & 0.988 \\ 
  ULSMV & 10 & 100 & 1.000 & 0.983 & 0.996 & 0.995 \\ 
  ULSMV & 10 & 200 & 1.000 & 0.988 & 1.000 & 1.000 \\ 
  ULSMV & 30 & 30 & 1.000 & 0.974 & 0.981 & 0.981 \\ 
  ULSMV & 30 & 50 & 1.000 & 0.984 & 0.992 & 0.989 \\ 
  ULSMV & 30 & 100 & 1.000 & 0.990 & 0.995 & 0.996 \\ 
  ULSMV & 30 & 200 & 1.000 & 0.993 & 1.000 & 1.000 \\ 
  WLSMV & 5 & 30 & 0.998 & 0.904 & 0.997 & 0.951 \\ 
  WLSMV & 5 & 50 & 1.000 & 0.941 & 1.000 & 0.979 \\ 
  WLSMV & 5 & 100 & 1.000 & 0.971 & 1.000 & 0.998 \\ 
  WLSMV & 5 & 200 & 1.000 & 0.981 & 1.000 & 1.000 \\ 
  WLSMV & 10 & 30 & 1.000 & 0.945 & 1.000 & 0.983 \\ 
  WLSMV & 10 & 50 & 1.000 & 0.963 & 1.000 & 0.998 \\ 
  WLSMV & 10 & 100 & 1.000 & 0.975 & 1.000 & 1.000 \\ 
  WLSMV & 10 & 200 & 1.000 & 0.986 & 1.000 & 1.000 \\ 
  WLSMV & 30 & 30 & 1.000 & 0.965 & 1.000 & 0.994 \\ 
  WLSMV & 30 & 50 & 1.000 & 0.975 & 1.000 & 1.000 \\ 
  WLSMV & 30 & 100 & 1.000 & 0.985 & 1.000 & 1.000 \\ 
  WLSMV & 30 & 200 & 1.000 & 0.985 & 1.000 & 1.000 \\ 
   \bottomrule
\end{tabular}
\end{table}

Across all conditions

## first table summary table
c <- sim_results %>%
  group_by(Model, Estimator, ss_l1, ss_l2, icc_ov, icc_lv) %>%
  summarise(Converge=mean(Converge))
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Model == 'C' & c$Estimator == 'MLR', c('ss_l1', 'ss_l2','icc_ov', 'icc_lv', 'Converge')],
            c[ c$Model == 'C' & c$Estimator == 'ULSMV', 'Converge'],
            c[ c$Model == 'C' & c$Estimator == 'WLSMV', 'Converge'],
            c[ c$Model == 'M1' & c$Estimator == 'MLR', 'Converge'], 
            c[ c$Model == 'M1' & c$Estimator == 'ULSMV', 'Converge'],
            c[ c$Model == 'M1' & c$Estimator == 'WLSMV', 'Converge'],
            c[ c$Model == 'M2' & c$Estimator == 'MLR', 'Converge'],
            c[ c$Model == 'M2' & c$Estimator == 'ULSMV', 'Converge'],
            c[ c$Model == 'M2' & c$Estimator == 'WLSMV', 'Converge'],
            c[ c$Model == 'M12' & c$Estimator == 'MLR', 'Converge'],
            c[ c$Model == 'M12' & c$Estimator == 'ULSMV', 'Converge'],
            c[ c$Model == 'M12' & c$Estimator == 'WLSMV', 'Converge'])
colnames(c1) <- c('SS Level-1', 'SS Level-2', 'ICC-OV', 'ICC-LV', rep(c('MLR', 'ULSMV', 'WLSMV'), 4))

## Print results in a nice looking table in HTML
kable(c1, format='html') %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 4, 'Model C'=3, 'Model M1'=3, 'Model M2'=3, 'Model M12'=3))
Model C
Model M1
Model M2
Model M12
SS Level-1 SS Level-2 ICC-OV ICC-LV MLR ULSMV WLSMV MLR ULSMV WLSMV MLR ULSMV WLSMV MLR ULSMV WLSMV
5 30 0.1 0.1 1.000 0.998 0.992 0.980 0.924 0.866 1.000 0.998 0.994 1.000 0.976 0.926
5 30 0.1 0.5 1.000 1.000 1.000 1.000 0.980 0.968 1.000 0.938 0.998 1.000 0.942 0.976
5 30 0.3 0.1 1.000 0.998 1.000 0.972 0.892 0.842 1.000 0.994 0.996 1.000 0.952 0.938
5 30 0.3 0.5 1.000 1.000 1.000 0.996 0.970 0.960 1.000 0.934 1.000 1.000 0.940 0.972
5 30 0.5 0.1 1.000 0.966 0.998 0.956 0.866 0.866 1.000 0.968 0.998 0.998 0.946 0.942
5 30 0.5 0.5 1.000 0.976 0.996 0.988 0.938 0.920 0.998 0.914 0.998 1.000 0.906 0.954
5 50 0.1 0.1 1.000 1.000 1.000 0.992 0.932 0.892 1.000 1.000 1.000 1.000 0.988 0.964
5 50 0.1 0.5 1.000 1.000 1.000 0.998 0.998 0.996 1.000 0.982 1.000 1.000 0.974 0.994
5 50 0.3 0.1 1.000 1.000 1.000 0.972 0.916 0.904 1.000 1.000 1.000 1.000 0.988 0.974
5 50 0.3 0.5 1.000 1.000 1.000 1.000 0.996 0.990 1.000 0.966 1.000 1.000 0.964 0.980
5 50 0.5 0.1 1.000 0.998 1.000 0.958 0.916 0.898 1.000 1.000 1.000 1.000 0.978 0.976
5 50 0.5 0.5 1.000 1.000 1.000 0.996 0.988 0.968 1.000 0.944 1.000 1.000 0.942 0.984
5 100 0.1 0.1 1.000 1.000 1.000 0.982 0.966 0.954 1.000 1.000 1.000 1.000 0.998 0.996
5 100 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 1.000 0.996 1.000
5 100 0.3 0.1 1.000 1.000 1.000 0.972 0.942 0.946 1.000 1.000 1.000 1.000 1.000 1.000
5 100 0.3 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 1.000 1.000 0.988 1.000
5 100 0.5 0.1 1.000 1.000 1.000 0.942 0.938 0.932 1.000 1.000 1.000 1.000 0.998 1.000
5 100 0.5 0.5 1.000 1.000 1.000 0.996 1.000 0.992 1.000 0.970 1.000 1.000 0.958 0.994
5 200 0.1 0.1 1.000 1.000 1.000 0.996 0.986 0.984 1.000 1.000 1.000 1.000 1.000 1.000
5 200 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
5 200 0.3 0.1 1.000 1.000 1.000 0.978 0.960 0.954 1.000 1.000 1.000 1.000 1.000 1.000
5 200 0.3 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
5 200 0.5 0.1 1.000 1.000 1.000 0.942 0.962 0.946 1.000 1.000 1.000 1.000 1.000 1.000
5 200 0.5 0.5 0.998 1.000 1.000 1.000 1.000 1.000 1.000 0.996 1.000 1.000 0.996 1.000
10 30 0.1 0.1 1.000 1.000 1.000 0.992 0.948 0.920 1.000 1.000 1.000 1.000 0.990 0.982
10 30 0.1 0.5 1.000 1.000 1.000 1.000 0.998 0.994 1.000 0.980 1.000 1.000 0.970 0.994
10 30 0.3 0.1 1.000 1.000 1.000 0.974 0.898 0.900 1.000 1.000 1.000 0.998 0.990 0.986
10 30 0.3 0.5 1.000 1.000 1.000 1.000 0.996 0.996 1.000 0.940 1.000 1.000 0.952 0.992
10 30 0.5 0.1 1.000 0.998 1.000 0.932 0.938 0.880 1.000 0.990 1.000 1.000 0.980 0.964
10 30 0.5 0.5 1.000 0.996 1.000 0.992 0.984 0.978 1.000 0.944 1.000 0.998 0.938 0.980
10 50 0.1 0.1 1.000 1.000 1.000 0.990 0.956 0.964 1.000 1.000 1.000 1.000 0.998 0.998
10 50 0.1 0.5 1.000 0.998 0.998 1.000 0.998 0.998 1.000 0.992 0.998 1.000 0.994 0.998
10 50 0.3 0.1 1.000 1.000 1.000 0.954 0.936 0.932 1.000 1.000 1.000 1.000 1.000 1.000
10 50 0.3 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.982 1.000 1.000 0.986 1.000
10 50 0.5 0.1 1.000 1.000 1.000 0.926 0.932 0.884 1.000 1.000 1.000 1.000 1.000 0.998
10 50 0.5 0.5 1.000 1.000 1.000 1.000 0.990 0.998 0.998 0.956 1.000 1.000 0.952 0.996
10 100 0.1 0.1 1.000 1.000 1.000 0.992 0.980 0.984 1.000 1.000 1.000 1.000 1.000 1.000
10 100 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 1.000 0.998 1.000
10 100 0.3 0.1 1.000 1.000 1.000 0.964 0.958 0.946 1.000 1.000 1.000 1.000 1.000 1.000
10 100 0.3 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 1.000 0.998 1.000
10 100 0.5 0.1 1.000 1.000 1.000 0.924 0.958 0.918 1.000 1.000 1.000 1.000 1.000 1.000
10 100 0.5 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.978 1.000 1.000 0.972 1.000
10 200 0.1 0.1 1.000 1.000 1.000 0.992 1.000 0.996 1.000 1.000 1.000 1.000 1.000 1.000
10 200 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
10 200 0.3 0.1 0.998 1.000 1.000 0.992 0.976 0.980 1.000 1.000 1.000 1.000 1.000 1.000
10 200 0.3 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
10 200 0.5 0.1 1.000 1.000 1.000 0.940 0.952 0.940 1.000 1.000 1.000 1.000 1.000 1.000
10 200 0.5 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 1.000 0.998 1.000
30 30 0.1 0.1 1.000 1.000 1.000 0.984 0.982 0.982 1.000 1.000 1.000 1.000 1.000 1.000
30 30 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 1.000 1.000 0.992 1.000
30 30 0.3 0.1 1.000 1.000 1.000 0.956 0.940 0.934 1.000 1.000 1.000 1.000 1.000 0.998
30 30 0.3 0.5 1.000 1.000 1.000 1.000 1.000 0.994 1.000 0.962 1.000 0.994 0.968 0.994
30 30 0.5 0.1 1.000 1.000 1.000 0.894 0.932 0.902 1.000 1.000 1.000 1.000 1.000 0.982
30 30 0.5 0.5 1.000 0.998 1.000 0.994 0.990 0.980 1.000 0.934 1.000 1.000 0.924 0.988
30 50 0.1 0.1 1.000 1.000 1.000 0.990 0.992 0.992 1.000 1.000 1.000 1.000 1.000 1.000
30 50 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.992 1.000 1.000 0.992 1.000
30 50 0.3 0.1 1.000 1.000 1.000 0.966 0.964 0.946 1.000 1.000 1.000 1.000 1.000 1.000
30 50 0.3 0.5 0.998 1.000 1.000 1.000 1.000 1.000 0.996 0.988 1.000 0.996 0.982 1.000
30 50 0.5 0.1 1.000 1.000 1.000 0.908 0.948 0.914 1.000 1.000 1.000 1.000 1.000 1.000
30 50 0.5 0.5 1.000 1.000 1.000 0.996 1.000 0.998 1.000 0.972 1.000 1.000 0.962 1.000
30 100 0.1 0.1 1.000 1.000 1.000 0.994 1.000 0.998 1.000 1.000 1.000 1.000 1.000 1.000
30 100 0.1 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 1.000 1.000 1.000
30 100 0.3 0.1 1.000 1.000 1.000 0.986 0.984 0.974 1.000 1.000 1.000 1.000 1.000 1.000
30 100 0.3 0.5 0.996 1.000 1.000 1.000 1.000 1.000 0.998 0.992 1.000 1.000 0.992 1.000
30 100 0.5 0.1 1.000 1.000 1.000 0.928 0.954 0.936 1.000 1.000 1.000 1.000 1.000 1.000
30 100 0.5 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.982 1.000 1.000 0.986 1.000
30 200 0.1 0.1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
30 200 0.1 0.5 1.000 1.000 1.000 1.000 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000
30 200 0.3 0.1 1.000 1.000 1.000 0.994 0.986 0.982 1.000 1.000 1.000 1.000 1.000 1.000
30 200 0.3 0.5 0.996 1.000 1.000 0.998 1.000 1.000 0.996 1.000 1.000 1.000 1.000 1.000
30 200 0.5 0.1 1.000 1.000 1.000 0.932 0.972 0.926 1.000 1.000 1.000 1.000 1.000 1.000
30 200 0.5 0.5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 1.000 
## Print out in tex
print(xtable(c1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:47 2019
\begin{table}[ht]
\centering
\begin{tabular}{llllrrrrrrrrrrrr}
  \toprule
SS Level-1 & SS Level-2 & ICC-OV & ICC-LV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV \\ 
  \midrule
5 & 30 & 0.1 & 0.1 & 1.000 & 0.998 & 0.992 & 0.980 & 0.924 & 0.866 & 1.000 & 0.998 & 0.994 & 1.000 & 0.976 & 0.926 \\ 
  5 & 30 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.980 & 0.968 & 1.000 & 0.938 & 0.998 & 1.000 & 0.942 & 0.976 \\ 
  5 & 30 & 0.3 & 0.1 & 1.000 & 0.998 & 1.000 & 0.972 & 0.892 & 0.842 & 1.000 & 0.994 & 0.996 & 1.000 & 0.952 & 0.938 \\ 
  5 & 30 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 0.996 & 0.970 & 0.960 & 1.000 & 0.934 & 1.000 & 1.000 & 0.940 & 0.972 \\ 
  5 & 30 & 0.5 & 0.1 & 1.000 & 0.966 & 0.998 & 0.956 & 0.866 & 0.866 & 1.000 & 0.968 & 0.998 & 0.998 & 0.946 & 0.942 \\ 
  5 & 30 & 0.5 & 0.5 & 1.000 & 0.976 & 0.996 & 0.988 & 0.938 & 0.920 & 0.998 & 0.914 & 0.998 & 1.000 & 0.906 & 0.954 \\ 
  5 & 50 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.992 & 0.932 & 0.892 & 1.000 & 1.000 & 1.000 & 1.000 & 0.988 & 0.964 \\ 
  5 & 50 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 0.998 & 0.998 & 0.996 & 1.000 & 0.982 & 1.000 & 1.000 & 0.974 & 0.994 \\ 
  5 & 50 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.972 & 0.916 & 0.904 & 1.000 & 1.000 & 1.000 & 1.000 & 0.988 & 0.974 \\ 
  5 & 50 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.996 & 0.990 & 1.000 & 0.966 & 1.000 & 1.000 & 0.964 & 0.980 \\ 
  5 & 50 & 0.5 & 0.1 & 1.000 & 0.998 & 1.000 & 0.958 & 0.916 & 0.898 & 1.000 & 1.000 & 1.000 & 1.000 & 0.978 & 0.976 \\ 
  5 & 50 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.996 & 0.988 & 0.968 & 1.000 & 0.944 & 1.000 & 1.000 & 0.942 & 0.984 \\ 
  5 & 100 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.982 & 0.966 & 0.954 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 0.996 \\ 
  5 & 100 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.996 & 1.000 \\ 
  5 & 100 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.972 & 0.942 & 0.946 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 100 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.988 & 1.000 & 1.000 & 0.988 & 1.000 \\ 
  5 & 100 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.942 & 0.938 & 0.932 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 \\ 
  5 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.996 & 1.000 & 0.992 & 1.000 & 0.970 & 1.000 & 1.000 & 0.958 & 0.994 \\ 
  5 & 200 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.996 & 0.986 & 0.984 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 200 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 200 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.978 & 0.960 & 0.954 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 200 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 200 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.942 & 0.962 & 0.946 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  5 & 200 & 0.5 & 0.5 & 0.998 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.996 & 1.000 & 1.000 & 0.996 & 1.000 \\ 
  10 & 30 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.992 & 0.948 & 0.920 & 1.000 & 1.000 & 1.000 & 1.000 & 0.990 & 0.982 \\ 
  10 & 30 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 0.994 & 1.000 & 0.980 & 1.000 & 1.000 & 0.970 & 0.994 \\ 
  10 & 30 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.974 & 0.898 & 0.900 & 1.000 & 1.000 & 1.000 & 0.998 & 0.990 & 0.986 \\ 
  10 & 30 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.996 & 0.996 & 1.000 & 0.940 & 1.000 & 1.000 & 0.952 & 0.992 \\ 
  10 & 30 & 0.5 & 0.1 & 1.000 & 0.998 & 1.000 & 0.932 & 0.938 & 0.880 & 1.000 & 0.990 & 1.000 & 1.000 & 0.980 & 0.964 \\ 
  10 & 30 & 0.5 & 0.5 & 1.000 & 0.996 & 1.000 & 0.992 & 0.984 & 0.978 & 1.000 & 0.944 & 1.000 & 0.998 & 0.938 & 0.980 \\ 
  10 & 50 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.990 & 0.956 & 0.964 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 0.998 \\ 
  10 & 50 & 0.1 & 0.5 & 1.000 & 0.998 & 0.998 & 1.000 & 0.998 & 0.998 & 1.000 & 0.992 & 0.998 & 1.000 & 0.994 & 0.998 \\ 
  10 & 50 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.954 & 0.936 & 0.932 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 50 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.982 & 1.000 & 1.000 & 0.986 & 1.000 \\ 
  10 & 50 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.926 & 0.932 & 0.884 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 \\ 
  10 & 50 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.990 & 0.998 & 0.998 & 0.956 & 1.000 & 1.000 & 0.952 & 0.996 \\ 
  10 & 100 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.992 & 0.980 & 0.984 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 100 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.998 & 1.000 \\ 
  10 & 100 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.964 & 0.958 & 0.946 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 100 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.998 & 1.000 \\ 
  10 & 100 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.924 & 0.958 & 0.918 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.978 & 1.000 & 1.000 & 0.972 & 1.000 \\ 
  10 & 200 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.992 & 1.000 & 0.996 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 200 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 200 & 0.3 & 0.1 & 0.998 & 1.000 & 1.000 & 0.992 & 0.976 & 0.980 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 200 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 200 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.940 & 0.952 & 0.940 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  10 & 200 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.998 & 1.000 \\ 
  30 & 30 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.984 & 0.982 & 0.982 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 30 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.988 & 1.000 & 1.000 & 0.992 & 1.000 \\ 
  30 & 30 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.956 & 0.940 & 0.934 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 \\ 
  30 & 30 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.994 & 1.000 & 0.962 & 1.000 & 0.994 & 0.968 & 0.994 \\ 
  30 & 30 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.894 & 0.932 & 0.902 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.982 \\ 
  30 & 30 & 0.5 & 0.5 & 1.000 & 0.998 & 1.000 & 0.994 & 0.990 & 0.980 & 1.000 & 0.934 & 1.000 & 1.000 & 0.924 & 0.988 \\ 
  30 & 50 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.990 & 0.992 & 0.992 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 50 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.992 & 1.000 & 1.000 & 0.992 & 1.000 \\ 
  30 & 50 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.966 & 0.964 & 0.946 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 50 & 0.3 & 0.5 & 0.998 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.996 & 0.988 & 1.000 & 0.996 & 0.982 & 1.000 \\ 
  30 & 50 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.908 & 0.948 & 0.914 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 50 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.996 & 1.000 & 0.998 & 1.000 & 0.972 & 1.000 & 1.000 & 0.962 & 1.000 \\ 
  30 & 100 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.994 & 1.000 & 0.998 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 100 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 100 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.986 & 0.984 & 0.974 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 100 & 0.3 & 0.5 & 0.996 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 0.992 & 1.000 & 1.000 & 0.992 & 1.000 \\ 
  30 & 100 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.928 & 0.954 & 0.936 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.982 & 1.000 & 1.000 & 0.986 & 1.000 \\ 
  30 & 200 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 200 & 0.1 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 200 & 0.3 & 0.1 & 1.000 & 1.000 & 1.000 & 0.994 & 0.986 & 0.982 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 200 & 0.3 & 0.5 & 0.996 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.996 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 200 & 0.5 & 0.1 & 1.000 & 1.000 & 1.000 & 0.932 & 0.972 & 0.926 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 200 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 \\ 
   \bottomrule
\end{tabular}
\end{table}

Admissibility Summary

Admissibility rates are first subsetted to the converged models. So, the rates may seem misleading and not directly relatable across all conditions and models due to differences in convergence rates.

c.sim_results <- filter(sim_results, Converge == 1)

Admissibilty by Model and Estimator

## first table summary table
c <- c.sim_results %>%
  group_by(Model, Estimator) %>%
  summarise(Admissible=mean(Admissible))
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Model == 'C', 'Admissible'], 
           c[ c$Model == 'M1', 'Admissible'], 
           c[ c$Model == 'M2', 'Admissible'], 
           c[ c$Model == 'M12', 'Admissible'])
rownames(c1) <- c('MLR', 'ULSMV', 'WLSMV')
colnames(c1) <- c('C' ,'M1' ,'M2', 'M12')
## Print results in a nice looking table in HTML
kable(c1, format='html') %>%
    kable_styling(full_width = T) %>%
    add_header_above(c(' '= 1, 'Model Specification'=4))
Model Specification
C M1 M2 M12
MLR 0.8339955 0.7310608 0.8316339 0.8299344
ULSMV 0.7650919 0.6100699 0.7719594 0.6409179
WLSMV 0.7217437 0.5238466 0.7334611 0.6174741 
## Print out in tex
print(xtable(c1, digits = 3), booktabs = T, include.rownames = T)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:48 2019
\begin{table}[ht]
\centering
\begin{tabular}{rrrrr}
  \toprule
 & C & M1 & M2 & M12 \\ 
  \midrule
MLR & 0.834 & 0.731 & 0.832 & 0.830 \\ 
  ULSMV & 0.765 & 0.610 & 0.772 & 0.641 \\ 
  WLSMV & 0.722 & 0.524 & 0.733 & 0.617 \\ 
   \bottomrule
\end{tabular}
\end{table}

Model, Estimator, and Sample Sizes

## first table summary table
c <- c.sim_results %>%
  group_by(Model, Estimator, ss_l1, ss_l2) %>%
  summarise(Admissible=mean(Admissible))
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Model == 'C', c('Estimator', 'ss_l1', 'ss_l2', 'Admissible')], 
           c[ c$Model == 'M1', 'Admissible'], 
           c[ c$Model == 'M2', 'Admissible'], 
           c[ c$Model == 'M12', 'Admissible'])
colnames(c1) <- c('Estimation', 'SS Level-1', 'SS Level-2', 'C' ,'M1' ,'M2', 'M12')

## Print results in a nice looking table in HTML
kable(c1, format='html') %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 3, 'Model Specification'=4))
Model Specification
Estimation SS Level-1 SS Level-2 C M1 M2 M12
MLR 5 30 0.5753333 0.4680923 0.5728576 0.5475158
MLR 5 50 0.6773333 0.5655849 0.6833333 0.6670000
MLR 5 100 0.8056667 0.6096402 0.8023333 0.8113333
MLR 5 200 0.9236412 0.6582150 0.9153333 0.9263333
MLR 10 30 0.6846667 0.5935484 0.6846667 0.6771181
MLR 10 50 0.7956667 0.6838160 0.7969323 0.7976667
MLR 10 100 0.9200000 0.7979592 0.9093333 0.9163333
MLR 10 200 0.9633211 0.8706955 0.9596667 0.9606667
MLR 30 30 0.8383333 0.7700755 0.8406667 0.8398398
MLR 30 50 0.9019673 0.8621160 0.8972648 0.8962642
MLR 30 100 0.9462975 0.9343263 0.9466489 0.9473333
MLR 30 200 0.9759840 0.9581364 0.9706471 0.9716667
ULSMV 5 30 0.4051869 0.3605027 0.3992342 0.3705404
ULSMV 5 50 0.5318439 0.4667595 0.5346232 0.4809736
ULSMV 5 100 0.6920000 0.5737256 0.7051713 0.6039070
ULSMV 5 200 0.8753333 0.6828030 0.8969313 0.7311541
ULSMV 10 30 0.5735736 0.4946199 0.5872907 0.5113402
ULSMV 10 50 0.7075692 0.5626290 0.7170320 0.6037099
ULSMV 10 100 0.8730000 0.6682497 0.8865082 0.7114611
ULSMV 10 200 0.9576667 0.7378543 0.9589863 0.7712571
ULSMV 30 30 0.7622541 0.5773443 0.7705642 0.6284840
ULSMV 30 50 0.8796667 0.6663279 0.8716398 0.6987871
ULSMV 30 100 0.9446667 0.7329067 0.9413932 0.7604550
ULSMV 30 200 0.9743333 0.7723304 0.9706667 0.7959320
WLSMV 5 30 0.3558303 0.3142752 0.3606283 0.3346181
WLSMV 5 50 0.4700000 0.3891643 0.4976667 0.4288147
WLSMV 5 100 0.6646667 0.5078984 0.6813333 0.5736227
WLSMV 5 200 0.8486667 0.6101292 0.8676667 0.7156667
WLSMV 10 30 0.5080000 0.4121383 0.5296667 0.4659207
WLSMV 10 50 0.6605535 0.5031163 0.6735579 0.5826377
WLSMV 10 100 0.8243333 0.5704514 0.8400000 0.6970000
WLSMV 10 200 0.9356667 0.6433401 0.9460000 0.7650000
WLSMV 30 30 0.6883333 0.4709945 0.7013333 0.6014760
WLSMV 30 50 0.8196667 0.5678632 0.8186667 0.6866667
WLSMV 30 100 0.9110000 0.6205146 0.9143333 0.7586667
WLSMV 30 200 0.9733333 0.6479350 0.9696667 0.7790000 
## Print out in tex
print(xtable(c1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:48 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllrrrr}
  \toprule
Estimation & SS Level-1 & SS Level-2 & C & M1 & M2 & M12 \\ 
  \midrule
MLR & 5 & 30 & 0.575 & 0.468 & 0.573 & 0.548 \\ 
  MLR & 5 & 50 & 0.677 & 0.566 & 0.683 & 0.667 \\ 
  MLR & 5 & 100 & 0.806 & 0.610 & 0.802 & 0.811 \\ 
  MLR & 5 & 200 & 0.924 & 0.658 & 0.915 & 0.926 \\ 
  MLR & 10 & 30 & 0.685 & 0.594 & 0.685 & 0.677 \\ 
  MLR & 10 & 50 & 0.796 & 0.684 & 0.797 & 0.798 \\ 
  MLR & 10 & 100 & 0.920 & 0.798 & 0.909 & 0.916 \\ 
  MLR & 10 & 200 & 0.963 & 0.871 & 0.960 & 0.961 \\ 
  MLR & 30 & 30 & 0.838 & 0.770 & 0.841 & 0.840 \\ 
  MLR & 30 & 50 & 0.902 & 0.862 & 0.897 & 0.896 \\ 
  MLR & 30 & 100 & 0.946 & 0.934 & 0.947 & 0.947 \\ 
  MLR & 30 & 200 & 0.976 & 0.958 & 0.971 & 0.972 \\ 
  ULSMV & 5 & 30 & 0.405 & 0.361 & 0.399 & 0.371 \\ 
  ULSMV & 5 & 50 & 0.532 & 0.467 & 0.535 & 0.481 \\ 
  ULSMV & 5 & 100 & 0.692 & 0.574 & 0.705 & 0.604 \\ 
  ULSMV & 5 & 200 & 0.875 & 0.683 & 0.897 & 0.731 \\ 
  ULSMV & 10 & 30 & 0.574 & 0.495 & 0.587 & 0.511 \\ 
  ULSMV & 10 & 50 & 0.708 & 0.563 & 0.717 & 0.604 \\ 
  ULSMV & 10 & 100 & 0.873 & 0.668 & 0.887 & 0.711 \\ 
  ULSMV & 10 & 200 & 0.958 & 0.738 & 0.959 & 0.771 \\ 
  ULSMV & 30 & 30 & 0.762 & 0.577 & 0.771 & 0.628 \\ 
  ULSMV & 30 & 50 & 0.880 & 0.666 & 0.872 & 0.699 \\ 
  ULSMV & 30 & 100 & 0.945 & 0.733 & 0.941 & 0.760 \\ 
  ULSMV & 30 & 200 & 0.974 & 0.772 & 0.971 & 0.796 \\ 
  WLSMV & 5 & 30 & 0.356 & 0.314 & 0.361 & 0.335 \\ 
  WLSMV & 5 & 50 & 0.470 & 0.389 & 0.498 & 0.429 \\ 
  WLSMV & 5 & 100 & 0.665 & 0.508 & 0.681 & 0.574 \\ 
  WLSMV & 5 & 200 & 0.849 & 0.610 & 0.868 & 0.716 \\ 
  WLSMV & 10 & 30 & 0.508 & 0.412 & 0.530 & 0.466 \\ 
  WLSMV & 10 & 50 & 0.661 & 0.503 & 0.674 & 0.583 \\ 
  WLSMV & 10 & 100 & 0.824 & 0.570 & 0.840 & 0.697 \\ 
  WLSMV & 10 & 200 & 0.936 & 0.643 & 0.946 & 0.765 \\ 
  WLSMV & 30 & 30 & 0.688 & 0.471 & 0.701 & 0.601 \\ 
  WLSMV & 30 & 50 & 0.820 & 0.568 & 0.819 & 0.687 \\ 
  WLSMV & 30 & 100 & 0.911 & 0.621 & 0.914 & 0.759 \\ 
  WLSMV & 30 & 200 & 0.973 & 0.648 & 0.970 & 0.779 \\ 
   \bottomrule
\end{tabular}
\end{table}

Across all conditions

## first table summary table
c <- c.sim_results %>%
  group_by(Model, Estimator, ss_l1, ss_l2, icc_ov, icc_lv) %>%
  summarise(Admissible=mean(Admissible))
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Model == 'C' & c$Estimator == 'MLR', c('ss_l1', 'ss_l2','icc_ov', 'icc_lv', 'Admissible')],
            c[ c$Model == 'C' & c$Estimator == 'ULSMV', 'Admissible'],
            c[ c$Model == 'C' & c$Estimator == 'WLSMV', 'Admissible'],
            c[ c$Model == 'M1' & c$Estimator == 'MLR', 'Admissible'], 
            c[ c$Model == 'M1' & c$Estimator == 'ULSMV', 'Admissible'],
            c[ c$Model == 'M1' & c$Estimator == 'WLSMV', 'Admissible'],
            c[ c$Model == 'M2' & c$Estimator == 'MLR', 'Admissible'],
            c[ c$Model == 'M2' & c$Estimator == 'ULSMV', 'Admissible'],
            c[ c$Model == 'M2' & c$Estimator == 'WLSMV', 'Admissible'],
            c[ c$Model == 'M12' & c$Estimator == 'MLR', 'Admissible'],
            c[ c$Model == 'M12' & c$Estimator == 'ULSMV', 'Admissible'],
            c[ c$Model == 'M12' & c$Estimator == 'WLSMV', 'Admissible'])
colnames(c1) <- c('SS Level-1', 'SS Level-2', 'ICC-OV', 'ICC-LV', rep(c('MLR', 'ULSMV', 'WLSMV'), 4))

## Print results in a nice looking table in HTML
kable(c1, format='html') %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 4, 'Model C'=3, 'Model M1'=3, 'Model M2'=3, 'Model M12'=3))
Model C
Model M1
Model M2
Model M12
SS Level-1 SS Level-2 ICC-OV ICC-LV MLR ULSMV WLSMV MLR ULSMV WLSMV MLR ULSMV WLSMV MLR ULSMV WLSMV
5 30 0.1 0.1 0.5220000 0.0300601 0.0483871 0.3265306 0.0411255 0.0461894 0.4960000 0.0460922 0.0583501 0.5100000 0.0409836 0.0669546
5 30 0.1 0.5 0.3720000 0.0360000 0.0240000 0.2660000 0.0122449 0.0082645 0.3820000 0.0511727 0.0440882 0.3380000 0.0233546 0.0389344
5 30 0.3 0.1 0.4680000 0.3567134 0.2500000 0.4012346 0.3766816 0.2707838 0.4380000 0.3682093 0.2369478 0.4100000 0.3634454 0.2494670
5 30 0.3 0.5 0.7920000 0.7300000 0.6740000 0.6285141 0.5422680 0.4708333 0.8220000 0.7087794 0.6940000 0.7640000 0.6212766 0.5884774
5 30 0.5 0.1 0.3600000 0.3354037 0.2244489 0.3995816 0.3741339 0.2933025 0.3540000 0.3409091 0.2104208 0.3446894 0.3234672 0.2016985
5 30 0.5 0.5 0.9380000 0.9528689 0.9136546 0.7834008 0.8230277 0.7847826 0.9458918 0.9212254 0.9178357 0.9180000 0.8830022 0.8532495
5 50 0.1 0.1 0.5600000 0.1380000 0.1460000 0.3024194 0.1158798 0.1412556 0.5620000 0.1540000 0.1700000 0.5520000 0.1497976 0.1618257
5 50 0.1 0.5 0.5000000 0.0920000 0.0520000 0.3206413 0.0220441 0.0060241 0.5500000 0.1446029 0.0940000 0.4940000 0.0657084 0.0724346
5 50 0.3 0.1 0.5480000 0.5420000 0.4040000 0.5082305 0.5327511 0.4225664 0.5380000 0.5500000 0.4240000 0.5480000 0.4979757 0.4106776
5 50 0.3 0.5 0.9720000 0.9380000 0.8680000 0.8180000 0.6606426 0.4949495 0.9780000 0.9192547 0.9100000 0.9300000 0.7510373 0.6489796
5 50 0.5 0.1 0.4880000 0.4829659 0.3580000 0.5240084 0.5371179 0.4387528 0.4760000 0.4860000 0.3920000 0.4920000 0.4969325 0.3668033
5 50 0.5 0.5 0.9960000 0.9980000 0.9920000 0.9156627 0.9251012 0.8264463 0.9960000 0.9851695 0.9960000 0.9860000 0.9469214 0.9105691
5 100 0.1 0.1 0.7520000 0.4940000 0.5560000 0.2708758 0.3933747 0.4213836 0.7640000 0.5140000 0.5500000 0.7520000 0.4529058 0.4618474
5 100 0.1 0.5 0.7260000 0.3140000 0.2380000 0.2740000 0.0260000 0.0100000 0.7720000 0.4268537 0.3280000 0.7760000 0.0662651 0.1080000
5 100 0.3 0.1 0.7600000 0.7580000 0.6820000 0.6111111 0.7091295 0.6575053 0.7020000 0.7360000 0.6820000 0.7460000 0.7300000 0.6660000
5 100 0.3 0.5 0.9960000 0.9900000 0.9820000 0.9060000 0.7080000 0.4620000 1.0000000 0.9878543 0.9960000 0.9880000 0.7955466 0.7080000
5 100 0.5 0.1 0.6000000 0.5960000 0.5320000 0.5987261 0.6226013 0.5751073 0.5760000 0.5820000 0.5320000 0.6080000 0.5991984 0.5320000
5 100 0.5 0.5 1.0000000 1.0000000 0.9980000 0.9919679 0.9880000 0.9334677 1.0000000 0.9958763 1.0000000 0.9980000 0.9958246 0.9678068
5 200 0.1 0.1 0.9520000 0.8900000 0.9000000 0.2429719 0.6835700 0.7195122 0.9260000 0.9200000 0.9200000 0.9360000 0.8200000 0.8360000
5 200 0.1 0.5 0.9560000 0.7280000 0.6140000 0.2500000 0.0020000 0.0000000 0.9740000 0.8200000 0.7160000 0.9820000 0.0680000 0.1380000
5 200 0.3 0.1 0.8900000 0.8800000 0.8620000 0.7137014 0.8708333 0.8553459 0.8600000 0.8880000 0.8500000 0.8860000 0.8800000 0.8400000
5 200 0.3 0.5 1.0000000 1.0000000 1.0000000 0.9900000 0.8220000 0.4440000 1.0000000 0.9980000 1.0000000 1.0000000 0.8640000 0.7780000
5 200 0.5 0.1 0.7440000 0.7540000 0.7160000 0.7579618 0.7276507 0.6976744 0.7320000 0.7560000 0.7200000 0.7540000 0.7560000 0.7060000
5 200 0.5 0.5 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9620000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9960000
10 30 0.1 0.1 0.6080000 0.3520000 0.3640000 0.3850806 0.3248945 0.3130435 0.5800000 0.3720000 0.3780000 0.6020000 0.3515152 0.3706721
10 30 0.1 0.5 0.4940000 0.1340000 0.0800000 0.3640000 0.0180361 0.0100604 0.5300000 0.2061224 0.1180000 0.5280000 0.0618557 0.0643863
10 30 0.3 0.1 0.5840000 0.5740000 0.4180000 0.5564682 0.5701559 0.4355556 0.5740000 0.5840000 0.4100000 0.5911824 0.5595960 0.3914807
10 30 0.3 0.5 0.9880000 0.9520000 0.8540000 0.8780000 0.6485944 0.4939759 0.9860000 0.9531915 0.9080000 0.9460000 0.7415966 0.6895161
10 30 0.5 0.1 0.4460000 0.4488978 0.3580000 0.4742489 0.5074627 0.4045455 0.4560000 0.4747475 0.3900000 0.4320000 0.4346939 0.3838174
10 30 0.5 0.5 0.9880000 0.9819277 0.9740000 0.8951613 0.9044715 0.8159509 0.9820000 0.9682203 0.9740000 0.9639279 0.9402985 0.8979592
10 50 0.1 0.1 0.8320000 0.6680000 0.7260000 0.4525253 0.4937238 0.5809129 0.8020000 0.6960000 0.7260000 0.8280000 0.6272545 0.6513026
10 50 0.1 0.5 0.7620000 0.3867735 0.2184369 0.5140000 0.0200401 0.0060120 0.8060000 0.4717742 0.2725451 0.7860000 0.0845070 0.1202405
10 50 0.3 0.1 0.6980000 0.6980000 0.6180000 0.6645702 0.6965812 0.6223176 0.6940000 0.6880000 0.6140000 0.7000000 0.7020000 0.6220000
10 50 0.3 0.5 1.0000000 0.9940000 0.9760000 0.9400000 0.6580000 0.4380000 1.0000000 0.9775967 0.9900000 0.9880000 0.7687627 0.7120000
10 50 0.5 0.1 0.4820000 0.4980000 0.4240000 0.5507559 0.5515021 0.4886878 0.4800000 0.4940000 0.4380000 0.4860000 0.4820000 0.4348697
10 50 0.5 0.5 1.0000000 1.0000000 1.0000000 0.9680000 0.9636364 0.8917836 1.0000000 0.9895397 1.0000000 0.9980000 0.9747899 0.9558233
10 100 0.1 0.1 0.9700000 0.9320000 0.9460000 0.5564516 0.7367347 0.7520325 0.9660000 0.9540000 0.9660000 0.9720000 0.8440000 0.8880000
10 100 0.1 0.5 0.9880000 0.7540000 0.5180000 0.7620000 0.0020000 0.0000000 0.9900000 0.8336673 0.6140000 0.9920000 0.0521042 0.1340000
10 100 0.3 0.1 0.8900000 0.8840000 0.8460000 0.7904564 0.8371608 0.8012685 0.8580000 0.8860000 0.8460000 0.8800000 0.8880000 0.8360000
10 100 0.3 0.5 1.0000000 1.0000000 0.9980000 0.9900000 0.7700000 0.3480000 1.0000000 0.9959920 1.0000000 1.0000000 0.8396794 0.7420000
10 100 0.5 0.1 0.6720000 0.6680000 0.6380000 0.6796537 0.6784969 0.6274510 0.6420000 0.6540000 0.6140000 0.6540000 0.6580000 0.6100000
10 100 0.5 0.5 1.0000000 1.0000000 1.0000000 0.9980000 0.9940000 0.9140000 1.0000000 0.9979550 1.0000000 1.0000000 0.9938272 0.9720000
10 200 0.1 0.1 1.0000000 0.9980000 1.0000000 0.6391129 0.8340000 0.8775100 1.0000000 0.9980000 1.0000000 0.9980000 0.9380000 0.9620000
10 200 0.1 0.5 1.0000000 0.9780000 0.8560000 0.8960000 0.0000000 0.0000000 1.0000000 0.9820000 0.9220000 1.0000000 0.0160000 0.0820000
10 200 0.3 0.1 0.9839679 0.9800000 0.9800000 0.8951613 0.9467213 0.9387755 0.9720000 0.9760000 0.9740000 0.9800000 0.9760000 0.9700000
10 200 0.3 0.5 1.0000000 1.0000000 1.0000000 1.0000000 0.8580000 0.2800000 1.0000000 1.0000000 1.0000000 1.0000000 0.9040000 0.8020000
10 200 0.5 0.1 0.7960000 0.7900000 0.7780000 0.7872340 0.7962185 0.7872340 0.7860000 0.7980000 0.7800000 0.7860000 0.7940000 0.7740000
10 200 0.5 0.5 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9920000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
30 30 0.1 0.1 0.9500000 0.9020000 0.8900000 0.7642276 0.6965377 0.6904277 0.9360000 0.9220000 0.9140000 0.9440000 0.7800000 0.8420000
30 30 0.1 0.5 0.9380000 0.5180000 0.2340000 0.8520000 0.0080000 0.0040000 0.9520000 0.6052632 0.2620000 0.9380000 0.0846774 0.1380000
30 30 0.3 0.1 0.6880000 0.6900000 0.6360000 0.6464435 0.6723404 0.5802998 0.6740000 0.6760000 0.6380000 0.6940000 0.6860000 0.6392786
30 30 0.3 0.5 1.0000000 0.9900000 0.9500000 0.9340000 0.6660000 0.4004024 1.0000000 0.9667360 0.9700000 0.9839034 0.7871901 0.6861167
30 30 0.5 0.1 0.4600000 0.4820000 0.4320000 0.4832215 0.5128755 0.4456763 0.4840000 0.4840000 0.4340000 0.4980000 0.4980000 0.4297352
30 30 0.5 0.5 0.9940000 0.9919840 0.9880000 0.9054326 0.9151515 0.7183673 0.9980000 0.9892934 0.9900000 0.9820000 0.9610390 0.8744939
30 50 0.1 0.1 0.9960000 0.9880000 0.9900000 0.8545455 0.7923387 0.8346774 0.9960000 0.9940000 0.9940000 0.9920000 0.9060000 0.9500000
30 50 0.1 0.5 0.9940000 0.8440000 0.5520000 0.9760000 0.0020000 0.0000000 1.0000000 0.8568548 0.5480000 1.0000000 0.0564516 0.1620000
30 50 0.3 0.1 0.8300000 0.8480000 0.8160000 0.7639752 0.8049793 0.7843552 0.8260000 0.8280000 0.8200000 0.8360000 0.8380000 0.8060000
30 50 0.3 0.5 1.0000000 1.0000000 1.0000000 0.9840000 0.7940000 0.3700000 1.0000000 0.9919028 1.0000000 0.9959839 0.8411405 0.7400000
30 50 0.5 0.1 0.5920000 0.5980000 0.5600000 0.5859031 0.6329114 0.5929978 0.5620000 0.5660000 0.5500000 0.5540000 0.5660000 0.5300000
30 50 0.5 0.5 1.0000000 1.0000000 1.0000000 0.9799197 0.9760000 0.8416834 1.0000000 0.9979424 1.0000000 1.0000000 0.9937630 0.9320000
30 100 0.1 0.1 1.0000000 1.0000000 1.0000000 0.9577465 0.8780000 0.8997996 1.0000000 1.0000000 1.0000000 1.0000000 0.9700000 0.9900000
30 100 0.1 0.5 1.0000000 0.9700000 0.8120000 0.9980000 0.0000000 0.0000000 1.0000000 0.9659319 0.8380000 1.0000000 0.0100000 0.1100000
30 100 0.3 0.1 0.9620000 0.9600000 0.9620000 0.9026369 0.9329268 0.9158111 0.9620000 0.9660000 0.9560000 0.9700000 0.9700000 0.9640000
30 100 0.3 0.5 1.0000000 1.0000000 1.0000000 1.0000000 0.8340000 0.2580000 1.0000000 1.0000000 1.0000000 1.0000000 0.9012097 0.8120000
30 100 0.5 0.1 0.7160000 0.7380000 0.6920000 0.7327586 0.7568134 0.7179487 0.7180000 0.7180000 0.6920000 0.7140000 0.7160000 0.6860000
30 100 0.5 0.5 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9460000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9900000
30 200 0.1 0.1 1.0000000 1.0000000 1.0000000 0.9900000 0.9440000 0.9620000 1.0000000 1.0000000 1.0000000 1.0000000 0.9980000 0.9980000
30 200 0.1 0.5 1.0000000 1.0000000 0.9820000 1.0000000 0.0000000 0.0000000 1.0000000 1.0000000 0.9960000 1.0000000 0.0000000 0.0360000
30 200 0.3 0.1 0.9980000 1.0000000 0.9980000 0.9476861 0.9756592 0.9837067 0.9920000 0.9920000 0.9940000 0.9940000 0.9920000 0.9960000
30 200 0.3 0.5 1.0000000 1.0000000 1.0000000 1.0000000 0.9160000 0.1660000 1.0000000 1.0000000 1.0000000 1.0000000 0.9540000 0.8260000
30 200 0.5 0.1 0.8580000 0.8460000 0.8600000 0.8004292 0.8004115 0.8012959 0.8320000 0.8320000 0.8280000 0.8360000 0.8320000 0.8180000
30 200 0.5 0.5 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9920000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 
## Print out in tex
print(xtable(c1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:48 2019
\begin{table}[ht]
\centering
\begin{tabular}{llllrrrrrrrrrrrr}
  \toprule
SS Level-1 & SS Level-2 & ICC-OV & ICC-LV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV & MLR & ULSMV & WLSMV \\ 
  \midrule
5 & 30 & 0.1 & 0.1 & 0.522 & 0.030 & 0.048 & 0.327 & 0.041 & 0.046 & 0.496 & 0.046 & 0.058 & 0.510 & 0.041 & 0.067 \\ 
  5 & 30 & 0.1 & 0.5 & 0.372 & 0.036 & 0.024 & 0.266 & 0.012 & 0.008 & 0.382 & 0.051 & 0.044 & 0.338 & 0.023 & 0.039 \\ 
  5 & 30 & 0.3 & 0.1 & 0.468 & 0.357 & 0.250 & 0.401 & 0.377 & 0.271 & 0.438 & 0.368 & 0.237 & 0.410 & 0.363 & 0.249 \\ 
  5 & 30 & 0.3 & 0.5 & 0.792 & 0.730 & 0.674 & 0.629 & 0.542 & 0.471 & 0.822 & 0.709 & 0.694 & 0.764 & 0.621 & 0.588 \\ 
  5 & 30 & 0.5 & 0.1 & 0.360 & 0.335 & 0.224 & 0.400 & 0.374 & 0.293 & 0.354 & 0.341 & 0.210 & 0.345 & 0.323 & 0.202 \\ 
  5 & 30 & 0.5 & 0.5 & 0.938 & 0.953 & 0.914 & 0.783 & 0.823 & 0.785 & 0.946 & 0.921 & 0.918 & 0.918 & 0.883 & 0.853 \\ 
  5 & 50 & 0.1 & 0.1 & 0.560 & 0.138 & 0.146 & 0.302 & 0.116 & 0.141 & 0.562 & 0.154 & 0.170 & 0.552 & 0.150 & 0.162 \\ 
  5 & 50 & 0.1 & 0.5 & 0.500 & 0.092 & 0.052 & 0.321 & 0.022 & 0.006 & 0.550 & 0.145 & 0.094 & 0.494 & 0.066 & 0.072 \\ 
  5 & 50 & 0.3 & 0.1 & 0.548 & 0.542 & 0.404 & 0.508 & 0.533 & 0.423 & 0.538 & 0.550 & 0.424 & 0.548 & 0.498 & 0.411 \\ 
  5 & 50 & 0.3 & 0.5 & 0.972 & 0.938 & 0.868 & 0.818 & 0.661 & 0.495 & 0.978 & 0.919 & 0.910 & 0.930 & 0.751 & 0.649 \\ 
  5 & 50 & 0.5 & 0.1 & 0.488 & 0.483 & 0.358 & 0.524 & 0.537 & 0.439 & 0.476 & 0.486 & 0.392 & 0.492 & 0.497 & 0.367 \\ 
  5 & 50 & 0.5 & 0.5 & 0.996 & 0.998 & 0.992 & 0.916 & 0.925 & 0.826 & 0.996 & 0.985 & 0.996 & 0.986 & 0.947 & 0.911 \\ 
  5 & 100 & 0.1 & 0.1 & 0.752 & 0.494 & 0.556 & 0.271 & 0.393 & 0.421 & 0.764 & 0.514 & 0.550 & 0.752 & 0.453 & 0.462 \\ 
  5 & 100 & 0.1 & 0.5 & 0.726 & 0.314 & 0.238 & 0.274 & 0.026 & 0.010 & 0.772 & 0.427 & 0.328 & 0.776 & 0.066 & 0.108 \\ 
  5 & 100 & 0.3 & 0.1 & 0.760 & 0.758 & 0.682 & 0.611 & 0.709 & 0.658 & 0.702 & 0.736 & 0.682 & 0.746 & 0.730 & 0.666 \\ 
  5 & 100 & 0.3 & 0.5 & 0.996 & 0.990 & 0.982 & 0.906 & 0.708 & 0.462 & 1.000 & 0.988 & 0.996 & 0.988 & 0.796 & 0.708 \\ 
  5 & 100 & 0.5 & 0.1 & 0.600 & 0.596 & 0.532 & 0.599 & 0.623 & 0.575 & 0.576 & 0.582 & 0.532 & 0.608 & 0.599 & 0.532 \\ 
  5 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 0.998 & 0.992 & 0.988 & 0.933 & 1.000 & 0.996 & 1.000 & 0.998 & 0.996 & 0.968 \\ 
  5 & 200 & 0.1 & 0.1 & 0.952 & 0.890 & 0.900 & 0.243 & 0.684 & 0.720 & 0.926 & 0.920 & 0.920 & 0.936 & 0.820 & 0.836 \\ 
  5 & 200 & 0.1 & 0.5 & 0.956 & 0.728 & 0.614 & 0.250 & 0.002 & 0.000 & 0.974 & 0.820 & 0.716 & 0.982 & 0.068 & 0.138 \\ 
  5 & 200 & 0.3 & 0.1 & 0.890 & 0.880 & 0.862 & 0.714 & 0.871 & 0.855 & 0.860 & 0.888 & 0.850 & 0.886 & 0.880 & 0.840 \\ 
  5 & 200 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 0.990 & 0.822 & 0.444 & 1.000 & 0.998 & 1.000 & 1.000 & 0.864 & 0.778 \\ 
  5 & 200 & 0.5 & 0.1 & 0.744 & 0.754 & 0.716 & 0.758 & 0.728 & 0.698 & 0.732 & 0.756 & 0.720 & 0.754 & 0.756 & 0.706 \\ 
  5 & 200 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.962 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.996 \\ 
  10 & 30 & 0.1 & 0.1 & 0.608 & 0.352 & 0.364 & 0.385 & 0.325 & 0.313 & 0.580 & 0.372 & 0.378 & 0.602 & 0.352 & 0.371 \\ 
  10 & 30 & 0.1 & 0.5 & 0.494 & 0.134 & 0.080 & 0.364 & 0.018 & 0.010 & 0.530 & 0.206 & 0.118 & 0.528 & 0.062 & 0.064 \\ 
  10 & 30 & 0.3 & 0.1 & 0.584 & 0.574 & 0.418 & 0.556 & 0.570 & 0.436 & 0.574 & 0.584 & 0.410 & 0.591 & 0.560 & 0.391 \\ 
  10 & 30 & 0.3 & 0.5 & 0.988 & 0.952 & 0.854 & 0.878 & 0.649 & 0.494 & 0.986 & 0.953 & 0.908 & 0.946 & 0.742 & 0.690 \\ 
  10 & 30 & 0.5 & 0.1 & 0.446 & 0.449 & 0.358 & 0.474 & 0.507 & 0.405 & 0.456 & 0.475 & 0.390 & 0.432 & 0.435 & 0.384 \\ 
  10 & 30 & 0.5 & 0.5 & 0.988 & 0.982 & 0.974 & 0.895 & 0.904 & 0.816 & 0.982 & 0.968 & 0.974 & 0.964 & 0.940 & 0.898 \\ 
  10 & 50 & 0.1 & 0.1 & 0.832 & 0.668 & 0.726 & 0.453 & 0.494 & 0.581 & 0.802 & 0.696 & 0.726 & 0.828 & 0.627 & 0.651 \\ 
  10 & 50 & 0.1 & 0.5 & 0.762 & 0.387 & 0.218 & 0.514 & 0.020 & 0.006 & 0.806 & 0.472 & 0.273 & 0.786 & 0.085 & 0.120 \\ 
  10 & 50 & 0.3 & 0.1 & 0.698 & 0.698 & 0.618 & 0.665 & 0.697 & 0.622 & 0.694 & 0.688 & 0.614 & 0.700 & 0.702 & 0.622 \\ 
  10 & 50 & 0.3 & 0.5 & 1.000 & 0.994 & 0.976 & 0.940 & 0.658 & 0.438 & 1.000 & 0.978 & 0.990 & 0.988 & 0.769 & 0.712 \\ 
  10 & 50 & 0.5 & 0.1 & 0.482 & 0.498 & 0.424 & 0.551 & 0.552 & 0.489 & 0.480 & 0.494 & 0.438 & 0.486 & 0.482 & 0.435 \\ 
  10 & 50 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.968 & 0.964 & 0.892 & 1.000 & 0.990 & 1.000 & 0.998 & 0.975 & 0.956 \\ 
  10 & 100 & 0.1 & 0.1 & 0.970 & 0.932 & 0.946 & 0.556 & 0.737 & 0.752 & 0.966 & 0.954 & 0.966 & 0.972 & 0.844 & 0.888 \\ 
  10 & 100 & 0.1 & 0.5 & 0.988 & 0.754 & 0.518 & 0.762 & 0.002 & 0.000 & 0.990 & 0.834 & 0.614 & 0.992 & 0.052 & 0.134 \\ 
  10 & 100 & 0.3 & 0.1 & 0.890 & 0.884 & 0.846 & 0.790 & 0.837 & 0.801 & 0.858 & 0.886 & 0.846 & 0.880 & 0.888 & 0.836 \\ 
  10 & 100 & 0.3 & 0.5 & 1.000 & 1.000 & 0.998 & 0.990 & 0.770 & 0.348 & 1.000 & 0.996 & 1.000 & 1.000 & 0.840 & 0.742 \\ 
  10 & 100 & 0.5 & 0.1 & 0.672 & 0.668 & 0.638 & 0.680 & 0.678 & 0.627 & 0.642 & 0.654 & 0.614 & 0.654 & 0.658 & 0.610 \\ 
  10 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.998 & 0.994 & 0.914 & 1.000 & 0.998 & 1.000 & 1.000 & 0.994 & 0.972 \\ 
  10 & 200 & 0.1 & 0.1 & 1.000 & 0.998 & 1.000 & 0.639 & 0.834 & 0.878 & 1.000 & 0.998 & 1.000 & 0.998 & 0.938 & 0.962 \\ 
  10 & 200 & 0.1 & 0.5 & 1.000 & 0.978 & 0.856 & 0.896 & 0.000 & 0.000 & 1.000 & 0.982 & 0.922 & 1.000 & 0.016 & 0.082 \\ 
  10 & 200 & 0.3 & 0.1 & 0.984 & 0.980 & 0.980 & 0.895 & 0.947 & 0.939 & 0.972 & 0.976 & 0.974 & 0.980 & 0.976 & 0.970 \\ 
  10 & 200 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.858 & 0.280 & 1.000 & 1.000 & 1.000 & 1.000 & 0.904 & 0.802 \\ 
  10 & 200 & 0.5 & 0.1 & 0.796 & 0.790 & 0.778 & 0.787 & 0.796 & 0.787 & 0.786 & 0.798 & 0.780 & 0.786 & 0.794 & 0.774 \\ 
  10 & 200 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.992 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
  30 & 30 & 0.1 & 0.1 & 0.950 & 0.902 & 0.890 & 0.764 & 0.697 & 0.690 & 0.936 & 0.922 & 0.914 & 0.944 & 0.780 & 0.842 \\ 
  30 & 30 & 0.1 & 0.5 & 0.938 & 0.518 & 0.234 & 0.852 & 0.008 & 0.004 & 0.952 & 0.605 & 0.262 & 0.938 & 0.085 & 0.138 \\ 
  30 & 30 & 0.3 & 0.1 & 0.688 & 0.690 & 0.636 & 0.646 & 0.672 & 0.580 & 0.674 & 0.676 & 0.638 & 0.694 & 0.686 & 0.639 \\ 
  30 & 30 & 0.3 & 0.5 & 1.000 & 0.990 & 0.950 & 0.934 & 0.666 & 0.400 & 1.000 & 0.967 & 0.970 & 0.984 & 0.787 & 0.686 \\ 
  30 & 30 & 0.5 & 0.1 & 0.460 & 0.482 & 0.432 & 0.483 & 0.513 & 0.446 & 0.484 & 0.484 & 0.434 & 0.498 & 0.498 & 0.430 \\ 
  30 & 30 & 0.5 & 0.5 & 0.994 & 0.992 & 0.988 & 0.905 & 0.915 & 0.718 & 0.998 & 0.989 & 0.990 & 0.982 & 0.961 & 0.874 \\ 
  30 & 50 & 0.1 & 0.1 & 0.996 & 0.988 & 0.990 & 0.855 & 0.792 & 0.835 & 0.996 & 0.994 & 0.994 & 0.992 & 0.906 & 0.950 \\ 
  30 & 50 & 0.1 & 0.5 & 0.994 & 0.844 & 0.552 & 0.976 & 0.002 & 0.000 & 1.000 & 0.857 & 0.548 & 1.000 & 0.056 & 0.162 \\ 
  30 & 50 & 0.3 & 0.1 & 0.830 & 0.848 & 0.816 & 0.764 & 0.805 & 0.784 & 0.826 & 0.828 & 0.820 & 0.836 & 0.838 & 0.806 \\ 
  30 & 50 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 0.984 & 0.794 & 0.370 & 1.000 & 0.992 & 1.000 & 0.996 & 0.841 & 0.740 \\ 
  30 & 50 & 0.5 & 0.1 & 0.592 & 0.598 & 0.560 & 0.586 & 0.633 & 0.593 & 0.562 & 0.566 & 0.550 & 0.554 & 0.566 & 0.530 \\ 
  30 & 50 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 0.980 & 0.976 & 0.842 & 1.000 & 0.998 & 1.000 & 1.000 & 0.994 & 0.932 \\ 
  30 & 100 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.958 & 0.878 & 0.900 & 1.000 & 1.000 & 1.000 & 1.000 & 0.970 & 0.990 \\ 
  30 & 100 & 0.1 & 0.5 & 1.000 & 0.970 & 0.812 & 0.998 & 0.000 & 0.000 & 1.000 & 0.966 & 0.838 & 1.000 & 0.010 & 0.110 \\ 
  30 & 100 & 0.3 & 0.1 & 0.962 & 0.960 & 0.962 & 0.903 & 0.933 & 0.916 & 0.962 & 0.966 & 0.956 & 0.970 & 0.970 & 0.964 \\ 
  30 & 100 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.834 & 0.258 & 1.000 & 1.000 & 1.000 & 1.000 & 0.901 & 0.812 \\ 
  30 & 100 & 0.5 & 0.1 & 0.716 & 0.738 & 0.692 & 0.733 & 0.757 & 0.718 & 0.718 & 0.718 & 0.692 & 0.714 & 0.716 & 0.686 \\ 
  30 & 100 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.946 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.990 \\ 
  30 & 200 & 0.1 & 0.1 & 1.000 & 1.000 & 1.000 & 0.990 & 0.944 & 0.962 & 1.000 & 1.000 & 1.000 & 1.000 & 0.998 & 0.998 \\ 
  30 & 200 & 0.1 & 0.5 & 1.000 & 1.000 & 0.982 & 1.000 & 0.000 & 0.000 & 1.000 & 1.000 & 0.996 & 1.000 & 0.000 & 0.036 \\ 
  30 & 200 & 0.3 & 0.1 & 0.998 & 1.000 & 0.998 & 0.948 & 0.976 & 0.984 & 0.992 & 0.992 & 0.994 & 0.994 & 0.992 & 0.996 \\ 
  30 & 200 & 0.3 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 0.916 & 0.166 & 1.000 & 1.000 & 1.000 & 1.000 & 0.954 & 0.826 \\ 
  30 & 200 & 0.5 & 0.1 & 0.858 & 0.846 & 0.860 & 0.800 & 0.800 & 0.801 & 0.832 & 0.832 & 0.828 & 0.836 & 0.832 & 0.818 \\ 
  30 & 200 & 0.5 & 0.5 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.992 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
   \bottomrule
\end{tabular}
\end{table}

Descriptive Statistics of Fit Indices

Now for the long process of making tables for the MANY conditions for the descriptive statistics. For this, we need to do this is steps so that all the information gets outputted in the correct manor for table. For each statistic under each condition, model, and estimator, the code below create a table that contains the average value and the standard deviation. Again, just like the descriptives above, a summary table was made to start.

These tables are made based only on the 1) converged models and 2) the admissible solutions.

The fit indices included are:

  1. Chi-square p-value summary, i.e. proportion of tims the p-value was greater than 0.05. No SD is reported here.
  2. CFI with mean and sd
  3. TLI with mean and sd
  4. RMSEA with mean and sd
  5. SRMRW with mean and sd
  6. SRMRB with mean and sd
mydata <- filter(sim_results, Converge == 1 & Admissible == 1)

## first table summary table
a <- mydata %>%
  group_by(Model, Estimator) %>%
  summarise(
    chi2=mean(Chi2_pvalue_decision, na.rm = T),
    CFI.m =mean(CFI, na.rm = T), CFI.sd =sd(CFI, na.rm = T),
    TLI.m =mean(TLI, na.rm = T), TLI.sd =sd(TLI, na.rm = T),
    RMSEA.m =mean(RMSEA, na.rm = T), RMSEA.sd =sd(RMSEA, na.rm = T),
    SRMRW.m =mean(SRMRW, na.rm = T), SRMRW.sd =sd(SRMRW, na.rm = T),
    SRMRB.m =mean(SRMRB, na.rm = T), SRMRB.sd =sd(SRMRB, na.rm = T)
  )
## Print results in a nice looking table in HTML
kable(a, format='html') %>%
    kable_styling(full_width = T)
Model Estimator chi2 CFI.m CFI.sd TLI.m TLI.sd RMSEA.m RMSEA.sd SRMRW.m SRMRW.sd SRMRB.m SRMRB.sd
C MLR 0.8202745 0.9812052 0.0371156 0.9774462 0.0445387 0.0116853 0.0137071 0.0267436 0.0149748 0.1127033 0.0560528
C ULSMV 0.9768364 0.9893737 0.0304160 0.9872484 0.0364992 0.0043197 0.0069715 0.0374443 0.0219530 0.0824565 0.0464499
C WLSMV 0.9745022 0.9939143 0.0151637 0.9926972 0.0181964 0.0049618 0.0074547 0.0308598 0.0173622 0.0879603 0.0477328
M1 MLR 0.0598615 0.9095858 0.0452227 0.8915221 0.0539201 0.0359143 0.0096761 0.0493582 0.0110398 0.1123173 0.0500658
M1 ULSMV 0.4775854 0.9399431 0.0501061 0.9279318 0.0601273 0.0182307 0.0129284 0.0640931 0.0165727 0.0869087 0.0509335
M1 WLSMV 0.2267400 0.9366788 0.0326178 0.9240146 0.0391414 0.0275762 0.0097161 0.0594152 0.0126836 0.0939776 0.0759974
M2 MLR 0.5229346 0.9695725 0.0409948 0.9634997 0.0489183 0.0182297 0.0146701 0.0271783 0.0155180 0.1367468 0.0505995
M2 ULSMV 0.7160134 0.9542678 0.0645749 0.9451213 0.0774899 0.0114736 0.0114129 0.0410089 0.0218186 0.0968839 0.0424409
M2 WLSMV 0.7513266 0.9846025 0.0222259 0.9815230 0.0266711 0.0103058 0.0098072 0.0316043 0.0174071 0.1043579 0.0412210
M12 MLR 0.0441231 0.8985820 0.0458621 0.8799245 0.0538692 0.0386203 0.0105879 0.0490658 0.0112168 0.1308869 0.0509402
M12 ULSMV 0.4236863 0.9288116 0.0572006 0.9156980 0.0677375 0.0199017 0.0129339 0.0632456 0.0166762 0.0948095 0.0451347
M12 WLSMV 0.2105645 0.9331558 0.0339604 0.9208424 0.0402163 0.0281158 0.0096108 0.0590211 0.0124923 0.0994165 0.0444520 
## make a copy of a to print into
a1 <- as_tibble(as.data.frame(matrix(NA, ncol=8,nrow=nrow(a))))
colnames(a1) <- c('Model', 'Estimation', "chi2", "CFI",'TLI', 'RMSEA', 'SRMRW', 'SRMRB')
i <- 1
for(i in 1:nrow(a)){
  a1[i,3:8] <- unlist(c(
    round(a[i,3],3),
  paste0(round(a[i,4],3), ' (', round(a[i,5],2), ')'),
  paste0(round(a[i,6],3), ' (', round(a[i,7],2), ')'),
  paste0(round(a[i,8],3), ' (', round(a[i,9],2), ')'),
  paste0(round(a[i,10],3), ' (', round(a[i,11],2), ')'),
  paste0(round(a[i,12],3), ' (', round(a[i,12],2), ')')
  ))
}
a1[,1:2] <- a[,1:2]## add factors back
## Print out in tex
print(xtable(a1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:48 2019
\begin{table}[ht]
\centering
\begin{tabular}{llllllll}
  \toprule
Model & Estimation & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
C & MLR & 0.82 & 0.981 (0.04) & 0.977 (0.04) & 0.012 (0.01) & 0.027 (0.01) & 0.113 (0.11) \\ 
  C & ULSMV & 0.977 & 0.989 (0.03) & 0.987 (0.04) & 0.004 (0.01) & 0.037 (0.02) & 0.082 (0.08) \\ 
  C & WLSMV & 0.975 & 0.994 (0.02) & 0.993 (0.02) & 0.005 (0.01) & 0.031 (0.02) & 0.088 (0.09) \\ 
  M1 & MLR & 0.06 & 0.91 (0.05) & 0.892 (0.05) & 0.036 (0.01) & 0.049 (0.01) & 0.112 (0.11) \\ 
  M1 & ULSMV & 0.478 & 0.94 (0.05) & 0.928 (0.06) & 0.018 (0.01) & 0.064 (0.02) & 0.087 (0.09) \\ 
  M1 & WLSMV & 0.227 & 0.937 (0.03) & 0.924 (0.04) & 0.028 (0.01) & 0.059 (0.01) & 0.094 (0.09) \\ 
  M2 & MLR & 0.523 & 0.97 (0.04) & 0.963 (0.05) & 0.018 (0.01) & 0.027 (0.02) & 0.137 (0.14) \\ 
  M2 & ULSMV & 0.716 & 0.954 (0.06) & 0.945 (0.08) & 0.011 (0.01) & 0.041 (0.02) & 0.097 (0.1) \\ 
  M2 & WLSMV & 0.751 & 0.985 (0.02) & 0.982 (0.03) & 0.01 (0.01) & 0.032 (0.02) & 0.104 (0.1) \\ 
  M12 & MLR & 0.044 & 0.899 (0.05) & 0.88 (0.05) & 0.039 (0.01) & 0.049 (0.01) & 0.131 (0.13) \\ 
  M12 & ULSMV & 0.424 & 0.929 (0.06) & 0.916 (0.07) & 0.02 (0.01) & 0.063 (0.02) & 0.095 (0.09) \\ 
  M12 & WLSMV & 0.211 & 0.933 (0.03) & 0.921 (0.04) & 0.028 (0.01) & 0.059 (0.01) & 0.099 (0.1) \\ 
   \bottomrule
\end{tabular}
\end{table}

Fit statistics by Model, Estimation Method, and Sample Size Conditions

An interesting additonal column is added called Prop.Use, which is the total proportion of usable replications for each marginal cell of the design. Each row of the following table represents the marginal distribution of each fit statistic over the ICC conditions. The total number of possible replications is 3000 (500 rep. $$6 conditions). This gives a rough account of the admissibility of the estimation method across sample sizes.

mydata <- filter(sim_results, Converge == 1 & Admissible == 1)

## first table summary table
a <- mydata %>%
  group_by(Model, Estimator, ss_l2, ss_l1) %>%
  summarise(
    Prop.Use=n()/3000,
    chi2=mean(Chi2_pvalue_decision, na.rm = T),
    CFI.m =mean(CFI, na.rm = T), CFI.sd =sd(CFI, na.rm = T),
    TLI.m =mean(TLI, na.rm = T), TLI.sd =sd(TLI, na.rm = T),
    RMSEA.m =mean(RMSEA, na.rm = T), RMSEA.sd =sd(RMSEA, na.rm = T),
    SRMRW.m =mean(SRMRW, na.rm = T), SRMRW.sd =sd(SRMRW, na.rm = T),
    SRMRB.m =mean(SRMRB, na.rm = T), SRMRB.sd =sd(SRMRB, na.rm = T)
  )
## Print results in a nice looking table in HTML
kable(a, format='html') %>%
    kable_styling(full_width = T)
Model Estimator ss_l2 ss_l1 Prop.Use chi2 CFI.m CFI.sd TLI.m TLI.sd RMSEA.m RMSEA.sd SRMRW.m SRMRW.sd SRMRB.m SRMRB.sd
C MLR 30 5 0.5753333 0.5046350 0.8932985 0.0823341 0.8719582 0.0988009 0.0405051 0.0206701 0.0636427 0.0081998 0.2031333 0.0638188
C MLR 30 10 0.6846667 0.6183057 0.9516131 0.0392000 0.9419357 0.0470400 0.0249005 0.0142381 0.0418158 0.0055189 0.1709135 0.0473209
C MLR 30 30 0.8383333 0.6743539 0.9845929 0.0132663 0.9815114 0.0159196 0.0130712 0.0080568 0.0227393 0.0029658 0.1448123 0.0356252
C MLR 50 5 0.6773333 0.7337598 0.9571215 0.0418101 0.9485458 0.0501721 0.0221401 0.0159915 0.0493632 0.0065675 0.1597397 0.0549686
C MLR 50 10 0.7956667 0.7813155 0.9794866 0.0207334 0.9753839 0.0248801 0.0144655 0.0108222 0.0324331 0.0042679 0.1353715 0.0429718
C MLR 50 30 0.9016667 0.8473198 0.9939626 0.0065780 0.9927551 0.0078936 0.0072079 0.0059472 0.0175935 0.0022846 0.1106368 0.0269979
C MLR 100 5 0.8056667 0.8634671 0.9847650 0.0181757 0.9817180 0.0218109 0.0115672 0.0103175 0.0349806 0.0045561 0.1190202 0.0483755
C MLR 100 10 0.9200000 0.9094203 0.9932192 0.0082911 0.9918630 0.0099493 0.0074047 0.0068392 0.0230136 0.0029498 0.0956019 0.0326032
C MLR 100 30 0.9456667 0.9139937 0.9977692 0.0029064 0.9973231 0.0034877 0.0039914 0.0039469 0.0124694 0.0016427 0.0778343 0.0186974
C MLR 200 5 0.9233333 0.9173285 0.9939687 0.0080929 0.9927624 0.0097115 0.0067031 0.0067363 0.0248031 0.0032101 0.0876249 0.0391772
C MLR 200 10 0.9630000 0.9224645 0.9971589 0.0040648 0.9965907 0.0048778 0.0043523 0.0047177 0.0161595 0.0021331 0.0675193 0.0222516
C MLR 200 30 0.9753333 0.9371155 0.9990867 0.0013090 0.9989041 0.0015708 0.0023794 0.0026531 0.0087901 0.0011544 0.0547267 0.0130280
C ULSMV 30 5 0.4010000 0.9890756 0.9454461 0.0874627 0.9345353 0.1049552 0.0107766 0.0127331 0.0940581 0.0187408 0.1437741 0.0472604
C ULSMV 30 10 0.5730000 0.9941759 0.9832451 0.0397025 0.9798941 0.0476430 0.0049432 0.0079180 0.0630602 0.0151495 0.1308273 0.0529347
C ULSMV 30 30 0.7620000 0.9991251 0.9989324 0.0038866 0.9987189 0.0046639 0.0011944 0.0033515 0.0385429 0.0142576 0.1087197 0.0325369
C ULSMV 50 5 0.5316667 0.9774295 0.9647630 0.0524855 0.9577156 0.0629826 0.0102355 0.0112933 0.0698603 0.0132705 0.1186968 0.0593424
C ULSMV 50 10 0.7073333 0.9844486 0.9833106 0.0300769 0.9799727 0.0360923 0.0062335 0.0075432 0.0471195 0.0115804 0.1054053 0.0446192
C ULSMV 50 30 0.8796667 0.9954528 0.9987028 0.0044795 0.9984434 0.0053754 0.0013230 0.0030596 0.0290815 0.0103994 0.0814828 0.0242640
C ULSMV 100 5 0.6920000 0.9701349 0.9835891 0.0247387 0.9803069 0.0296864 0.0076592 0.0083623 0.0476319 0.0093270 0.0938483 0.0522473
C ULSMV 100 10 0.8730000 0.9648721 0.9907426 0.0168573 0.9888912 0.0202287 0.0050159 0.0058859 0.0322581 0.0072826 0.0733091 0.0293011
C ULSMV 100 30 0.9446667 0.9876500 0.9985521 0.0042864 0.9982626 0.0051437 0.0013400 0.0026252 0.0202040 0.0072211 0.0570341 0.0168804
C ULSMV 200 5 0.8753333 0.9508759 0.9925862 0.0123017 0.9911035 0.0147620 0.0052801 0.0061754 0.0329136 0.0062563 0.0697868 0.0378112
C ULSMV 200 10 0.9576667 0.9470936 0.9949812 0.0088710 0.9939774 0.0106452 0.0037695 0.0043763 0.0226012 0.0051646 0.0509646 0.0192118
C ULSMV 200 30 0.9743333 0.9791310 0.9983751 0.0038216 0.9980501 0.0045860 0.0014283 0.0021999 0.0141136 0.0049309 0.0400727 0.0117348
C WLSMV 30 5 0.3550000 0.9800190 0.9668167 0.0428512 0.9601800 0.0514214 0.0145736 0.0139307 0.0792162 0.0108543 0.1580229 0.0465658
C WLSMV 30 10 0.5080000 0.9895013 0.9898077 0.0176223 0.9877692 0.0211467 0.0068601 0.0088506 0.0518724 0.0070620 0.1432885 0.0762484
C WLSMV 30 30 0.6883333 0.9980630 0.9987074 0.0036447 0.9984488 0.0043736 0.0016198 0.0038659 0.0289064 0.0040449 0.1200868 0.0250014
C WLSMV 50 5 0.4700000 0.9695035 0.9786915 0.0271873 0.9744298 0.0326248 0.0120139 0.0115607 0.0611043 0.0083854 0.1249717 0.0473309
C WLSMV 50 10 0.6603333 0.9798082 0.9910403 0.0123614 0.9892484 0.0148337 0.0074499 0.0078938 0.0397920 0.0053735 0.1145602 0.0414510
C WLSMV 50 30 0.8196667 0.9955266 0.9989983 0.0024830 0.9987979 0.0029796 0.0016069 0.0033358 0.0220497 0.0029613 0.0896034 0.0194144
C WLSMV 100 5 0.6646667 0.9638917 0.9894180 0.0142140 0.9873016 0.0170568 0.0083205 0.0085951 0.0426039 0.0057673 0.1004546 0.0518476
C WLSMV 100 10 0.8243333 0.9656288 0.9955280 0.0062126 0.9946336 0.0074551 0.0055033 0.0058379 0.0280509 0.0036185 0.0784487 0.0263732
C WLSMV 100 30 0.9110000 0.9857300 0.9992901 0.0015954 0.9991481 0.0019144 0.0015406 0.0027663 0.0154768 0.0021092 0.0619890 0.0137278
C WLSMV 200 5 0.8486667 0.9481540 0.9949855 0.0071701 0.9939826 0.0086041 0.0056892 0.0062064 0.0299979 0.0040167 0.0729840 0.0358455
C WLSMV 200 10 0.9356667 0.9515497 0.9977034 0.0033198 0.9972441 0.0039838 0.0039304 0.0043703 0.0197049 0.0026053 0.0541746 0.0170661
C WLSMV 200 30 0.9733333 0.9784247 0.9995363 0.0009171 0.9994436 0.0011005 0.0014136 0.0021738 0.0108560 0.0014413 0.0432125 0.0096334
M1 MLR 30 5 0.4596667 0.2719362 0.8279448 0.1038494 0.7937454 0.1231198 0.0537087 0.0200521 0.0757545 0.0154374 0.1978938 0.0606371
M1 MLR 30 10 0.5826667 0.1401602 0.8795338 0.0619563 0.8555550 0.0728614 0.0427529 0.0126111 0.0578834 0.0088208 0.1677090 0.0431786
M1 MLR 30 30 0.7480000 0.0013369 0.9061395 0.0258827 0.8873674 0.0310592 0.0372108 0.0057018 0.0460096 0.0057624 0.1447929 0.0313018
M1 MLR 50 5 0.5576667 0.3287507 0.8981393 0.0602647 0.8777671 0.0723177 0.0381660 0.0146017 0.0636215 0.0089219 0.1565018 0.0527934
M1 MLR 50 10 0.6690000 0.0563029 0.9094790 0.0360598 0.8913748 0.0432718 0.0362868 0.0082390 0.0515803 0.0068527 0.1323486 0.0361130
M1 MLR 50 30 0.8420000 0.0000000 0.9154093 0.0231617 0.8984922 0.0277590 0.0346816 0.0042685 0.0437228 0.0043392 0.1133781 0.0223251
M1 MLR 100 5 0.5986667 0.1375278 0.9229975 0.0359734 0.9075970 0.0431681 0.0325719 0.0085964 0.0536799 0.0073109 0.1137741 0.0419142
M1 MLR 100 10 0.7820000 0.0012788 0.9215794 0.0241065 0.9058953 0.0289278 0.0335419 0.0052091 0.0468181 0.0055537 0.0964331 0.0282805
M1 MLR 100 30 0.9200000 0.0000000 0.9179311 0.0147859 0.9015174 0.0177431 0.0339103 0.0029876 0.0421127 0.0034376 0.0843857 0.0145124
M1 MLR 200 5 0.6490000 0.0056497 0.9285557 0.0239519 0.9142668 0.0287422 0.0311465 0.0050364 0.0478174 0.0055974 0.0839134 0.0320091
M1 MLR 200 10 0.8596667 0.0000000 0.9246596 0.0158180 0.9095916 0.0189816 0.0328584 0.0034157 0.0440500 0.0044707 0.0727541 0.0206507
M1 MLR 200 30 0.9460000 0.0000000 0.9189001 0.0085301 0.9026801 0.0102361 0.0336105 0.0021532 0.0412574 0.0027771 0.0645938 0.0101504
M1 ULSMV 30 5 0.3346667 0.9696970 0.9073943 0.1044099 0.8888731 0.1252919 0.0174782 0.0142945 0.1047616 0.0190994 0.1497136 0.1136820
M1 ULSMV 30 10 0.4750000 0.8903725 0.9462343 0.0647959 0.9354812 0.0777551 0.0132841 0.0127143 0.0800463 0.0149260 0.1344491 0.0646932
M1 ULSMV 30 30 0.5623333 0.7848251 0.9800241 0.0316037 0.9760289 0.0379244 0.0080622 0.0128189 0.0633565 0.0124231 0.1135873 0.0274332
M1 ULSMV 50 5 0.4470000 0.8575690 0.9172491 0.0737221 0.9006989 0.0884665 0.0200700 0.0129166 0.0855089 0.0142693 0.1182543 0.0579931
M1 ULSMV 50 10 0.5450000 0.6495413 0.9319071 0.0524389 0.9182885 0.0629266 0.0188332 0.0118942 0.0685087 0.0114979 0.1066408 0.0411526
M1 ULSMV 50 30 0.6556667 0.6578546 0.9716646 0.0332382 0.9659975 0.0398858 0.0107650 0.0136268 0.0577916 0.0087566 0.0875164 0.0195622
M1 ULSMV 100 5 0.5590000 0.5080501 0.9267744 0.0435493 0.9121292 0.0522592 0.0231327 0.0099313 0.0687324 0.0105225 0.0938491 0.0495554
M1 ULSMV 100 10 0.6566667 0.2994924 0.9289709 0.0355471 0.9147651 0.0426566 0.0225724 0.0102018 0.0590466 0.0079209 0.0772215 0.0266596
M1 ULSMV 100 30 0.7253333 0.4154412 0.9560404 0.0344288 0.9472485 0.0413145 0.0145168 0.0136608 0.0535876 0.0058511 0.0634259 0.0130710
M1 ULSMV 200 5 0.6723333 0.1333664 0.9296839 0.0274694 0.9156207 0.0329633 0.0253275 0.0078386 0.0593315 0.0076708 0.0715381 0.0359275
M1 ULSMV 200 10 0.7290000 0.0470965 0.9274715 0.0248166 0.9129658 0.0297799 0.0243513 0.0091773 0.0542042 0.0057894 0.0558310 0.0174003
M1 ULSMV 200 30 0.7666667 0.1873913 0.9371040 0.0287456 0.9245248 0.0344948 0.0181344 0.0122186 0.0514797 0.0041080 0.0465557 0.0088666
M1 WLSMV 30 5 0.2840000 0.8545888 0.9189596 0.0659712 0.9027516 0.0791655 0.0274477 0.0152448 0.0922661 0.0132521 0.1573806 0.0418333
M1 WLSMV 30 10 0.3893333 0.6915167 0.9393435 0.0423451 0.9272123 0.0508142 0.0246788 0.0111527 0.0711664 0.0096520 0.1506691 0.2482949
M1 WLSMV 30 30 0.4546667 0.5476540 0.9672782 0.0310901 0.9607339 0.0373081 0.0147945 0.0128011 0.0574748 0.0067499 0.1237206 0.0222551
M1 WLSMV 50 5 0.3663333 0.6323931 0.9240718 0.0514137 0.9088861 0.0616965 0.0284971 0.0123555 0.0778598 0.0108183 0.1265593 0.0471844
M1 WLSMV 50 10 0.4843333 0.2436339 0.9323828 0.0310196 0.9188594 0.0372236 0.0288184 0.0074252 0.0624854 0.0079416 0.1175497 0.0408589
M1 WLSMV 50 30 0.5536667 0.2829621 0.9532182 0.0248902 0.9438619 0.0298683 0.0209310 0.0100254 0.0537301 0.0053369 0.0954668 0.0157112
M1 WLSMV 100 5 0.4930000 0.2143340 0.9293181 0.0339205 0.9151817 0.0407046 0.0296656 0.0078239 0.0641684 0.0084442 0.1007401 0.0482194
M1 WLSMV 100 10 0.5560000 0.0053957 0.9314496 0.0218091 0.9177396 0.0261709 0.0311011 0.0049609 0.0555240 0.0060314 0.0833994 0.0248467
M1 WLSMV 100 30 0.6110000 0.0000000 0.9405006 0.0149149 0.9286007 0.0178979 0.0268681 0.0065361 0.0509208 0.0036706 0.0685730 0.0108172
M1 WLSMV 200 5 0.5983333 0.0027855 0.9304711 0.0220219 0.9165654 0.0264263 0.0308497 0.0050272 0.0565740 0.0064400 0.0770224 0.0356701
M1 WLSMV 200 10 0.6343333 0.0000000 0.9309639 0.0155415 0.9171567 0.0186498 0.0325573 0.0034691 0.0517302 0.0044779 0.0602821 0.0162349
M1 WLSMV 200 30 0.6380000 0.0000000 0.9352503 0.0109519 0.9223004 0.0131422 0.0307719 0.0043826 0.0494035 0.0025627 0.0502531 0.0078150
M2 MLR 30 5 0.5726667 0.4208382 0.8768868 0.0853363 0.8524864 0.1003233 0.0454019 0.0209482 0.0648371 0.0144530 0.2142905 0.0628831
M2 MLR 30 10 0.6846667 0.5141188 0.9394119 0.0418715 0.9272943 0.0502458 0.0291665 0.0146850 0.0418724 0.0056009 0.1845507 0.0423258
M2 MLR 30 30 0.8406667 0.4976209 0.9768239 0.0166290 0.9721887 0.0199548 0.0170544 0.0087010 0.0227204 0.0029506 0.1653603 0.0271548
M2 MLR 50 5 0.6833333 0.5975610 0.9424306 0.0455495 0.9309167 0.0546594 0.0279804 0.0169842 0.0500009 0.0068203 0.1759554 0.0528066
M2 MLR 50 10 0.7966667 0.5669456 0.9658055 0.0261056 0.9589666 0.0313268 0.0208794 0.0122474 0.0326685 0.0043425 0.1551570 0.0379620
M2 MLR 50 30 0.8966667 0.5613383 0.9868622 0.0113048 0.9842347 0.0135657 0.0119606 0.0075891 0.0176248 0.0022920 0.1348196 0.0212810
M2 MLR 100 5 0.8023333 0.5961778 0.9692922 0.0245319 0.9631507 0.0294383 0.0198589 0.0124892 0.0359699 0.0049588 0.1416916 0.0460302
M2 MLR 100 10 0.9093333 0.5546188 0.9800927 0.0172118 0.9761112 0.0206542 0.0152660 0.0103069 0.0232723 0.0030261 0.1217249 0.0302883
M2 MLR 100 30 0.9463333 0.5265939 0.9907999 0.0090406 0.9889599 0.0108487 0.0097267 0.0067564 0.0125287 0.0016545 0.1073089 0.0220481
M2 MLR 200 5 0.9153333 0.5149308 0.9783773 0.0182503 0.9740528 0.0219003 0.0163294 0.0112542 0.0261184 0.0039480 0.1155436 0.0394663
M2 MLR 200 10 0.9596667 0.4831539 0.9844525 0.0149561 0.9813430 0.0179473 0.0131158 0.0097141 0.0165422 0.0022726 0.0978743 0.0273643
M2 MLR 200 30 0.9700000 0.4402062 0.9921578 0.0084937 0.9905893 0.0101924 0.0088152 0.0064968 0.0088860 0.0011759 0.0886444 0.0262954
M2 ULSMV 30 5 0.3823333 0.9868074 0.9264214 0.0967748 0.9117057 0.1161297 0.0140636 0.0137196 0.0943324 0.0193981 0.1519312 0.0512032
M2 ULSMV 30 10 0.5730000 0.9784508 0.9668766 0.0581189 0.9602519 0.0697426 0.0081142 0.0099281 0.0643076 0.0165428 0.1384124 0.0502489
M2 ULSMV 30 30 0.7556667 0.9823555 0.9919844 0.0238393 0.9903813 0.0286072 0.0027527 0.0051926 0.0407429 0.0159208 0.1198659 0.0286560
M2 ULSMV 50 5 0.5250000 0.9180952 0.9427773 0.0663777 0.9313328 0.0796533 0.0151046 0.0127881 0.0711857 0.0144865 0.1260666 0.0516593
M2 ULSMV 50 10 0.7086667 0.8480715 0.9534984 0.0570345 0.9441981 0.0684414 0.0125772 0.0104281 0.0494179 0.0133196 0.1168586 0.0438318
M2 ULSMV 50 30 0.8646667 0.8430995 0.9749747 0.0531008 0.9699696 0.0637209 0.0053262 0.0075118 0.0329672 0.0135000 0.0961203 0.0203675
M2 ULSMV 100 5 0.7000000 0.7223810 0.9504737 0.0499822 0.9405685 0.0599786 0.0164645 0.0116254 0.0505291 0.0117531 0.1051609 0.0502177
M2 ULSMV 100 10 0.8826667 0.6076284 0.9470276 0.0591298 0.9364332 0.0709558 0.0146684 0.0113022 0.0361078 0.0105281 0.0888733 0.0273949
M2 ULSMV 100 30 0.9370000 0.6627535 0.9535552 0.0756336 0.9442662 0.0907603 0.0081699 0.0091489 0.0255416 0.0117089 0.0756964 0.0161622
M2 ULSMV 200 5 0.8963333 0.5135738 0.9537313 0.0467093 0.9444776 0.0560512 0.0165402 0.0123953 0.0368798 0.0097533 0.0855435 0.0362861
M2 ULSMV 200 10 0.9586667 0.4680111 0.9443404 0.0603178 0.9332085 0.0723814 0.0152573 0.0120317 0.0277382 0.0096519 0.0701240 0.0205928
M2 ULSMV 200 30 0.9706667 0.5065247 0.9369822 0.0862658 0.9243786 0.1035189 0.0102084 0.0095388 0.0208021 0.0107966 0.0619948 0.0168042
M2 WLSMV 30 5 0.3596667 0.9681051 0.9591080 0.0471423 0.9509296 0.0565707 0.0172070 0.0143739 0.0795665 0.0110994 0.1652995 0.0447009
M2 WLSMV 30 10 0.5296667 0.9836272 0.9857805 0.0205927 0.9829366 0.0247113 0.0089647 0.0096746 0.0519500 0.0071831 0.1501391 0.0438445
M2 WLSMV 30 30 0.7013333 0.9947719 0.9979767 0.0048825 0.9975720 0.0058590 0.0022790 0.0045687 0.0290990 0.0041329 0.1316784 0.0223058
M2 WLSMV 50 5 0.4976667 0.9296718 0.9689022 0.0329686 0.9626827 0.0395623 0.0158590 0.0123343 0.0615267 0.0085039 0.1346153 0.0486574
M2 WLSMV 50 10 0.6733333 0.9242574 0.9836566 0.0175404 0.9803879 0.0210485 0.0116228 0.0088297 0.0401239 0.0054389 0.1253863 0.0378966
M2 WLSMV 50 30 0.8186667 0.9429967 0.9964640 0.0065366 0.9957568 0.0078439 0.0037314 0.0051852 0.0222908 0.0030045 0.1055758 0.0168203
M2 WLSMV 100 5 0.6813333 0.7832681 0.9755103 0.0233066 0.9706123 0.0279680 0.0151498 0.0103333 0.0434504 0.0060392 0.1114324 0.0470169
M2 WLSMV 100 10 0.8400000 0.6801587 0.9834910 0.0176504 0.9801892 0.0211805 0.0123715 0.0085628 0.0286676 0.0037662 0.0949750 0.0249364
M2 WLSMV 100 30 0.9143333 0.7141816 0.9931811 0.0102629 0.9918173 0.0123155 0.0062363 0.0062334 0.0158115 0.0021682 0.0839499 0.0173962
M2 WLSMV 200 5 0.8676667 0.5532078 0.9771855 0.0227883 0.9726226 0.0273460 0.0148311 0.0102331 0.0312747 0.0046371 0.0896961 0.0343056
M2 WLSMV 200 10 0.9460000 0.4834390 0.9818385 0.0203680 0.9782062 0.0244416 0.0130322 0.0090693 0.0207342 0.0030618 0.0755724 0.0207490
M2 WLSMV 200 30 0.9696667 0.5084221 0.9909249 0.0123258 0.9891099 0.0147909 0.0080355 0.0064509 0.0113358 0.0015741 0.0697180 0.0210749
M12 MLR 30 5 0.5473333 0.2186358 0.8122564 0.1050549 0.7781211 0.1212436 0.0574015 0.0199415 0.0757239 0.0174724 0.2109369 0.0617553
M12 MLR 30 10 0.6766667 0.1108374 0.8698080 0.0551897 0.8458252 0.0653562 0.0451135 0.0126302 0.0574990 0.0079218 0.1825757 0.0435619
M12 MLR 30 30 0.8390000 0.0007946 0.9010196 0.0248010 0.8827864 0.0293697 0.0380411 0.0058893 0.0454899 0.0050897 0.1620194 0.0301549
M12 MLR 50 5 0.6670000 0.2353823 0.8780417 0.0621108 0.8555757 0.0735523 0.0433740 0.0152759 0.0632914 0.0087184 0.1715667 0.0518652
M12 MLR 50 10 0.7976667 0.0384455 0.8969962 0.0361292 0.8780218 0.0427846 0.0394144 0.0091658 0.0510261 0.0066060 0.1512124 0.0383161
M12 MLR 50 30 0.8956667 0.0000000 0.9113025 0.0177182 0.8949635 0.0209821 0.0353560 0.0044104 0.0431673 0.0041295 0.1296369 0.0221478
M12 MLR 100 5 0.8113333 0.0661463 0.9023798 0.0360181 0.8843972 0.0426530 0.0385858 0.0100967 0.0530380 0.0067296 0.1351073 0.0444233
M12 MLR 100 10 0.9163333 0.0007275 0.9096490 0.0227230 0.8930054 0.0269089 0.0363592 0.0065496 0.0458809 0.0051455 0.1159149 0.0294768
M12 MLR 100 30 0.9473333 0.0000000 0.9138584 0.0121888 0.8979902 0.0144341 0.0345671 0.0033846 0.0415303 0.0031746 0.1005420 0.0205126
M12 MLR 200 5 0.9263333 0.0021591 0.9104595 0.0226503 0.8939652 0.0268227 0.0366745 0.0076823 0.0467959 0.0053940 0.1081184 0.0371831
M12 MLR 200 10 0.9606667 0.0000000 0.9129963 0.0152327 0.8969693 0.0180388 0.0354119 0.0053128 0.0428842 0.0038452 0.0909054 0.0247797
M12 MLR 200 30 0.9716667 0.0000000 0.9147422 0.0085218 0.8990368 0.0100916 0.0342528 0.0028611 0.0406357 0.0024238 0.0808753 0.0234726
M12 ULSMV 30 5 0.3496667 0.9615014 0.8929173 0.1117543 0.8731916 0.1323406 0.0193149 0.0142984 0.1055940 0.0195633 0.1524434 0.0504126
M12 ULSMV 30 10 0.4960000 0.8633917 0.9349710 0.0729470 0.9229920 0.0863846 0.0150679 0.0129723 0.0797289 0.0153221 0.1413475 0.0526934
M12 ULSMV 30 30 0.6163333 0.7620335 0.9764866 0.0358736 0.9721552 0.0424819 0.0087562 0.0129682 0.0627361 0.0120926 0.1209981 0.0280917
M12 ULSMV 50 5 0.4676667 0.8068425 0.9056477 0.0780119 0.8882670 0.0923826 0.0220752 0.0129666 0.0849297 0.0143446 0.1270214 0.0549684
M12 ULSMV 50 10 0.5966667 0.5592179 0.9190078 0.0578230 0.9040882 0.0684746 0.0212568 0.0115508 0.0674124 0.0113188 0.1172690 0.0447423
M12 ULSMV 50 30 0.6913333 0.6142719 0.9644024 0.0393945 0.9578449 0.0466514 0.0122135 0.0136551 0.0570140 0.0083236 0.0942241 0.0197557
M12 ULSMV 100 5 0.5976667 0.3898494 0.9124454 0.0492565 0.8963169 0.0583301 0.0256472 0.0094146 0.0678399 0.0100662 0.1033590 0.0516531
M12 ULSMV 100 10 0.7076667 0.2190297 0.9156575 0.0441456 0.9001207 0.0522777 0.0245811 0.0097402 0.0580464 0.0073321 0.0852262 0.0272494
M12 ULSMV 100 30 0.7576667 0.3752750 0.9471744 0.0416849 0.9374434 0.0493636 0.0156615 0.0134813 0.0526453 0.0051173 0.0701375 0.0127914
M12 ULSMV 200 5 0.7306667 0.0903285 0.9168927 0.0353834 0.9015835 0.0419013 0.0273698 0.0077926 0.0581552 0.0072850 0.0809653 0.0372992
M12 ULSMV 200 10 0.7710000 0.0272374 0.9146034 0.0370768 0.8988724 0.0439067 0.0259418 0.0089017 0.0530784 0.0050690 0.0635525 0.0177569
M12 ULSMV 200 30 0.7956667 0.1051529 0.9249346 0.0379465 0.9111067 0.0449366 0.0190353 0.0116796 0.0502841 0.0031674 0.0537948 0.0098609
M12 WLSMV 30 5 0.3183333 0.8483563 0.9139667 0.0655161 0.8981185 0.0775849 0.0287698 0.0144873 0.0926543 0.0133104 0.1670900 0.0481952
M12 WLSMV 30 10 0.4580000 0.6924198 0.9377671 0.0430470 0.9263032 0.0509767 0.0249148 0.0110225 0.0707198 0.0095559 0.1509541 0.0467082
M12 WLSMV 30 30 0.5976667 0.5237033 0.9650444 0.0315736 0.9586052 0.0373898 0.0153373 0.0124542 0.0572708 0.0067354 0.1298915 0.0231663
M12 WLSMV 50 5 0.4196667 0.6084194 0.9200406 0.0514383 0.9053112 0.0609138 0.0295396 0.0118873 0.0778411 0.0110315 0.1343776 0.0497305
M12 WLSMV 50 10 0.5816667 0.2126074 0.9287980 0.0326966 0.9156819 0.0387197 0.0294168 0.0073772 0.0621519 0.0080141 0.1230980 0.0398964
M12 WLSMV 50 30 0.6866667 0.2509709 0.9500310 0.0265563 0.9408262 0.0314482 0.0214510 0.0097060 0.0535905 0.0052167 0.1005716 0.0167004
M12 WLSMV 100 5 0.5726667 0.1647264 0.9233434 0.0345696 0.9092224 0.0409377 0.0309663 0.0074938 0.0641541 0.0083080 0.1076600 0.0483367
M12 WLSMV 100 10 0.6970000 0.0043042 0.9268466 0.0249744 0.9133710 0.0295750 0.0317602 0.0051385 0.0554555 0.0060234 0.0892887 0.0255312
M12 WLSMV 100 30 0.7586667 0.0000000 0.9369300 0.0189741 0.9253118 0.0224693 0.0272624 0.0062509 0.0508182 0.0036739 0.0739779 0.0121518
M12 WLSMV 200 5 0.7156667 0.0023288 0.9254905 0.0245777 0.9117651 0.0291052 0.0317410 0.0053603 0.0563204 0.0065315 0.0839725 0.0361022
M12 WLSMV 200 10 0.7650000 0.0000000 0.9265558 0.0194881 0.9130267 0.0230780 0.0330855 0.0037924 0.0516664 0.0044090 0.0660142 0.0173355
M12 WLSMV 200 30 0.7790000 0.0000000 0.9318765 0.0147567 0.9193274 0.0174750 0.0309352 0.0041469 0.0493448 0.0025864 0.0557039 0.0103781 
## make a copy of a to print into
a1 <- as_tibble(as.data.frame(matrix(NA, ncol=11,nrow=nrow(a))))
colnames(a1) <- c('Model', 'Estimation', "N2", "N1", "Prop.Use", "chi2", "CFI",'TLI', 'RMSEA', 'SRMRW', 'SRMRB')
i <- 1
for(i in 1:nrow(a)){
  a1[i,5:11] <- unlist(c(
  round(a[i,5],3), round(a[i,6],3),
  paste0(round(a[i,7],3), ' (', round(a[i,8],2), ')'),
  paste0(round(a[i,9],3), ' (', round(a[i,10],2), ')'),
  paste0(round(a[i,11],3), ' (', round(a[i,12],2), ')'),
  paste0(round(a[i,13],3), ' (', round(a[i,14],2), ')'),
  paste0(round(a[i,15],3), ' (', round(a[i,16],2), ')')
  ))
}
a1[,1:4] <- a[,1:4]## add factors back
## Print out in tex
print(xtable(a1, digits = 3), booktabs = T, include.rownames = F)
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllllll}
  \toprule
Model & Estimation & N2 & N1 & Prop.Use & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
C & MLR & 30 & 5 & 0.575 & 0.505 & 0.893 (0.08) & 0.872 (0.1) & 0.041 (0.02) & 0.064 (0.01) & 0.203 (0.06) \\ 
  C & MLR & 30 & 10 & 0.685 & 0.618 & 0.952 (0.04) & 0.942 (0.05) & 0.025 (0.01) & 0.042 (0.01) & 0.171 (0.05) \\ 
  C & MLR & 30 & 30 & 0.838 & 0.674 & 0.985 (0.01) & 0.982 (0.02) & 0.013 (0.01) & 0.023 (0) & 0.145 (0.04) \\ 
  C & MLR & 50 & 5 & 0.677 & 0.734 & 0.957 (0.04) & 0.949 (0.05) & 0.022 (0.02) & 0.049 (0.01) & 0.16 (0.05) \\ 
  C & MLR & 50 & 10 & 0.796 & 0.781 & 0.979 (0.02) & 0.975 (0.02) & 0.014 (0.01) & 0.032 (0) & 0.135 (0.04) \\ 
  C & MLR & 50 & 30 & 0.902 & 0.847 & 0.994 (0.01) & 0.993 (0.01) & 0.007 (0.01) & 0.018 (0) & 0.111 (0.03) \\ 
  C & MLR & 100 & 5 & 0.806 & 0.863 & 0.985 (0.02) & 0.982 (0.02) & 0.012 (0.01) & 0.035 (0) & 0.119 (0.05) \\ 
  C & MLR & 100 & 10 & 0.92 & 0.909 & 0.993 (0.01) & 0.992 (0.01) & 0.007 (0.01) & 0.023 (0) & 0.096 (0.03) \\ 
  C & MLR & 100 & 30 & 0.946 & 0.914 & 0.998 (0) & 0.997 (0) & 0.004 (0) & 0.012 (0) & 0.078 (0.02) \\ 
  C & MLR & 200 & 5 & 0.923 & 0.917 & 0.994 (0.01) & 0.993 (0.01) & 0.007 (0.01) & 0.025 (0) & 0.088 (0.04) \\ 
  C & MLR & 200 & 10 & 0.963 & 0.922 & 0.997 (0) & 0.997 (0) & 0.004 (0) & 0.016 (0) & 0.068 (0.02) \\ 
  C & MLR & 200 & 30 & 0.975 & 0.937 & 0.999 (0) & 0.999 (0) & 0.002 (0) & 0.009 (0) & 0.055 (0.01) \\ 
  C & ULSMV & 30 & 5 & 0.401 & 0.989 & 0.945 (0.09) & 0.935 (0.1) & 0.011 (0.01) & 0.094 (0.02) & 0.144 (0.05) \\ 
  C & ULSMV & 30 & 10 & 0.573 & 0.994 & 0.983 (0.04) & 0.98 (0.05) & 0.005 (0.01) & 0.063 (0.02) & 0.131 (0.05) \\ 
  C & ULSMV & 30 & 30 & 0.762 & 0.999 & 0.999 (0) & 0.999 (0) & 0.001 (0) & 0.039 (0.01) & 0.109 (0.03) \\ 
  C & ULSMV & 50 & 5 & 0.532 & 0.977 & 0.965 (0.05) & 0.958 (0.06) & 0.01 (0.01) & 0.07 (0.01) & 0.119 (0.06) \\ 
  C & ULSMV & 50 & 10 & 0.707 & 0.984 & 0.983 (0.03) & 0.98 (0.04) & 0.006 (0.01) & 0.047 (0.01) & 0.105 (0.04) \\ 
  C & ULSMV & 50 & 30 & 0.88 & 0.995 & 0.999 (0) & 0.998 (0.01) & 0.001 (0) & 0.029 (0.01) & 0.081 (0.02) \\ 
  C & ULSMV & 100 & 5 & 0.692 & 0.97 & 0.984 (0.02) & 0.98 (0.03) & 0.008 (0.01) & 0.048 (0.01) & 0.094 (0.05) \\ 
  C & ULSMV & 100 & 10 & 0.873 & 0.965 & 0.991 (0.02) & 0.989 (0.02) & 0.005 (0.01) & 0.032 (0.01) & 0.073 (0.03) \\ 
  C & ULSMV & 100 & 30 & 0.945 & 0.988 & 0.999 (0) & 0.998 (0.01) & 0.001 (0) & 0.02 (0.01) & 0.057 (0.02) \\ 
  C & ULSMV & 200 & 5 & 0.875 & 0.951 & 0.993 (0.01) & 0.991 (0.01) & 0.005 (0.01) & 0.033 (0.01) & 0.07 (0.04) \\ 
  C & ULSMV & 200 & 10 & 0.958 & 0.947 & 0.995 (0.01) & 0.994 (0.01) & 0.004 (0) & 0.023 (0.01) & 0.051 (0.02) \\ 
  C & ULSMV & 200 & 30 & 0.974 & 0.979 & 0.998 (0) & 0.998 (0) & 0.001 (0) & 0.014 (0) & 0.04 (0.01) \\ 
  C & WLSMV & 30 & 5 & 0.355 & 0.98 & 0.967 (0.04) & 0.96 (0.05) & 0.015 (0.01) & 0.079 (0.01) & 0.158 (0.05) \\ 
  C & WLSMV & 30 & 10 & 0.508 & 0.99 & 0.99 (0.02) & 0.988 (0.02) & 0.007 (0.01) & 0.052 (0.01) & 0.143 (0.08) \\ 
  C & WLSMV & 30 & 30 & 0.688 & 0.998 & 0.999 (0) & 0.998 (0) & 0.002 (0) & 0.029 (0) & 0.12 (0.03) \\ 
  C & WLSMV & 50 & 5 & 0.47 & 0.97 & 0.979 (0.03) & 0.974 (0.03) & 0.012 (0.01) & 0.061 (0.01) & 0.125 (0.05) \\ 
  C & WLSMV & 50 & 10 & 0.66 & 0.98 & 0.991 (0.01) & 0.989 (0.01) & 0.007 (0.01) & 0.04 (0.01) & 0.115 (0.04) \\ 
  C & WLSMV & 50 & 30 & 0.82 & 0.996 & 0.999 (0) & 0.999 (0) & 0.002 (0) & 0.022 (0) & 0.09 (0.02) \\ 
  C & WLSMV & 100 & 5 & 0.665 & 0.964 & 0.989 (0.01) & 0.987 (0.02) & 0.008 (0.01) & 0.043 (0.01) & 0.1 (0.05) \\ 
  C & WLSMV & 100 & 10 & 0.824 & 0.966 & 0.996 (0.01) & 0.995 (0.01) & 0.006 (0.01) & 0.028 (0) & 0.078 (0.03) \\ 
  C & WLSMV & 100 & 30 & 0.911 & 0.986 & 0.999 (0) & 0.999 (0) & 0.002 (0) & 0.015 (0) & 0.062 (0.01) \\ 
  C & WLSMV & 200 & 5 & 0.849 & 0.948 & 0.995 (0.01) & 0.994 (0.01) & 0.006 (0.01) & 0.03 (0) & 0.073 (0.04) \\ 
  C & WLSMV & 200 & 10 & 0.936 & 0.952 & 0.998 (0) & 0.997 (0) & 0.004 (0) & 0.02 (0) & 0.054 (0.02) \\ 
  C & WLSMV & 200 & 30 & 0.973 & 0.978 & 1 (0) & 0.999 (0) & 0.001 (0) & 0.011 (0) & 0.043 (0.01) \\ 
  M1 & MLR & 30 & 5 & 0.46 & 0.272 & 0.828 (0.1) & 0.794 (0.12) & 0.054 (0.02) & 0.076 (0.02) & 0.198 (0.06) \\ 
  M1 & MLR & 30 & 10 & 0.583 & 0.14 & 0.88 (0.06) & 0.856 (0.07) & 0.043 (0.01) & 0.058 (0.01) & 0.168 (0.04) \\ 
  M1 & MLR & 30 & 30 & 0.748 & 0.001 & 0.906 (0.03) & 0.887 (0.03) & 0.037 (0.01) & 0.046 (0.01) & 0.145 (0.03) \\ 
  M1 & MLR & 50 & 5 & 0.558 & 0.329 & 0.898 (0.06) & 0.878 (0.07) & 0.038 (0.01) & 0.064 (0.01) & 0.157 (0.05) \\ 
  M1 & MLR & 50 & 10 & 0.669 & 0.056 & 0.909 (0.04) & 0.891 (0.04) & 0.036 (0.01) & 0.052 (0.01) & 0.132 (0.04) \\ 
  M1 & MLR & 50 & 30 & 0.842 & 0 & 0.915 (0.02) & 0.898 (0.03) & 0.035 (0) & 0.044 (0) & 0.113 (0.02) \\ 
  M1 & MLR & 100 & 5 & 0.599 & 0.138 & 0.923 (0.04) & 0.908 (0.04) & 0.033 (0.01) & 0.054 (0.01) & 0.114 (0.04) \\ 
  M1 & MLR & 100 & 10 & 0.782 & 0.001 & 0.922 (0.02) & 0.906 (0.03) & 0.034 (0.01) & 0.047 (0.01) & 0.096 (0.03) \\ 
  M1 & MLR & 100 & 30 & 0.92 & 0 & 0.918 (0.01) & 0.902 (0.02) & 0.034 (0) & 0.042 (0) & 0.084 (0.01) \\ 
  M1 & MLR & 200 & 5 & 0.649 & 0.006 & 0.929 (0.02) & 0.914 (0.03) & 0.031 (0.01) & 0.048 (0.01) & 0.084 (0.03) \\ 
  M1 & MLR & 200 & 10 & 0.86 & 0 & 0.925 (0.02) & 0.91 (0.02) & 0.033 (0) & 0.044 (0) & 0.073 (0.02) \\ 
  M1 & MLR & 200 & 30 & 0.946 & 0 & 0.919 (0.01) & 0.903 (0.01) & 0.034 (0) & 0.041 (0) & 0.065 (0.01) \\ 
  M1 & ULSMV & 30 & 5 & 0.335 & 0.97 & 0.907 (0.1) & 0.889 (0.13) & 0.017 (0.01) & 0.105 (0.02) & 0.15 (0.11) \\ 
  M1 & ULSMV & 30 & 10 & 0.475 & 0.89 & 0.946 (0.06) & 0.935 (0.08) & 0.013 (0.01) & 0.08 (0.01) & 0.134 (0.06) \\ 
  M1 & ULSMV & 30 & 30 & 0.562 & 0.785 & 0.98 (0.03) & 0.976 (0.04) & 0.008 (0.01) & 0.063 (0.01) & 0.114 (0.03) \\ 
  M1 & ULSMV & 50 & 5 & 0.447 & 0.858 & 0.917 (0.07) & 0.901 (0.09) & 0.02 (0.01) & 0.086 (0.01) & 0.118 (0.06) \\ 
  M1 & ULSMV & 50 & 10 & 0.545 & 0.65 & 0.932 (0.05) & 0.918 (0.06) & 0.019 (0.01) & 0.069 (0.01) & 0.107 (0.04) \\ 
  M1 & ULSMV & 50 & 30 & 0.656 & 0.658 & 0.972 (0.03) & 0.966 (0.04) & 0.011 (0.01) & 0.058 (0.01) & 0.088 (0.02) \\ 
  M1 & ULSMV & 100 & 5 & 0.559 & 0.508 & 0.927 (0.04) & 0.912 (0.05) & 0.023 (0.01) & 0.069 (0.01) & 0.094 (0.05) \\ 
  M1 & ULSMV & 100 & 10 & 0.657 & 0.299 & 0.929 (0.04) & 0.915 (0.04) & 0.023 (0.01) & 0.059 (0.01) & 0.077 (0.03) \\ 
  M1 & ULSMV & 100 & 30 & 0.725 & 0.415 & 0.956 (0.03) & 0.947 (0.04) & 0.015 (0.01) & 0.054 (0.01) & 0.063 (0.01) \\ 
  M1 & ULSMV & 200 & 5 & 0.672 & 0.133 & 0.93 (0.03) & 0.916 (0.03) & 0.025 (0.01) & 0.059 (0.01) & 0.072 (0.04) \\ 
  M1 & ULSMV & 200 & 10 & 0.729 & 0.047 & 0.927 (0.02) & 0.913 (0.03) & 0.024 (0.01) & 0.054 (0.01) & 0.056 (0.02) \\ 
  M1 & ULSMV & 200 & 30 & 0.767 & 0.187 & 0.937 (0.03) & 0.925 (0.03) & 0.018 (0.01) & 0.051 (0) & 0.047 (0.01) \\ 
  M1 & WLSMV & 30 & 5 & 0.284 & 0.855 & 0.919 (0.07) & 0.903 (0.08) & 0.027 (0.02) & 0.092 (0.01) & 0.157 (0.04) \\ 
  M1 & WLSMV & 30 & 10 & 0.389 & 0.692 & 0.939 (0.04) & 0.927 (0.05) & 0.025 (0.01) & 0.071 (0.01) & 0.151 (0.25) \\ 
  M1 & WLSMV & 30 & 30 & 0.455 & 0.548 & 0.967 (0.03) & 0.961 (0.04) & 0.015 (0.01) & 0.057 (0.01) & 0.124 (0.02) \\ 
  M1 & WLSMV & 50 & 5 & 0.366 & 0.632 & 0.924 (0.05) & 0.909 (0.06) & 0.028 (0.01) & 0.078 (0.01) & 0.127 (0.05) \\ 
  M1 & WLSMV & 50 & 10 & 0.484 & 0.244 & 0.932 (0.03) & 0.919 (0.04) & 0.029 (0.01) & 0.062 (0.01) & 0.118 (0.04) \\ 
  M1 & WLSMV & 50 & 30 & 0.554 & 0.283 & 0.953 (0.02) & 0.944 (0.03) & 0.021 (0.01) & 0.054 (0.01) & 0.095 (0.02) \\ 
  M1 & WLSMV & 100 & 5 & 0.493 & 0.214 & 0.929 (0.03) & 0.915 (0.04) & 0.03 (0.01) & 0.064 (0.01) & 0.101 (0.05) \\ 
  M1 & WLSMV & 100 & 10 & 0.556 & 0.005 & 0.931 (0.02) & 0.918 (0.03) & 0.031 (0) & 0.056 (0.01) & 0.083 (0.02) \\ 
  M1 & WLSMV & 100 & 30 & 0.611 & 0 & 0.941 (0.01) & 0.929 (0.02) & 0.027 (0.01) & 0.051 (0) & 0.069 (0.01) \\ 
  M1 & WLSMV & 200 & 5 & 0.598 & 0.003 & 0.93 (0.02) & 0.917 (0.03) & 0.031 (0.01) & 0.057 (0.01) & 0.077 (0.04) \\ 
  M1 & WLSMV & 200 & 10 & 0.634 & 0 & 0.931 (0.02) & 0.917 (0.02) & 0.033 (0) & 0.052 (0) & 0.06 (0.02) \\ 
  M1 & WLSMV & 200 & 30 & 0.638 & 0 & 0.935 (0.01) & 0.922 (0.01) & 0.031 (0) & 0.049 (0) & 0.05 (0.01) \\ 
  M2 & MLR & 30 & 5 & 0.573 & 0.421 & 0.877 (0.09) & 0.852 (0.1) & 0.045 (0.02) & 0.065 (0.01) & 0.214 (0.06) \\ 
  M2 & MLR & 30 & 10 & 0.685 & 0.514 & 0.939 (0.04) & 0.927 (0.05) & 0.029 (0.01) & 0.042 (0.01) & 0.185 (0.04) \\ 
  M2 & MLR & 30 & 30 & 0.841 & 0.498 & 0.977 (0.02) & 0.972 (0.02) & 0.017 (0.01) & 0.023 (0) & 0.165 (0.03) \\ 
  M2 & MLR & 50 & 5 & 0.683 & 0.598 & 0.942 (0.05) & 0.931 (0.05) & 0.028 (0.02) & 0.05 (0.01) & 0.176 (0.05) \\ 
  M2 & MLR & 50 & 10 & 0.797 & 0.567 & 0.966 (0.03) & 0.959 (0.03) & 0.021 (0.01) & 0.033 (0) & 0.155 (0.04) \\ 
  M2 & MLR & 50 & 30 & 0.897 & 0.561 & 0.987 (0.01) & 0.984 (0.01) & 0.012 (0.01) & 0.018 (0) & 0.135 (0.02) \\ 
  M2 & MLR & 100 & 5 & 0.802 & 0.596 & 0.969 (0.02) & 0.963 (0.03) & 0.02 (0.01) & 0.036 (0) & 0.142 (0.05) \\ 
  M2 & MLR & 100 & 10 & 0.909 & 0.555 & 0.98 (0.02) & 0.976 (0.02) & 0.015 (0.01) & 0.023 (0) & 0.122 (0.03) \\ 
  M2 & MLR & 100 & 30 & 0.946 & 0.527 & 0.991 (0.01) & 0.989 (0.01) & 0.01 (0.01) & 0.013 (0) & 0.107 (0.02) \\ 
  M2 & MLR & 200 & 5 & 0.915 & 0.515 & 0.978 (0.02) & 0.974 (0.02) & 0.016 (0.01) & 0.026 (0) & 0.116 (0.04) \\ 
  M2 & MLR & 200 & 10 & 0.96 & 0.483 & 0.984 (0.01) & 0.981 (0.02) & 0.013 (0.01) & 0.017 (0) & 0.098 (0.03) \\ 
  M2 & MLR & 200 & 30 & 0.97 & 0.44 & 0.992 (0.01) & 0.991 (0.01) & 0.009 (0.01) & 0.009 (0) & 0.089 (0.03) \\ 
  M2 & ULSMV & 30 & 5 & 0.382 & 0.987 & 0.926 (0.1) & 0.912 (0.12) & 0.014 (0.01) & 0.094 (0.02) & 0.152 (0.05) \\ 
  M2 & ULSMV & 30 & 10 & 0.573 & 0.978 & 0.967 (0.06) & 0.96 (0.07) & 0.008 (0.01) & 0.064 (0.02) & 0.138 (0.05) \\ 
  M2 & ULSMV & 30 & 30 & 0.756 & 0.982 & 0.992 (0.02) & 0.99 (0.03) & 0.003 (0.01) & 0.041 (0.02) & 0.12 (0.03) \\ 
  M2 & ULSMV & 50 & 5 & 0.525 & 0.918 & 0.943 (0.07) & 0.931 (0.08) & 0.015 (0.01) & 0.071 (0.01) & 0.126 (0.05) \\ 
  M2 & ULSMV & 50 & 10 & 0.709 & 0.848 & 0.953 (0.06) & 0.944 (0.07) & 0.013 (0.01) & 0.049 (0.01) & 0.117 (0.04) \\ 
  M2 & ULSMV & 50 & 30 & 0.865 & 0.843 & 0.975 (0.05) & 0.97 (0.06) & 0.005 (0.01) & 0.033 (0.01) & 0.096 (0.02) \\ 
  M2 & ULSMV & 100 & 5 & 0.7 & 0.722 & 0.95 (0.05) & 0.941 (0.06) & 0.016 (0.01) & 0.051 (0.01) & 0.105 (0.05) \\ 
  M2 & ULSMV & 100 & 10 & 0.883 & 0.608 & 0.947 (0.06) & 0.936 (0.07) & 0.015 (0.01) & 0.036 (0.01) & 0.089 (0.03) \\ 
  M2 & ULSMV & 100 & 30 & 0.937 & 0.663 & 0.954 (0.08) & 0.944 (0.09) & 0.008 (0.01) & 0.026 (0.01) & 0.076 (0.02) \\ 
  M2 & ULSMV & 200 & 5 & 0.896 & 0.514 & 0.954 (0.05) & 0.944 (0.06) & 0.017 (0.01) & 0.037 (0.01) & 0.086 (0.04) \\ 
  M2 & ULSMV & 200 & 10 & 0.959 & 0.468 & 0.944 (0.06) & 0.933 (0.07) & 0.015 (0.01) & 0.028 (0.01) & 0.07 (0.02) \\ 
  M2 & ULSMV & 200 & 30 & 0.971 & 0.507 & 0.937 (0.09) & 0.924 (0.1) & 0.01 (0.01) & 0.021 (0.01) & 0.062 (0.02) \\ 
  M2 & WLSMV & 30 & 5 & 0.36 & 0.968 & 0.959 (0.05) & 0.951 (0.06) & 0.017 (0.01) & 0.08 (0.01) & 0.165 (0.04) \\ 
  M2 & WLSMV & 30 & 10 & 0.53 & 0.984 & 0.986 (0.02) & 0.983 (0.02) & 0.009 (0.01) & 0.052 (0.01) & 0.15 (0.04) \\ 
  M2 & WLSMV & 30 & 30 & 0.701 & 0.995 & 0.998 (0) & 0.998 (0.01) & 0.002 (0) & 0.029 (0) & 0.132 (0.02) \\ 
  M2 & WLSMV & 50 & 5 & 0.498 & 0.93 & 0.969 (0.03) & 0.963 (0.04) & 0.016 (0.01) & 0.062 (0.01) & 0.135 (0.05) \\ 
  M2 & WLSMV & 50 & 10 & 0.673 & 0.924 & 0.984 (0.02) & 0.98 (0.02) & 0.012 (0.01) & 0.04 (0.01) & 0.125 (0.04) \\ 
  M2 & WLSMV & 50 & 30 & 0.819 & 0.943 & 0.996 (0.01) & 0.996 (0.01) & 0.004 (0.01) & 0.022 (0) & 0.106 (0.02) \\ 
  M2 & WLSMV & 100 & 5 & 0.681 & 0.783 & 0.976 (0.02) & 0.971 (0.03) & 0.015 (0.01) & 0.043 (0.01) & 0.111 (0.05) \\ 
  M2 & WLSMV & 100 & 10 & 0.84 & 0.68 & 0.983 (0.02) & 0.98 (0.02) & 0.012 (0.01) & 0.029 (0) & 0.095 (0.02) \\ 
  M2 & WLSMV & 100 & 30 & 0.914 & 0.714 & 0.993 (0.01) & 0.992 (0.01) & 0.006 (0.01) & 0.016 (0) & 0.084 (0.02) \\ 
  M2 & WLSMV & 200 & 5 & 0.868 & 0.553 & 0.977 (0.02) & 0.973 (0.03) & 0.015 (0.01) & 0.031 (0) & 0.09 (0.03) \\ 
  M2 & WLSMV & 200 & 10 & 0.946 & 0.483 & 0.982 (0.02) & 0.978 (0.02) & 0.013 (0.01) & 0.021 (0) & 0.076 (0.02) \\ 
  M2 & WLSMV & 200 & 30 & 0.97 & 0.508 & 0.991 (0.01) & 0.989 (0.01) & 0.008 (0.01) & 0.011 (0) & 0.07 (0.02) \\ 
  M12 & MLR & 30 & 5 & 0.547 & 0.219 & 0.812 (0.11) & 0.778 (0.12) & 0.057 (0.02) & 0.076 (0.02) & 0.211 (0.06) \\ 
  M12 & MLR & 30 & 10 & 0.677 & 0.111 & 0.87 (0.06) & 0.846 (0.07) & 0.045 (0.01) & 0.057 (0.01) & 0.183 (0.04) \\ 
  M12 & MLR & 30 & 30 & 0.839 & 0.001 & 0.901 (0.02) & 0.883 (0.03) & 0.038 (0.01) & 0.045 (0.01) & 0.162 (0.03) \\ 
  M12 & MLR & 50 & 5 & 0.667 & 0.235 & 0.878 (0.06) & 0.856 (0.07) & 0.043 (0.02) & 0.063 (0.01) & 0.172 (0.05) \\ 
  M12 & MLR & 50 & 10 & 0.798 & 0.038 & 0.897 (0.04) & 0.878 (0.04) & 0.039 (0.01) & 0.051 (0.01) & 0.151 (0.04) \\ 
  M12 & MLR & 50 & 30 & 0.896 & 0 & 0.911 (0.02) & 0.895 (0.02) & 0.035 (0) & 0.043 (0) & 0.13 (0.02) \\ 
  M12 & MLR & 100 & 5 & 0.811 & 0.066 & 0.902 (0.04) & 0.884 (0.04) & 0.039 (0.01) & 0.053 (0.01) & 0.135 (0.04) \\ 
  M12 & MLR & 100 & 10 & 0.916 & 0.001 & 0.91 (0.02) & 0.893 (0.03) & 0.036 (0.01) & 0.046 (0.01) & 0.116 (0.03) \\ 
  M12 & MLR & 100 & 30 & 0.947 & 0 & 0.914 (0.01) & 0.898 (0.01) & 0.035 (0) & 0.042 (0) & 0.101 (0.02) \\ 
  M12 & MLR & 200 & 5 & 0.926 & 0.002 & 0.91 (0.02) & 0.894 (0.03) & 0.037 (0.01) & 0.047 (0.01) & 0.108 (0.04) \\ 
  M12 & MLR & 200 & 10 & 0.961 & 0 & 0.913 (0.02) & 0.897 (0.02) & 0.035 (0.01) & 0.043 (0) & 0.091 (0.02) \\ 
  M12 & MLR & 200 & 30 & 0.972 & 0 & 0.915 (0.01) & 0.899 (0.01) & 0.034 (0) & 0.041 (0) & 0.081 (0.02) \\ 
  M12 & ULSMV & 30 & 5 & 0.35 & 0.962 & 0.893 (0.11) & 0.873 (0.13) & 0.019 (0.01) & 0.106 (0.02) & 0.152 (0.05) \\ 
  M12 & ULSMV & 30 & 10 & 0.496 & 0.863 & 0.935 (0.07) & 0.923 (0.09) & 0.015 (0.01) & 0.08 (0.02) & 0.141 (0.05) \\ 
  M12 & ULSMV & 30 & 30 & 0.616 & 0.762 & 0.976 (0.04) & 0.972 (0.04) & 0.009 (0.01) & 0.063 (0.01) & 0.121 (0.03) \\ 
  M12 & ULSMV & 50 & 5 & 0.468 & 0.807 & 0.906 (0.08) & 0.888 (0.09) & 0.022 (0.01) & 0.085 (0.01) & 0.127 (0.05) \\ 
  M12 & ULSMV & 50 & 10 & 0.597 & 0.559 & 0.919 (0.06) & 0.904 (0.07) & 0.021 (0.01) & 0.067 (0.01) & 0.117 (0.04) \\ 
  M12 & ULSMV & 50 & 30 & 0.691 & 0.614 & 0.964 (0.04) & 0.958 (0.05) & 0.012 (0.01) & 0.057 (0.01) & 0.094 (0.02) \\ 
  M12 & ULSMV & 100 & 5 & 0.598 & 0.39 & 0.912 (0.05) & 0.896 (0.06) & 0.026 (0.01) & 0.068 (0.01) & 0.103 (0.05) \\ 
  M12 & ULSMV & 100 & 10 & 0.708 & 0.219 & 0.916 (0.04) & 0.9 (0.05) & 0.025 (0.01) & 0.058 (0.01) & 0.085 (0.03) \\ 
  M12 & ULSMV & 100 & 30 & 0.758 & 0.375 & 0.947 (0.04) & 0.937 (0.05) & 0.016 (0.01) & 0.053 (0.01) & 0.07 (0.01) \\ 
  M12 & ULSMV & 200 & 5 & 0.731 & 0.09 & 0.917 (0.04) & 0.902 (0.04) & 0.027 (0.01) & 0.058 (0.01) & 0.081 (0.04) \\ 
  M12 & ULSMV & 200 & 10 & 0.771 & 0.027 & 0.915 (0.04) & 0.899 (0.04) & 0.026 (0.01) & 0.053 (0.01) & 0.064 (0.02) \\ 
  M12 & ULSMV & 200 & 30 & 0.796 & 0.105 & 0.925 (0.04) & 0.911 (0.04) & 0.019 (0.01) & 0.05 (0) & 0.054 (0.01) \\ 
  M12 & WLSMV & 30 & 5 & 0.318 & 0.848 & 0.914 (0.07) & 0.898 (0.08) & 0.029 (0.01) & 0.093 (0.01) & 0.167 (0.05) \\ 
  M12 & WLSMV & 30 & 10 & 0.458 & 0.692 & 0.938 (0.04) & 0.926 (0.05) & 0.025 (0.01) & 0.071 (0.01) & 0.151 (0.05) \\ 
  M12 & WLSMV & 30 & 30 & 0.598 & 0.524 & 0.965 (0.03) & 0.959 (0.04) & 0.015 (0.01) & 0.057 (0.01) & 0.13 (0.02) \\ 
  M12 & WLSMV & 50 & 5 & 0.42 & 0.608 & 0.92 (0.05) & 0.905 (0.06) & 0.03 (0.01) & 0.078 (0.01) & 0.134 (0.05) \\ 
  M12 & WLSMV & 50 & 10 & 0.582 & 0.213 & 0.929 (0.03) & 0.916 (0.04) & 0.029 (0.01) & 0.062 (0.01) & 0.123 (0.04) \\ 
  M12 & WLSMV & 50 & 30 & 0.687 & 0.251 & 0.95 (0.03) & 0.941 (0.03) & 0.021 (0.01) & 0.054 (0.01) & 0.101 (0.02) \\ 
  M12 & WLSMV & 100 & 5 & 0.573 & 0.165 & 0.923 (0.03) & 0.909 (0.04) & 0.031 (0.01) & 0.064 (0.01) & 0.108 (0.05) \\ 
  M12 & WLSMV & 100 & 10 & 0.697 & 0.004 & 0.927 (0.02) & 0.913 (0.03) & 0.032 (0.01) & 0.055 (0.01) & 0.089 (0.03) \\ 
  M12 & WLSMV & 100 & 30 & 0.759 & 0 & 0.937 (0.02) & 0.925 (0.02) & 0.027 (0.01) & 0.051 (0) & 0.074 (0.01) \\ 
  M12 & WLSMV & 200 & 5 & 0.716 & 0.002 & 0.925 (0.02) & 0.912 (0.03) & 0.032 (0.01) & 0.056 (0.01) & 0.084 (0.04) \\ 
  M12 & WLSMV & 200 & 10 & 0.765 & 0 & 0.927 (0.02) & 0.913 (0.02) & 0.033 (0) & 0.052 (0) & 0.066 (0.02) \\ 
  M12 & WLSMV & 200 & 30 & 0.779 & 0 & 0.932 (0.01) & 0.919 (0.02) & 0.031 (0) & 0.049 (0) & 0.056 (0.01) \\ 
   \bottomrule
\end{tabular}
\end{table}

TONS of additional tables of the fit statistics across conditions

## Now, create MANY subset tables to breakdown these relationships
## loop around these iterators
for(M in mods){
  for(E in ests){
    ### subset tothe model (M) and estimator (E)
    #M <- 'C'
    #E <- 'MLR'
    cat('\n\n ===============================\n')
    cat('\nModel:\t', M)
    cat('\nEstimator:\t', E, '\n')
    sub_dat <- mydata[ mydata$Model == M & mydata$Estimator == E,]

    a <- sub_dat %>%
        group_by(ss_l1, ss_l2, icc_ov, icc_lv) %>%
        summarise(
          N = n(),
          chi2=mean(Chi2_pvalue_decision, na.rm = T),
          CFI.m =mean(CFI, na.rm = T), CFI.sd =sd(CFI, na.rm = T),
          TLI.m =mean(TLI, na.rm = T), TLI.sd =sd(TLI, na.rm = T),
          RMSEA.m =mean(RMSEA, na.rm = T), RMSEA.sd =sd(RMSEA, na.rm = T),
          SRMRW.m =mean(SRMRW, na.rm = T), SRMRW.sd =sd(SRMRW, na.rm = T),
          SRMRB.m =mean(SRMRB, na.rm = T), SRMRB.sd =sd(SRMRB, na.rm = T)
        )
    #print(xtable(a, digits = 3), booktabs = T, include.rownames = F)
    ## Now, create subsets of this results matrix for outputting into small(ish) tables
    ## Subset by ICC conditions
    ICCO <- unique(a$icc_ov)
    ICCL <- unique(a$icc_lv)
    icco <- ICCO[1]
    iccl <- ICCL[1]
    for(icco in ICCO){
      for(iccl in ICCL){
        ### subset tothe model (M) and estimator (E)
        #M <- 'C'
        #E <- 'MLR'
        cat('\n===============================\n')
        cat('\nModel:\t', M)
        cat('\nEstimator:\t', E)
        cat('\nICC Obs. Var.:\t', icco)
        cat('\nICC Lat. Var.:\t', iccl,'\n')
        a_s <- filter(a, icc_ov == icco, icc_lv == iccl)
        ## make a copy of a to print into
        a1 <- as_tibble(as.data.frame(matrix(NA, ncol=9,nrow=nrow(a_s))))
        colnames(a1) <- c('N2', 'N1', 'Num_Rep', "chi2", "CFI",'TLI', 'RMSEA', 'SRMRW', 'SRMRB')
        i <- 1
        for(i in 1:nrow(a_s)){
          a1[i,3:9] <- unlist(c(
            round(a_s[i,5],2),
            round(a_s[i,6],2),
            paste0(round(a_s[i,7],2), '(', round(a_s[i,8],2), ')'),
            paste0(round(a_s[i,9],2), '(', round(a_s[i,10],2), ')'),
            paste0(round(a_s[i,11],2), '(', round(a_s[i,12],2), ')'),
            paste0(round(a_s[i,13],2), '(', round(a_s[i,14],2), ')'),
            paste0(round(a_s[i,15],2), '(', round(a_s[i,16],2), ')')
          ))
        }
        a1[,1:2] <- a_s[,c(2,1)]## add factors back with diff. order
        ## Print out in tex
        print(xtable(a1,
                     caption = paste0('Summary of Fit Statistics Across Conditions: Model ',
                                      M,', Estimator ',E,', ICC_O ',icco,' and ICC_L ', iccl)), 
              booktabs = T, include.rownames = F)
      }
    }## End subset table printing
  }
} ## End loops..


 ===============================

Model:   C
Estimator:   MLR 

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 261 & 0.35 & 0.88(0.09) & 0.85(0.11) & 0.05(0.02) & 0.06(0.01) & 0.32(0.05) \\ 
  50 & 5 & 280 & 0.53 & 0.94(0.04) & 0.93(0.05) & 0.03(0.02) & 0.05(0.01) & 0.27(0.04) \\ 
  100 & 5 & 376 & 0.7 & 0.98(0.02) & 0.97(0.02) & 0.02(0.01) & 0.03(0) & 0.22(0.03) \\ 
  200 & 5 & 476 & 0.9 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.17(0.02) \\ 
  30 & 10 & 304 & 0.44 & 0.94(0.04) & 0.93(0.04) & 0.03(0.01) & 0.04(0.01) & 0.25(0.04) \\ 
  50 & 10 & 416 & 0.68 & 0.97(0.02) & 0.97(0.03) & 0.02(0.01) & 0.03(0) & 0.21(0.03) \\ 
  100 & 10 & 485 & 0.88 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.15(0.02) \\ 
  200 & 10 & 500 & 0.91 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.11(0.02) \\ 
  30 & 30 & 475 & 0.61 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.19(0.03) \\ 
  50 & 30 & 498 & 0.84 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.14(0.02) \\ 
  100 & 30 & 500 & 0.91 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.1(0.01) \\ 
  200 & 30 & 500 & 0.94 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 186 & 0.44 & 0.93(0.04) & 0.92(0.05) & 0.04(0.02) & 0.07(0.01) & 0.16(0.04) \\ 
  50 & 5 & 250 & 0.69 & 0.97(0.03) & 0.97(0.03) & 0.03(0.02) & 0.05(0.01) & 0.12(0.02) \\ 
  100 & 5 & 363 & 0.84 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.09(0.02) \\ 
  200 & 5 & 478 & 0.9 & 1(0) & 1(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 247 & 0.49 & 0.96(0.03) & 0.95(0.03) & 0.03(0.01) & 0.04(0.01) & 0.12(0.03) \\ 
  50 & 10 & 381 & 0.71 & 0.98(0.02) & 0.98(0.02) & 0.02(0.01) & 0.03(0) & 0.1(0.02) \\ 
  100 & 10 & 494 & 0.89 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.07(0.01) \\ 
  200 & 10 & 500 & 0.92 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 469 & 0.62 & 0.99(0.01) & 0.98(0.01) & 0.01(0.01) & 0.02(0) & 0.1(0.02) \\ 
  50 & 30 & 497 & 0.85 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.07(0.01) \\ 
  100 & 30 & 500 & 0.9 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
  200 & 30 & 500 & 0.93 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.04(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 234 & 0.5 & 0.88(0.09) & 0.85(0.11) & 0.04(0.02) & 0.06(0.01) & 0.23(0.03) \\ 
  50 & 5 & 274 & 0.75 & 0.95(0.05) & 0.94(0.06) & 0.02(0.02) & 0.05(0.01) & 0.19(0.02) \\ 
  100 & 5 & 380 & 0.91 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.03(0) & 0.14(0.02) \\ 
  200 & 5 & 445 & 0.9 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.09(0.01) \\ 
  30 & 10 & 292 & 0.63 & 0.95(0.04) & 0.93(0.05) & 0.02(0.01) & 0.04(0.01) & 0.2(0.02) \\ 
  50 & 10 & 349 & 0.8 & 0.98(0.02) & 0.97(0.03) & 0.01(0.01) & 0.03(0) & 0.15(0.02) \\ 
  100 & 10 & 445 & 0.88 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.11(0.01) \\ 
  200 & 10 & 491 & 0.93 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  30 & 30 & 344 & 0.7 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.16(0.02) \\ 
  50 & 30 & 415 & 0.82 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.13(0.01) \\ 
  100 & 30 & 481 & 0.92 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.09(0.01) \\ 
  200 & 30 & 499 & 0.92 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 396 & 0.49 & 0.91(0.06) & 0.89(0.08) & 0.04(0.02) & 0.07(0.01) & 0.17(0.03) \\ 
  50 & 5 & 486 & 0.73 & 0.96(0.04) & 0.95(0.04) & 0.02(0.02) & 0.05(0.01) & 0.13(0.02) \\ 
  100 & 5 & 498 & 0.91 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0) & 0.09(0.01) \\ 
  200 & 5 & 500 & 0.93 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 494 & 0.65 & 0.96(0.04) & 0.95(0.04) & 0.02(0.01) & 0.04(0.01) & 0.15(0.02) \\ 
  50 & 10 & 500 & 0.82 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.03(0) & 0.11(0.02) \\ 
  100 & 10 & 500 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.08(0.01) \\ 
  200 & 10 & 500 & 0.93 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 500 & 0.71 & 0.99(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.13(0.02) \\ 
  50 & 30 & 499 & 0.84 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 498 & 0.92 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.07(0.01) \\ 
  200 & 30 & 498 & 0.95 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 180 & 0.59 & 0.87(0.1) & 0.84(0.12) & 0.04(0.02) & 0.06(0.01) & 0.19(0.02) \\ 
  50 & 5 & 244 & 0.83 & 0.95(0.05) & 0.94(0.05) & 0.02(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  100 & 5 & 300 & 0.92 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.1(0.01) \\ 
  200 & 5 & 372 & 0.93 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.07(0.01) \\ 
  30 & 10 & 223 & 0.71 & 0.95(0.05) & 0.94(0.06) & 0.02(0.01) & 0.04(0.01) & 0.17(0.02) \\ 
  50 & 10 & 241 & 0.85 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.13(0.01) \\ 
  100 & 10 & 336 & 0.93 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.09(0.01) \\ 
  200 & 10 & 398 & 0.93 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 230 & 0.7 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.16(0.02) \\ 
  50 & 30 & 296 & 0.89 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.12(0.01) \\ 
  100 & 30 & 358 & 0.93 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.08(0.01) \\ 
  200 & 30 & 429 & 0.94 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 469 & 0.6 & 0.89(0.08) & 0.87(0.1) & 0.04(0.02) & 0.06(0.01) & 0.17(0.03) \\ 
  50 & 5 & 498 & 0.82 & 0.96(0.04) & 0.95(0.05) & 0.02(0.02) & 0.05(0.01) & 0.13(0.02) \\ 
  100 & 5 & 500 & 0.88 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.04(0) & 0.09(0.01) \\ 
  200 & 5 & 499 & 0.94 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.06(0.01) \\ 
  30 & 10 & 494 & 0.71 & 0.95(0.04) & 0.95(0.05) & 0.02(0.01) & 0.04(0.01) & 0.15(0.02) \\ 
  50 & 10 & 500 & 0.83 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.12(0.02) \\ 
  100 & 10 & 500 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.08(0.01) \\ 
  200 & 10 & 500 & 0.92 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 497 & 0.73 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.14(0.02) \\ 
  50 & 30 & 500 & 0.87 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.11(0.01) \\ 
  100 & 30 & 500 & 0.91 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.08(0.01) \\ 
  200 & 30 & 500 & 0.93 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator MLR, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   C
Estimator:   ULSMV 

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 15 & 1 & 0.99(0.02) & 0.99(0.02) & 0(0.01) & 0.07(0.01) & 0.35(0.06) \\ 
  50 & 5 & 69 & 0.99 & 0.99(0.02) & 0.99(0.03) & 0.01(0.01) & 0.06(0.01) & 0.32(0.13) \\ 
  100 & 5 & 247 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.22(0.05) \\ 
  200 & 5 & 445 & 0.95 & 1(0.01) & 1(0.01) & 0(0.01) & 0.03(0) & 0.15(0.03) \\ 
  30 & 10 & 176 & 0.99 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.25(0.06) \\ 
  50 & 10 & 334 & 0.99 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.19(0.04) \\ 
  100 & 10 & 466 & 0.97 & 1(0) & 1(0.01) & 0.01(0.01) & 0.03(0) & 0.13(0.02) \\ 
  200 & 10 & 499 & 0.95 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.09(0.01) \\ 
  30 & 30 & 451 & 1 & 1(0) & 1(0.01) & 0.01(0.01) & 0.03(0) & 0.15(0.02) \\ 
  50 & 30 & 494 & 0.99 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.11(0.01) \\ 
  100 & 30 & 500 & 0.97 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.08(0.01) \\ 
  200 & 30 & 500 & 0.97 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 18 & 1 & 0.99(0.02) & 0.98(0.03) & 0.01(0.01) & 0.09(0.01) & 0.13(0.03) \\ 
  50 & 5 & 46 & 1 & 0.99(0.02) & 0.98(0.03) & 0.01(0.01) & 0.07(0.01) & 0.1(0.02) \\ 
  100 & 5 & 157 & 0.99 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.07(0.02) \\ 
  200 & 5 & 364 & 0.96 & 0.99(0.01) & 0.99(0.01) & 0(0.01) & 0.03(0) & 0.05(0.01) \\ 
  30 & 10 & 67 & 1 & 0.99(0.02) & 0.99(0.02) & 0(0.01) & 0.06(0.01) & 0.09(0.02) \\ 
  50 & 10 & 193 & 1 & 0.99(0.02) & 0.99(0.02) & 0(0.01) & 0.04(0.01) & 0.07(0.02) \\ 
  100 & 10 & 377 & 0.98 & 0.99(0.01) & 0.99(0.01) & 0(0.01) & 0.03(0) & 0.05(0.01) \\ 
  200 & 10 & 489 & 0.96 & 1(0.01) & 1(0.01) & 0(0) & 0.02(0) & 0.03(0.01) \\ 
  30 & 30 & 259 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.07(0.02) \\ 
  50 & 30 & 422 & 1 & 1(0) & 1(0.01) & 0(0) & 0.03(0) & 0.05(0.01) \\ 
  100 & 30 & 485 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.02(0) & 0.04(0.01) \\ 
  200 & 30 & 500 & 0.98 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.02(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 178 & 0.99 & 0.96(0.05) & 0.96(0.06) & 0.01(0.01) & 0.08(0.01) & 0.21(0.04) \\ 
  50 & 5 & 271 & 0.97 & 0.97(0.03) & 0.97(0.04) & 0.01(0.01) & 0.06(0.01) & 0.16(0.03) \\ 
  100 & 5 & 379 & 0.98 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.11(0.01) \\ 
  200 & 5 & 440 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  30 & 10 & 287 & 0.99 & 0.98(0.03) & 0.97(0.04) & 0.01(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 10 & 349 & 0.98 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.04(0) & 0.12(0.01) \\ 
  100 & 10 & 442 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  200 & 10 & 490 & 0.95 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 345 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.03(0) & 0.13(0.01) \\ 
  50 & 30 & 424 & 0.99 & 1(0.01) & 1(0.01) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 480 & 0.97 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 500 & 0.94 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 365 & 0.99 & 0.96(0.06) & 0.95(0.07) & 0.01(0.01) & 0.09(0.01) & 0.13(0.03) \\ 
  50 & 5 & 469 & 0.97 & 0.97(0.04) & 0.97(0.05) & 0.01(0.01) & 0.07(0.01) & 0.1(0.02) \\ 
  100 & 5 & 495 & 0.97 & 0.99(0.02) & 0.98(0.02) & 0.01(0.01) & 0.05(0.01) & 0.07(0.01) \\ 
  200 & 5 & 500 & 0.94 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.05(0.01) \\ 
  30 & 10 & 476 & 0.99 & 0.98(0.04) & 0.98(0.05) & 0(0.01) & 0.06(0.01) & 0.1(0.02) \\ 
  50 & 10 & 497 & 0.97 & 0.98(0.03) & 0.98(0.03) & 0.01(0.01) & 0.05(0.01) & 0.08(0.01) \\ 
  100 & 10 & 500 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0(0.01) & 0.04(0) & 0.05(0.01) \\ 
  200 & 10 & 500 & 0.95 & 1(0.01) & 0.99(0.01) & 0(0) & 0.02(0) & 0.04(0.01) \\ 
  30 & 30 & 495 & 1 & 1(0) & 1(0) & 0(0) & 0.04(0.01) & 0.09(0.02) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0.01) & 0.07(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  200 & 30 & 500 & 0.99 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.03(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 162 & 0.97 & 0.91(0.13) & 0.89(0.15) & 0.01(0.01) & 0.09(0.02) & 0.15(0.02) \\ 
  50 & 5 & 241 & 0.96 & 0.95(0.07) & 0.94(0.08) & 0.01(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 5 & 298 & 0.98 & 0.97(0.03) & 0.97(0.04) & 0.01(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 5 & 377 & 0.95 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 224 & 1 & 0.97(0.06) & 0.97(0.07) & 0(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  50 & 10 & 249 & 0.99 & 0.97(0.04) & 0.96(0.05) & 0.01(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  100 & 10 & 334 & 0.95 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  200 & 10 & 395 & 0.93 & 0.99(0.01) & 0.99(0.01) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 241 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0.01) & 0.12(0.01) \\ 
  50 & 30 & 299 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.09(0.01) \\ 
  100 & 30 & 369 & 1 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 423 & 0.99 & 1(0.01) & 1(0.01) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 465 & 1 & 0.94(0.1) & 0.92(0.11) & 0.01(0.01) & 0.11(0.02) & 0.12(0.02) \\ 
  50 & 5 & 499 & 0.99 & 0.96(0.06) & 0.95(0.07) & 0.01(0.01) & 0.08(0.01) & 0.09(0.01) \\ 
  100 & 5 & 500 & 0.97 & 0.98(0.03) & 0.97(0.04) & 0.01(0.01) & 0.06(0.01) & 0.06(0.01) \\ 
  200 & 5 & 500 & 0.95 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.04(0.01) \\ 
  30 & 10 & 489 & 1 & 0.99(0.04) & 0.99(0.05) & 0(0) & 0.08(0.01) & 0.1(0.02) \\ 
  50 & 10 & 500 & 0.99 & 0.98(0.04) & 0.98(0.05) & 0(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  100 & 10 & 500 & 0.97 & 0.98(0.03) & 0.98(0.03) & 0(0.01) & 0.04(0.01) & 0.06(0.01) \\ 
  200 & 10 & 500 & 0.94 & 0.99(0.01) & 0.99(0.02) & 0(0) & 0.03(0) & 0.04(0) \\ 
  30 & 30 & 495 & 1 & 1(0) & 1(0) & 0(0) & 0.06(0.01) & 0.09(0.01) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.05(0.01) & 0.07(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0.01) & 0.05(0.01) \\ 
  200 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.04(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   C
Estimator:   WLSMV 

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 24 & 1 & 0.99(0.02) & 0.99(0.03) & 0.01(0.01) & 0.07(0.01) & 0.34(0.05) \\ 
  50 & 5 & 73 & 1 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.06(0.01) & 0.29(0.05) \\ 
  100 & 5 & 278 & 0.95 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.22(0.05) \\ 
  200 & 5 & 450 & 0.95 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.14(0.03) \\ 
  30 & 10 & 182 & 0.99 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.26(0.18) \\ 
  50 & 10 & 363 & 0.96 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0) & 0.19(0.04) \\ 
  100 & 10 & 473 & 0.97 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.12(0.02) \\ 
  200 & 10 & 500 & 0.95 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.08(0.01) \\ 
  30 & 30 & 445 & 0.99 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.15(0.02) \\ 
  50 & 30 & 495 & 0.99 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.11(0.01) \\ 
  100 & 30 & 500 & 0.96 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.08(0.01) \\ 
  200 & 30 & 500 & 0.97 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 12 & 1 & 0.99(0.02) & 0.98(0.03) & 0.01(0.01) & 0.07(0.01) & 0.16(0.04) \\ 
  50 & 5 & 26 & 1 & 0.99(0.02) & 0.98(0.02) & 0.01(0.01) & 0.06(0.01) & 0.12(0.02) \\ 
  100 & 5 & 119 & 0.96 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.08(0.02) \\ 
  200 & 5 & 307 & 0.96 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 40 & 1 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.12(0.03) \\ 
  50 & 10 & 109 & 0.99 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.08(0.02) \\ 
  100 & 10 & 259 & 0.97 & 1(0.01) & 1(0.01) & 0(0.01) & 0.03(0) & 0.06(0.01) \\ 
  200 & 10 & 428 & 0.95 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.04(0.01) \\ 
  30 & 30 & 117 & 1 & 1(0) & 1(0.01) & 0(0) & 0.03(0) & 0.08(0.01) \\ 
  50 & 30 & 276 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  100 & 30 & 406 & 0.98 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.04(0.01) \\ 
  200 & 30 & 491 & 0.97 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.03(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 125 & 0.98 & 0.97(0.04) & 0.96(0.05) & 0.01(0.01) & 0.07(0.01) & 0.21(0.06) \\ 
  50 & 5 & 202 & 0.96 & 0.98(0.03) & 0.97(0.03) & 0.01(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  100 & 5 & 341 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.11(0.01) \\ 
  200 & 5 & 431 & 0.94 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  30 & 10 & 209 & 0.97 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  50 & 10 & 309 & 0.98 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.12(0.01) \\ 
  100 & 10 & 423 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  200 & 10 & 490 & 0.95 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 318 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.13(0.01) \\ 
  50 & 30 & 408 & 0.99 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 481 & 0.98 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 499 & 0.96 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 337 & 0.97 & 0.97(0.05) & 0.96(0.05) & 0.02(0.01) & 0.08(0.01) & 0.15(0.02) \\ 
  50 & 5 & 434 & 0.96 & 0.98(0.03) & 0.98(0.03) & 0.01(0.01) & 0.06(0.01) & 0.11(0.02) \\ 
  100 & 5 & 491 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 5 & 500 & 0.95 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.05(0.01) \\ 
  30 & 10 & 427 & 0.99 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  50 & 10 & 488 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.09(0.01) \\ 
  100 & 10 & 499 & 0.97 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  200 & 10 & 500 & 0.96 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.04(0.01) \\ 
  30 & 30 & 475 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.1(0.01) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.08(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  200 & 30 & 500 & 0.99 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.04(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 112 & 0.96 & 0.95(0.05) & 0.94(0.06) & 0.02(0.01) & 0.08(0.01) & 0.16(0.02) \\ 
  50 & 5 & 179 & 0.97 & 0.98(0.03) & 0.97(0.03) & 0.01(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 5 & 266 & 0.99 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 5 & 358 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 179 & 0.99 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.13(0.02) \\ 
  50 & 10 & 212 & 0.99 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  100 & 10 & 319 & 0.97 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  200 & 10 & 389 & 0.96 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 216 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.12(0.01) \\ 
  50 & 30 & 280 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.09(0.01) \\ 
  100 & 30 & 346 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 430 & 1 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   C
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 455 & 0.99 & 0.97(0.04) & 0.96(0.05) & 0.01(0.01) & 0.08(0.01) & 0.14(0.02) \\ 
  50 & 5 & 496 & 0.98 & 0.98(0.03) & 0.97(0.03) & 0.01(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 5 & 499 & 0.95 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.07(0.01) \\ 
  200 & 5 & 500 & 0.95 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.05(0.01) \\ 
  30 & 10 & 487 & 1 & 0.99(0.01) & 0.99(0.02) & 0(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  50 & 10 & 500 & 0.99 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0.01) & 0.09(0.01) \\ 
  100 & 10 & 500 & 0.96 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  200 & 10 & 500 & 0.95 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 494 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.11(0.01) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.08(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.06(0.01) \\ 
  200 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.04(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model C, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M1
Estimator:   MLR 

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 160 & 0.11 & 0.78(0.14) & 0.74(0.16) & 0.07(0.02) & 0.08(0.03) & 0.32(0.06) \\ 
  50 & 5 & 150 & 0.15 & 0.88(0.07) & 0.85(0.08) & 0.05(0.01) & 0.06(0.01) & 0.29(0.05) \\ 
  100 & 5 & 133 & 0.05 & 0.91(0.03) & 0.89(0.04) & 0.04(0.01) & 0.05(0.01) & 0.24(0.04) \\ 
  200 & 5 & 121 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.19(0.03) \\ 
  30 & 10 & 191 & 0.04 & 0.86(0.08) & 0.83(0.09) & 0.05(0.02) & 0.06(0.01) & 0.25(0.04) \\ 
  50 & 10 & 224 & 0.01 & 0.9(0.03) & 0.88(0.04) & 0.04(0.01) & 0.05(0.01) & 0.21(0.03) \\ 
  100 & 10 & 276 & 0 & 0.92(0.02) & 0.9(0.03) & 0.03(0.01) & 0.05(0) & 0.16(0.02) \\ 
  200 & 10 & 317 & 0 & 0.92(0.01) & 0.91(0.02) & 0.03(0) & 0.04(0) & 0.12(0.02) \\ 
  30 & 30 & 376 & 0 & 0.9(0.03) & 0.88(0.04) & 0.04(0.01) & 0.05(0.01) & 0.18(0.03) \\ 
  50 & 30 & 423 & 0 & 0.91(0.04) & 0.89(0.05) & 0.04(0.01) & 0.04(0) & 0.14(0.02) \\ 
  100 & 30 & 476 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.1(0.01) \\ 
  200 & 30 & 495 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 133 & 0.12 & 0.88(0.06) & 0.85(0.07) & 0.06(0.02) & 0.08(0.01) & 0.16(0.03) \\ 
  50 & 5 & 160 & 0.15 & 0.92(0.04) & 0.91(0.04) & 0.05(0.01) & 0.07(0.01) & 0.13(0.03) \\ 
  100 & 5 & 137 & 0.02 & 0.95(0.02) & 0.93(0.02) & 0.04(0.01) & 0.06(0.01) & 0.1(0.02) \\ 
  200 & 5 & 125 & 0 & 0.95(0.01) & 0.94(0.02) & 0.03(0.01) & 0.05(0.01) & 0.08(0.01) \\ 
  30 & 10 & 182 & 0.05 & 0.9(0.04) & 0.88(0.05) & 0.05(0.01) & 0.06(0.01) & 0.13(0.03) \\ 
  50 & 10 & 257 & 0.01 & 0.92(0.02) & 0.91(0.03) & 0.04(0.01) & 0.06(0.01) & 0.1(0.02) \\ 
  100 & 10 & 381 & 0 & 0.94(0.01) & 0.92(0.02) & 0.04(0) & 0.05(0) & 0.08(0.01) \\ 
  200 & 10 & 448 & 0 & 0.94(0.01) & 0.93(0.01) & 0.04(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 426 & 0 & 0.91(0.02) & 0.89(0.02) & 0.04(0.01) & 0.05(0.01) & 0.11(0.02) \\ 
  50 & 30 & 488 & 0 & 0.92(0.01) & 0.91(0.02) & 0.04(0) & 0.05(0) & 0.09(0.02) \\ 
  100 & 30 & 499 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 500 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 195 & 0.27 & 0.8(0.12) & 0.76(0.14) & 0.05(0.02) & 0.07(0.02) & 0.23(0.03) \\ 
  50 & 5 & 247 & 0.36 & 0.89(0.06) & 0.87(0.08) & 0.04(0.01) & 0.06(0.01) & 0.19(0.02) \\ 
  100 & 5 & 297 & 0.19 & 0.92(0.04) & 0.9(0.05) & 0.03(0.01) & 0.05(0.01) & 0.14(0.02) \\ 
  200 & 5 & 349 & 0.01 & 0.92(0.02) & 0.9(0.03) & 0.03(0.01) & 0.05(0.01) & 0.1(0.01) \\ 
  30 & 10 & 271 & 0.14 & 0.87(0.06) & 0.84(0.08) & 0.04(0.01) & 0.06(0.01) & 0.19(0.03) \\ 
  50 & 10 & 317 & 0.07 & 0.9(0.04) & 0.88(0.05) & 0.04(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  100 & 10 & 381 & 0.01 & 0.91(0.03) & 0.89(0.03) & 0.03(0.01) & 0.05(0.01) & 0.1(0.01) \\ 
  200 & 10 & 444 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.07(0.01) \\ 
  30 & 30 & 309 & 0 & 0.9(0.03) & 0.88(0.03) & 0.04(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 30 & 369 & 0 & 0.91(0.02) & 0.89(0.02) & 0.03(0) & 0.04(0) & 0.12(0.01) \\ 
  100 & 30 & 445 & 0 & 0.91(0.01) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
  200 & 30 & 471 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 313 & 0.26 & 0.85(0.08) & 0.83(0.09) & 0.05(0.02) & 0.08(0.01) & 0.17(0.03) \\ 
  50 & 5 & 409 & 0.31 & 0.91(0.05) & 0.89(0.06) & 0.04(0.01) & 0.07(0.01) & 0.14(0.02) \\ 
  100 & 5 & 453 & 0.1 & 0.93(0.03) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  200 & 5 & 495 & 0.01 & 0.94(0.02) & 0.93(0.02) & 0.03(0) & 0.05(0.01) & 0.07(0.01) \\ 
  30 & 10 & 439 & 0.12 & 0.89(0.04) & 0.87(0.05) & 0.04(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  50 & 10 & 470 & 0.04 & 0.92(0.03) & 0.9(0.04) & 0.04(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  100 & 10 & 495 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0.01) & 0.09(0.01) \\ 
  200 & 10 & 500 & 0 & 0.93(0.01) & 0.91(0.01) & 0.03(0) & 0.04(0) & 0.07(0.01) \\ 
  30 & 30 & 467 & 0 & 0.91(0.02) & 0.89(0.03) & 0.04(0.01) & 0.05(0) & 0.13(0.02) \\ 
  50 & 30 & 492 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.11(0.02) \\ 
  100 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  200 & 30 & 499 & 0 & 0.92(0.01) & 0.91(0.01) & 0.03(0) & 0.04(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 191 & 0.34 & 0.8(0.1) & 0.76(0.13) & 0.05(0.02) & 0.07(0.01) & 0.19(0.02) \\ 
  50 & 5 & 251 & 0.42 & 0.89(0.07) & 0.87(0.08) & 0.03(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  100 & 5 & 282 & 0.23 & 0.91(0.04) & 0.89(0.05) & 0.03(0.01) & 0.05(0.01) & 0.1(0.01) \\ 
  200 & 5 & 357 & 0.01 & 0.91(0.03) & 0.9(0.03) & 0.03(0.01) & 0.05(0.01) & 0.07(0.01) \\ 
  30 & 10 & 221 & 0.22 & 0.88(0.06) & 0.85(0.08) & 0.04(0.01) & 0.06(0.01) & 0.17(0.02) \\ 
  50 & 10 & 255 & 0.09 & 0.9(0.04) & 0.88(0.05) & 0.03(0.01) & 0.05(0.01) & 0.13(0.01) \\ 
  100 & 10 & 314 & 0 & 0.91(0.03) & 0.9(0.03) & 0.03(0.01) & 0.04(0) & 0.09(0.01) \\ 
  200 & 10 & 370 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
  30 & 30 & 216 & 0 & 0.9(0.03) & 0.88(0.03) & 0.04(0) & 0.05(0.01) & 0.15(0.02) \\ 
  50 & 30 & 266 & 0 & 0.91(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.12(0.01) \\ 
  100 & 30 & 340 & 0 & 0.91(0.03) & 0.9(0.03) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  200 & 30 & 373 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 387 & 0.37 & 0.84(0.09) & 0.8(0.11) & 0.05(0.02) & 0.07(0.01) & 0.17(0.02) \\ 
  50 & 5 & 456 & 0.4 & 0.9(0.06) & 0.88(0.07) & 0.04(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  100 & 5 & 494 & 0.15 & 0.92(0.03) & 0.9(0.04) & 0.03(0.01) & 0.05(0.01) & 0.09(0.01) \\ 
  200 & 5 & 500 & 0 & 0.93(0.02) & 0.91(0.03) & 0.03(0) & 0.05(0.01) & 0.07(0.01) \\ 
  30 & 10 & 444 & 0.2 & 0.88(0.07) & 0.86(0.08) & 0.04(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  50 & 10 & 484 & 0.09 & 0.91(0.04) & 0.89(0.05) & 0.03(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  100 & 10 & 499 & 0 & 0.92(0.02) & 0.9(0.03) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  200 & 10 & 500 & 0 & 0.92(0.02) & 0.91(0.02) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
  30 & 30 & 450 & 0 & 0.91(0.03) & 0.89(0.03) & 0.03(0.01) & 0.04(0) & 0.14(0.02) \\ 
  50 & 30 & 488 & 0 & 0.91(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.11(0.01) \\ 
  100 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  200 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator MLR, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M1
Estimator:   ULSMV 

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 19 & 1 & 0.96(0.05) & 0.95(0.06) & 0.02(0.02) & 0.09(0.01) & 0.52(0.68) \\ 
  50 & 5 & 54 & 0.65 & 0.93(0.05) & 0.91(0.06) & 0.03(0.01) & 0.07(0.01) & 0.32(0.14) \\ 
  100 & 5 & 190 & 0.09 & 0.92(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.22(0.05) \\ 
  200 & 5 & 337 & 0 & 0.92(0.02) & 0.91(0.03) & 0.04(0.01) & 0.05(0.01) & 0.14(0.03) \\ 
  30 & 10 & 154 & 0.44 & 0.93(0.04) & 0.92(0.04) & 0.03(0.01) & 0.07(0.01) & 0.26(0.12) \\ 
  50 & 10 & 236 & 0.05 & 0.92(0.03) & 0.91(0.04) & 0.04(0.01) & 0.06(0.01) & 0.19(0.04) \\ 
  100 & 10 & 361 & 0 & 0.92(0.02) & 0.91(0.03) & 0.04(0.01) & 0.05(0.01) & 0.13(0.02) \\ 
  200 & 10 & 417 & 0 & 0.92(0.01) & 0.91(0.02) & 0.04(0) & 0.05(0) & 0.09(0.01) \\ 
  30 & 30 & 342 & 0.01 & 0.93(0.02) & 0.92(0.02) & 0.03(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  50 & 30 & 393 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.11(0.01) \\ 
  100 & 30 & 439 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.08(0.01) \\ 
  200 & 30 & 472 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 6 & 1 & 0.97(0.05) & 0.96(0.06) & 0.01(0.01) & 0.09(0.02) & 0.13(0.04) \\ 
  50 & 5 & 11 & 0.55 & 0.91(0.06) & 0.89(0.07) & 0.03(0.01) & 0.09(0.01) & 0.1(0.03) \\ 
  100 & 5 & 13 & 0.31 & 0.92(0.04) & 0.9(0.05) & 0.03(0.01) & 0.07(0.01) & 0.07(0.01) \\ 
  200 & 5 & 1 & 0 & 0.95(NA) & 0.94(NA) & 0.02(NA) & 0.06(NA) & 0.04(NA) \\ 
  30 & 10 & 9 & 0.78 & 0.91(0.08) & 0.89(0.1) & 0.02(0.01) & 0.08(0.01) & 0.09(0.02) \\ 
  50 & 10 & 10 & 0.2 & 0.88(0.05) & 0.85(0.06) & 0.03(0.01) & 0.07(0.01) & 0.07(0.02) \\ 
  100 & 10 & 1 & 0 & 0.9(NA) & 0.88(NA) & 0.03(NA) & 0.05(NA) & 0.06(NA) \\ 
  30 & 30 & 4 & 0.5 & 0.92(0.05) & 0.91(0.05) & 0.02(0.01) & 0.06(0) & 0.08(0.03) \\ 
  50 & 30 & 1 & 0 & 0.82(NA) & 0.78(NA) & 0.02(NA) & 0.06(NA) & 0.06(NA) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 168 & 0.93 & 0.91(0.08) & 0.9(0.09) & 0.02(0.01) & 0.09(0.01) & 0.21(0.04) \\ 
  50 & 5 & 244 & 0.76 & 0.93(0.05) & 0.91(0.06) & 0.02(0.01) & 0.07(0.01) & 0.15(0.03) \\ 
  100 & 5 & 334 & 0.3 & 0.93(0.04) & 0.92(0.04) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  200 & 5 & 418 & 0.02 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  30 & 10 & 256 & 0.82 & 0.92(0.05) & 0.91(0.06) & 0.02(0.01) & 0.07(0.01) & 0.15(0.02) \\ 
  50 & 10 & 326 & 0.38 & 0.93(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 10 & 401 & 0.02 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 10 & 462 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 316 & 0.93 & 0.97(0.03) & 0.96(0.03) & 0.01(0.01) & 0.06(0.01) & 0.13(0.01) \\ 
  50 & 30 & 388 & 0.3 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.05(0) & 0.1(0.01) \\ 
  100 & 30 & 459 & 0 & 0.94(0.01) & 0.93(0.01) & 0.02(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 481 & 0 & 0.94(0.01) & 0.93(0.01) & 0.02(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 263 & 0.97 & 0.91(0.09) & 0.9(0.11) & 0.02(0.01) & 0.1(0.01) & 0.13(0.03) \\ 
  50 & 5 & 329 & 0.79 & 0.91(0.07) & 0.89(0.08) & 0.02(0.01) & 0.09(0.01) & 0.1(0.02) \\ 
  100 & 5 & 354 & 0.4 & 0.92(0.04) & 0.9(0.05) & 0.03(0.01) & 0.07(0.01) & 0.07(0.01) \\ 
  200 & 5 & 411 & 0.01 & 0.92(0.03) & 0.9(0.03) & 0.03(0) & 0.06(0.01) & 0.05(0.01) \\ 
  30 & 10 & 323 & 0.95 & 0.93(0.07) & 0.91(0.09) & 0.02(0.01) & 0.08(0.01) & 0.11(0.02) \\ 
  50 & 10 & 329 & 0.67 & 0.91(0.06) & 0.89(0.07) & 0.02(0.01) & 0.07(0.01) & 0.08(0.01) \\ 
  100 & 10 & 385 & 0.08 & 0.91(0.04) & 0.89(0.04) & 0.02(0) & 0.06(0.01) & 0.06(0.01) \\ 
  200 & 10 & 429 & 0 & 0.9(0.02) & 0.88(0.03) & 0.02(0) & 0.06(0) & 0.05(0.01) \\ 
  30 & 30 & 333 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.07(0.01) & 0.09(0.01) \\ 
  50 & 30 & 397 & 0.98 & 0.98(0.03) & 0.98(0.03) & 0(0) & 0.06(0.01) & 0.07(0.01) \\ 
  100 & 30 & 417 & 0.11 & 0.93(0.03) & 0.92(0.03) & 0.01(0) & 0.06(0) & 0.05(0.01) \\ 
  200 & 30 & 458 & 0 & 0.9(0.02) & 0.88(0.02) & 0.02(0) & 0.05(0) & 0.04(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:49 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 162 & 0.97 & 0.88(0.13) & 0.85(0.16) & 0.02(0.01) & 0.1(0.02) & 0.15(0.02) \\ 
  50 & 5 & 246 & 0.94 & 0.91(0.08) & 0.89(0.1) & 0.02(0.01) & 0.08(0.01) & 0.11(0.01) \\ 
  100 & 5 & 292 & 0.77 & 0.93(0.05) & 0.92(0.06) & 0.02(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 5 & 350 & 0.34 & 0.94(0.03) & 0.93(0.03) & 0.02(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 238 & 0.99 & 0.95(0.07) & 0.94(0.09) & 0.01(0.01) & 0.07(0.01) & 0.13(0.01) \\ 
  50 & 10 & 257 & 0.93 & 0.94(0.06) & 0.92(0.07) & 0.01(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 10 & 325 & 0.66 & 0.94(0.04) & 0.93(0.04) & 0.01(0.01) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 379 & 0.12 & 0.94(0.02) & 0.93(0.02) & 0.02(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 239 & 1 & 1(0) & 1(0) & 0(0) & 0.06(0.01) & 0.12(0.01) \\ 
  50 & 30 & 300 & 1 & 1(0) & 1(0.01) & 0(0) & 0.05(0) & 0.09(0.01) \\ 
  100 & 30 & 361 & 0.99 & 0.98(0.02) & 0.98(0.02) & 0(0) & 0.05(0) & 0.06(0.01) \\ 
  200 & 30 & 389 & 0.23 & 0.96(0.01) & 0.95(0.02) & 0.01(0) & 0.05(0) & 0.05(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 386 & 0.99 & 0.91(0.11) & 0.89(0.13) & 0.01(0.01) & 0.12(0.02) & 0.12(0.02) \\ 
  50 & 5 & 457 & 0.95 & 0.92(0.08) & 0.9(0.1) & 0.02(0.01) & 0.09(0.01) & 0.09(0.01) \\ 
  100 & 5 & 494 & 0.73 & 0.93(0.05) & 0.91(0.06) & 0.02(0.01) & 0.08(0.01) & 0.06(0.01) \\ 
  200 & 5 & 500 & 0.27 & 0.93(0.03) & 0.92(0.03) & 0.02(0) & 0.07(0.01) & 0.05(0.01) \\ 
  30 & 10 & 445 & 1 & 0.98(0.06) & 0.97(0.07) & 0(0.01) & 0.09(0.01) & 0.1(0.02) \\ 
  50 & 10 & 477 & 0.97 & 0.95(0.06) & 0.94(0.07) & 0.01(0.01) & 0.08(0.01) & 0.08(0.01) \\ 
  100 & 10 & 497 & 0.68 & 0.94(0.04) & 0.92(0.05) & 0.01(0.01) & 0.07(0.01) & 0.06(0.01) \\ 
  200 & 10 & 500 & 0.12 & 0.93(0.03) & 0.92(0.03) & 0.02(0) & 0.06(0.01) & 0.04(0) \\ 
  30 & 30 & 453 & 1 & 1(0) & 1(0) & 0(0) & 0.08(0.01) & 0.09(0.01) \\ 
  50 & 30 & 488 & 1 & 1(0) & 1(0) & 0(0) & 0.07(0.01) & 0.07(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.06(0.01) & 0.05(0.01) \\ 
  200 & 30 & 500 & 0.68 & 0.97(0.02) & 0.96(0.02) & 0.01(0) & 0.06(0) & 0.04(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M1
Estimator:   WLSMV 

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 20 & 0.9 & 0.94(0.06) & 0.93(0.07) & 0.02(0.02) & 0.09(0.01) & 0.33(0.04) \\ 
  50 & 5 & 63 & 0.68 & 0.93(0.05) & 0.91(0.06) & 0.03(0.01) & 0.07(0.01) & 0.28(0.05) \\ 
  100 & 5 & 201 & 0.2 & 0.93(0.04) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.21(0.04) \\ 
  200 & 5 & 354 & 0 & 0.93(0.02) & 0.91(0.03) & 0.03(0.01) & 0.05(0.01) & 0.14(0.02) \\ 
  30 & 10 & 144 & 0.51 & 0.93(0.04) & 0.92(0.05) & 0.03(0.01) & 0.07(0.01) & 0.3(0.69) \\ 
  50 & 10 & 280 & 0.15 & 0.93(0.03) & 0.91(0.03) & 0.03(0.01) & 0.06(0.01) & 0.19(0.04) \\ 
  100 & 10 & 370 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0.01) & 0.12(0.02) \\ 
  200 & 10 & 437 & 0 & 0.93(0.01) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.09(0.01) \\ 
  30 & 30 & 339 & 0.01 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.05(0.01) & 0.15(0.02) \\ 
  50 & 30 & 414 & 0 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.11(0.01) \\ 
  100 & 30 & 449 & 0 & 0.93(0.01) & 0.91(0.01) & 0.03(0) & 0.05(0) & 0.08(0.01) \\ 
  200 & 30 & 481 & 0 & 0.93(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 4 & 1 & 0.95(0.05) & 0.94(0.06) & 0.02(0.02) & 0.08(0.01) & 0.14(0.05) \\ 
  50 & 5 & 3 & 0 & 0.86(0.01) & 0.83(0.01) & 0.04(0) & 0.09(0.01) & 0.14(0.07) \\ 
  100 & 5 & 5 & 0.4 & 0.93(0.04) & 0.91(0.05) & 0.03(0.01) & 0.06(0.01) & 0.08(0.02) \\ 
  30 & 10 & 5 & 0.4 & 0.91(0.06) & 0.9(0.07) & 0.03(0.01) & 0.07(0.01) & 0.14(0.03) \\ 
  50 & 10 & 3 & 0 & 0.91(0.04) & 0.89(0.05) & 0.03(0.01) & 0.07(0.02) & 0.1(0.03) \\ 
  30 & 30 & 2 & 0.5 & 0.96(0.03) & 0.95(0.04) & 0.02(0.01) & 0.05(0.01) & 0.08(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 114 & 0.87 & 0.92(0.07) & 0.9(0.08) & 0.03(0.02) & 0.09(0.01) & 0.2(0.03) \\ 
  50 & 5 & 191 & 0.59 & 0.92(0.05) & 0.91(0.06) & 0.03(0.01) & 0.07(0.01) & 0.15(0.02) \\ 
  100 & 5 & 311 & 0.21 & 0.93(0.03) & 0.92(0.04) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  200 & 5 & 408 & 0 & 0.93(0.02) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.07(0.01) \\ 
  30 & 10 & 196 & 0.51 & 0.92(0.05) & 0.91(0.05) & 0.03(0.01) & 0.07(0.01) & 0.15(0.02) \\ 
  50 & 10 & 290 & 0.13 & 0.93(0.03) & 0.91(0.03) & 0.03(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 10 & 379 & 0.01 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 10 & 460 & 0 & 0.94(0.01) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 271 & 0.12 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.06(0.01) & 0.13(0.01) \\ 
  50 & 30 & 371 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.05(0) & 0.1(0.01) \\ 
  100 & 30 & 446 & 0 & 0.94(0.01) & 0.92(0.01) & 0.03(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 483 & 0 & 0.94(0.01) & 0.92(0.01) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 226 & 0.81 & 0.91(0.07) & 0.9(0.08) & 0.03(0.02) & 0.09(0.01) & 0.15(0.03) \\ 
  50 & 5 & 245 & 0.56 & 0.92(0.06) & 0.9(0.07) & 0.03(0.01) & 0.08(0.01) & 0.11(0.02) \\ 
  100 & 5 & 231 & 0.18 & 0.93(0.04) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 5 & 222 & 0 & 0.93(0.02) & 0.91(0.03) & 0.03(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 246 & 0.56 & 0.93(0.04) & 0.91(0.05) & 0.03(0.01) & 0.07(0.01) & 0.13(0.02) \\ 
  50 & 10 & 219 & 0.15 & 0.92(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  100 & 10 & 174 & 0 & 0.92(0.03) & 0.9(0.03) & 0.03(0.01) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 140 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 199 & 0.8 & 0.97(0.02) & 0.97(0.02) & 0.01(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  50 & 30 & 185 & 0.04 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.05(0.01) & 0.08(0.01) \\ 
  100 & 30 & 129 & 0 & 0.93(0.01) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  200 & 30 & 83 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 127 & 0.82 & 0.91(0.07) & 0.89(0.08) & 0.03(0.02) & 0.09(0.01) & 0.15(0.02) \\ 
  50 & 5 & 197 & 0.71 & 0.93(0.05) & 0.91(0.06) & 0.03(0.01) & 0.08(0.01) & 0.11(0.01) \\ 
  100 & 5 & 268 & 0.28 & 0.94(0.03) & 0.92(0.04) & 0.03(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 5 & 330 & 0.01 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 178 & 0.8 & 0.95(0.04) & 0.93(0.05) & 0.02(0.01) & 0.07(0.01) & 0.13(0.02) \\ 
  50 & 10 & 216 & 0.3 & 0.94(0.03) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 10 & 288 & 0 & 0.94(0.02) & 0.92(0.02) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 370 & 0 & 0.94(0.01) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 201 & 0.99 & 0.99(0.01) & 0.99(0.02) & 0(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  50 & 30 & 271 & 0.37 & 0.97(0.01) & 0.96(0.02) & 0.01(0) & 0.05(0.01) & 0.09(0.01) \\ 
  100 & 30 & 336 & 0 & 0.95(0.01) & 0.94(0.01) & 0.02(0) & 0.05(0) & 0.06(0.01) \\ 
  200 & 30 & 371 & 0 & 0.94(0.01) & 0.93(0.01) & 0.03(0) & 0.05(0) & 0.05(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M1
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 361 & 0.89 & 0.93(0.06) & 0.91(0.07) & 0.03(0.01) & 0.09(0.01) & 0.14(0.02) \\ 
  50 & 5 & 400 & 0.66 & 0.93(0.05) & 0.91(0.06) & 0.03(0.01) & 0.08(0.01) & 0.1(0.01) \\ 
  100 & 5 & 463 & 0.2 & 0.93(0.03) & 0.91(0.04) & 0.03(0.01) & 0.07(0.01) & 0.07(0.01) \\ 
  200 & 5 & 481 & 0 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.05(0.01) \\ 
  30 & 10 & 399 & 0.88 & 0.96(0.04) & 0.95(0.05) & 0.02(0.01) & 0.07(0.01) & 0.12(0.02) \\ 
  50 & 10 & 445 & 0.4 & 0.94(0.03) & 0.93(0.04) & 0.03(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  100 & 10 & 457 & 0.01 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 496 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 352 & 1 & 1(0) & 1(0.01) & 0(0) & 0.06(0.01) & 0.11(0.01) \\ 
  50 & 30 & 420 & 0.86 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  100 & 30 & 473 & 0 & 0.95(0.01) & 0.94(0.01) & 0.02(0) & 0.05(0) & 0.06(0.01) \\ 
  200 & 30 & 496 & 0 & 0.94(0.01) & 0.93(0.01) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M1, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M2
Estimator:   MLR 

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 248 & 0.35 & 0.87(0.11) & 0.84(0.12) & 0.05(0.02) & 0.06(0.03) & 0.33(0.06) \\ 
  50 & 5 & 281 & 0.52 & 0.94(0.04) & 0.93(0.05) & 0.03(0.02) & 0.05(0.01) & 0.28(0.04) \\ 
  100 & 5 & 382 & 0.69 & 0.98(0.02) & 0.97(0.03) & 0.02(0.01) & 0.03(0) & 0.23(0.03) \\ 
  200 & 5 & 463 & 0.84 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.18(0.03) \\ 
  30 & 10 & 290 & 0.42 & 0.94(0.04) & 0.92(0.05) & 0.03(0.01) & 0.04(0.01) & 0.26(0.04) \\ 
  50 & 10 & 401 & 0.6 & 0.97(0.02) & 0.96(0.03) & 0.02(0.01) & 0.03(0) & 0.22(0.03) \\ 
  100 & 10 & 483 & 0.81 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.17(0.02) \\ 
  200 & 10 & 500 & 0.79 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.12(0.02) \\ 
  30 & 30 & 468 & 0.56 & 0.98(0.01) & 0.98(0.02) & 0.02(0.01) & 0.02(0) & 0.2(0.03) \\ 
  50 & 30 & 498 & 0.72 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.15(0.02) \\ 
  100 & 30 & 500 & 0.74 & 1(0) & 0.99(0) & 0.01(0) & 0.01(0) & 0.12(0.01) \\ 
  200 & 30 & 500 & 0.59 & 1(0) & 1(0) & 0.01(0) & 0.01(0) & 0.09(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 191 & 0.2 & 0.89(0.06) & 0.87(0.07) & 0.06(0.02) & 0.07(0.01) & 0.21(0.05) \\ 
  50 & 5 & 275 & 0.2 & 0.93(0.03) & 0.91(0.04) & 0.04(0.01) & 0.05(0.01) & 0.18(0.03) \\ 
  100 & 5 & 386 & 0.09 & 0.95(0.02) & 0.94(0.03) & 0.03(0.01) & 0.04(0) & 0.16(0.03) \\ 
  200 & 5 & 487 & 0 & 0.96(0.01) & 0.95(0.02) & 0.03(0.01) & 0.03(0) & 0.14(0.02) \\ 
  30 & 10 & 265 & 0.15 & 0.92(0.04) & 0.91(0.04) & 0.04(0.01) & 0.04(0.01) & 0.17(0.04) \\ 
  50 & 10 & 403 & 0.09 & 0.94(0.02) & 0.93(0.03) & 0.03(0.01) & 0.04(0) & 0.16(0.03) \\ 
  100 & 10 & 495 & 0 & 0.96(0.01) & 0.95(0.01) & 0.03(0) & 0.03(0) & 0.14(0.02) \\ 
  200 & 10 & 500 & 0 & 0.96(0.01) & 0.95(0.01) & 0.03(0) & 0.02(0) & 0.13(0.01) \\ 
  30 & 30 & 476 & 0.08 & 0.96(0.02) & 0.95(0.02) & 0.03(0.01) & 0.02(0) & 0.16(0.03) \\ 
  50 & 30 & 500 & 0.03 & 0.97(0.01) & 0.97(0.01) & 0.02(0) & 0.02(0) & 0.15(0.02) \\ 
  100 & 30 & 500 & 0 & 0.98(0.01) & 0.97(0.01) & 0.02(0) & 0.01(0) & 0.14(0.02) \\ 
  200 & 30 & 500 & 0 & 0.98(0) & 0.97(0) & 0.02(0) & 0.01(0) & 0.13(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 219 & 0.47 & 0.87(0.09) & 0.85(0.11) & 0.04(0.02) & 0.06(0.01) & 0.23(0.03) \\ 
  50 & 5 & 269 & 0.78 & 0.95(0.05) & 0.94(0.06) & 0.02(0.02) & 0.05(0.01) & 0.19(0.02) \\ 
  100 & 5 & 351 & 0.88 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.03(0) & 0.14(0.02) \\ 
  200 & 5 & 430 & 0.87 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.1(0.01) \\ 
  30 & 10 & 287 & 0.63 & 0.94(0.04) & 0.93(0.05) & 0.02(0.01) & 0.04(0.01) & 0.2(0.02) \\ 
  50 & 10 & 347 & 0.77 & 0.98(0.02) & 0.97(0.03) & 0.01(0.01) & 0.03(0) & 0.16(0.02) \\ 
  100 & 10 & 429 & 0.87 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.11(0.01) \\ 
  200 & 10 & 486 & 0.88 & 1(0) & 1(0.01) & 0.01(0) & 0.02(0) & 0.08(0.01) \\ 
  30 & 30 & 337 & 0.71 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.16(0.02) \\ 
  50 & 30 & 413 & 0.8 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.13(0.01) \\ 
  100 & 30 & 481 & 0.88 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.09(0.01) \\ 
  200 & 30 & 496 & 0.88 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 411 & 0.37 & 0.88(0.07) & 0.86(0.08) & 0.05(0.02) & 0.07(0.01) & 0.19(0.03) \\ 
  50 & 5 & 489 & 0.52 & 0.94(0.04) & 0.93(0.05) & 0.03(0.02) & 0.05(0.01) & 0.16(0.02) \\ 
  100 & 5 & 500 & 0.46 & 0.96(0.02) & 0.96(0.03) & 0.02(0.01) & 0.04(0) & 0.12(0.02) \\ 
  200 & 5 & 500 & 0.14 & 0.97(0.01) & 0.96(0.02) & 0.02(0.01) & 0.03(0) & 0.11(0.01) \\ 
  30 & 10 & 493 & 0.47 & 0.94(0.04) & 0.92(0.05) & 0.03(0.01) & 0.04(0.01) & 0.17(0.03) \\ 
  50 & 10 & 500 & 0.5 & 0.96(0.02) & 0.96(0.03) & 0.02(0.01) & 0.03(0) & 0.14(0.02) \\ 
  100 & 10 & 500 & 0.26 & 0.97(0.01) & 0.97(0.02) & 0.02(0.01) & 0.02(0) & 0.11(0.01) \\ 
  200 & 10 & 500 & 0.02 & 0.98(0.01) & 0.97(0.01) & 0.02(0) & 0.02(0) & 0.1(0.01) \\ 
  30 & 30 & 500 & 0.49 & 0.98(0.02) & 0.97(0.02) & 0.02(0.01) & 0.02(0) & 0.16(0.02) \\ 
  50 & 30 & 498 & 0.43 & 0.98(0.01) & 0.98(0.01) & 0.01(0.01) & 0.02(0) & 0.13(0.02) \\ 
  100 & 30 & 499 & 0.19 & 0.99(0.01) & 0.99(0.01) & 0.01(0) & 0.01(0) & 0.11(0.01) \\ 
  200 & 30 & 498 & 0 & 0.99(0) & 0.99(0) & 0.01(0) & 0.01(0) & 0.1(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 177 & 0.57 & 0.87(0.1) & 0.84(0.12) & 0.04(0.02) & 0.06(0.01) & 0.19(0.02) \\ 
  50 & 5 & 238 & 0.83 & 0.95(0.05) & 0.94(0.05) & 0.02(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  100 & 5 & 288 & 0.92 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.11(0.01) \\ 
  200 & 5 & 366 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.07(0.01) \\ 
  30 & 10 & 228 & 0.75 & 0.95(0.04) & 0.94(0.05) & 0.02(0.01) & 0.04(0.01) & 0.17(0.02) \\ 
  50 & 10 & 240 & 0.85 & 0.98(0.02) & 0.97(0.03) & 0.01(0.01) & 0.03(0) & 0.13(0.01) \\ 
  100 & 10 & 321 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.09(0.01) \\ 
  200 & 10 & 393 & 0.92 & 1(0) & 1(0.01) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  30 & 30 & 242 & 0.69 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.16(0.02) \\ 
  50 & 30 & 281 & 0.87 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.12(0.01) \\ 
  100 & 30 & 359 & 0.91 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.08(0.01) \\ 
  200 & 30 & 416 & 0.94 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 472 & 0.51 & 0.88(0.09) & 0.85(0.1) & 0.04(0.02) & 0.06(0.01) & 0.18(0.03) \\ 
  50 & 5 & 498 & 0.73 & 0.95(0.05) & 0.94(0.06) & 0.02(0.02) & 0.05(0.01) & 0.14(0.02) \\ 
  100 & 5 & 500 & 0.67 & 0.97(0.03) & 0.96(0.03) & 0.02(0.01) & 0.04(0) & 0.11(0.01) \\ 
  200 & 5 & 500 & 0.48 & 0.98(0.01) & 0.97(0.02) & 0.02(0.01) & 0.03(0) & 0.08(0.01) \\ 
  30 & 10 & 491 & 0.64 & 0.94(0.04) & 0.93(0.05) & 0.02(0.01) & 0.04(0.01) & 0.16(0.02) \\ 
  50 & 10 & 499 & 0.71 & 0.97(0.03) & 0.96(0.03) & 0.02(0.01) & 0.03(0) & 0.13(0.02) \\ 
  100 & 10 & 500 & 0.64 & 0.98(0.01) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.1(0.01) \\ 
  200 & 10 & 500 & 0.39 & 0.99(0.01) & 0.98(0.01) & 0.01(0) & 0.02(0) & 0.08(0.01) \\ 
  30 & 30 & 499 & 0.62 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.02(0) & 0.15(0.02) \\ 
  50 & 30 & 500 & 0.7 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.02(0) & 0.12(0.02) \\ 
  100 & 30 & 500 & 0.56 & 0.99(0) & 0.99(0.01) & 0.01(0) & 0.01(0) & 0.1(0.01) \\ 
  200 & 30 & 500 & 0.32 & 0.99(0) & 0.99(0) & 0.01(0) & 0.01(0) & 0.08(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator MLR, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M2
Estimator:   ULSMV 

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 23 & 1 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.07(0.01) & 0.36(0.06) \\ 
  50 & 5 & 77 & 0.99 & 0.99(0.02) & 0.98(0.03) & 0.01(0.01) & 0.06(0.01) & 0.31(0.06) \\ 
  100 & 5 & 257 & 0.94 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.22(0.05) \\ 
  200 & 5 & 460 & 0.93 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.15(0.03) \\ 
  30 & 10 & 186 & 1 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.26(0.04) \\ 
  50 & 10 & 348 & 0.98 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0) & 0.2(0.04) \\ 
  100 & 10 & 477 & 0.94 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.14(0.02) \\ 
  200 & 10 & 499 & 0.87 & 1(0) & 1(0) & 0.01(0) & 0.02(0) & 0.1(0.01) \\ 
  30 & 30 & 461 & 0.99 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.16(0.02) \\ 
  50 & 30 & 497 & 0.96 & 1(0) & 1(0) & 0.01(0) & 0.02(0) & 0.12(0.02) \\ 
  100 & 30 & 500 & 0.84 & 1(0) & 1(0) & 0.01(0) & 0.01(0) & 0.09(0.01) \\ 
  200 & 30 & 500 & 0.62 & 1(0) & 1(0) & 0.01(0) & 0.01(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 24 & 1 & 0.96(0.06) & 0.95(0.07) & 0.01(0.01) & 0.08(0.01) & 0.15(0.04) \\ 
  50 & 5 & 71 & 0.79 & 0.93(0.06) & 0.91(0.07) & 0.02(0.01) & 0.07(0.01) & 0.13(0.03) \\ 
  100 & 5 & 213 & 0.25 & 0.92(0.05) & 0.9(0.06) & 0.03(0.01) & 0.05(0.01) & 0.11(0.02) \\ 
  200 & 5 & 410 & 0 & 0.9(0.04) & 0.88(0.04) & 0.03(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  30 & 10 & 101 & 0.87 & 0.93(0.08) & 0.91(0.1) & 0.02(0.01) & 0.06(0.01) & 0.13(0.03) \\ 
  50 & 10 & 234 & 0.34 & 0.89(0.06) & 0.87(0.07) & 0.03(0.01) & 0.05(0.01) & 0.11(0.02) \\ 
  100 & 10 & 416 & 0 & 0.87(0.05) & 0.84(0.06) & 0.03(0.01) & 0.04(0.01) & 0.1(0.02) \\ 
  200 & 10 & 491 & 0 & 0.85(0.04) & 0.82(0.05) & 0.03(0) & 0.03(0) & 0.09(0.01) \\ 
  30 & 30 & 299 & 0.88 & 0.95(0.05) & 0.94(0.06) & 0.01(0.01) & 0.04(0.01) & 0.11(0.02) \\ 
  50 & 30 & 425 & 0.12 & 0.87(0.06) & 0.85(0.07) & 0.02(0) & 0.03(0.01) & 0.1(0.02) \\ 
  100 & 30 & 482 & 0 & 0.81(0.06) & 0.77(0.07) & 0.03(0) & 0.03(0.01) & 0.09(0.01) \\ 
  200 & 30 & 500 & 0 & 0.77(0.05) & 0.73(0.06) & 0.03(0) & 0.03(0) & 0.09(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 183 & 0.99 & 0.96(0.06) & 0.95(0.07) & 0.01(0.01) & 0.08(0.01) & 0.21(0.04) \\ 
  50 & 5 & 275 & 0.97 & 0.97(0.04) & 0.97(0.04) & 0.01(0.01) & 0.06(0.01) & 0.16(0.02) \\ 
  100 & 5 & 368 & 0.97 & 0.99(0.02) & 0.98(0.02) & 0.01(0.01) & 0.04(0.01) & 0.11(0.01) \\ 
  200 & 5 & 444 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  30 & 10 & 292 & 0.99 & 0.98(0.03) & 0.97(0.04) & 0.01(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 10 & 344 & 0.98 & 0.98(0.02) & 0.98(0.02) & 0.01(0.01) & 0.04(0) & 0.12(0.01) \\ 
  100 & 10 & 443 & 0.95 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  200 & 10 & 488 & 0.92 & 1(0) & 1(0.01) & 0.01(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 338 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.03(0) & 0.13(0.01) \\ 
  50 & 30 & 414 & 0.99 & 1(0.01) & 0.99(0.01) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 483 & 0.96 & 1(0.01) & 1(0.01) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 496 & 0.87 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 331 & 0.98 & 0.93(0.08) & 0.92(0.09) & 0.02(0.01) & 0.09(0.01) & 0.14(0.03) \\ 
  50 & 5 & 444 & 0.84 & 0.93(0.06) & 0.92(0.08) & 0.02(0.01) & 0.07(0.01) & 0.11(0.02) \\ 
  100 & 5 & 488 & 0.46 & 0.93(0.05) & 0.91(0.06) & 0.02(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  200 & 5 & 499 & 0.05 & 0.92(0.03) & 0.9(0.04) & 0.03(0.01) & 0.04(0.01) & 0.07(0.01) \\ 
  30 & 10 & 448 & 0.96 & 0.95(0.07) & 0.93(0.08) & 0.01(0.01) & 0.07(0.01) & 0.12(0.02) \\ 
  50 & 10 & 480 & 0.72 & 0.93(0.06) & 0.91(0.07) & 0.02(0.01) & 0.05(0.01) & 0.09(0.02) \\ 
  100 & 10 & 497 & 0.17 & 0.91(0.05) & 0.89(0.06) & 0.02(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 10 & 500 & 0 & 0.9(0.03) & 0.89(0.04) & 0.02(0) & 0.04(0.01) & 0.07(0.01) \\ 
  30 & 30 & 465 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.05(0.01) & 0.1(0.02) \\ 
  50 & 30 & 490 & 0.99 & 0.99(0.03) & 0.98(0.03) & 0(0) & 0.04(0.01) & 0.08(0.01) \\ 
  100 & 30 & 496 & 0.26 & 0.93(0.04) & 0.92(0.04) & 0.01(0) & 0.03(0.01) & 0.07(0.01) \\ 
  200 & 30 & 500 & 0 & 0.9(0.03) & 0.88(0.03) & 0.02(0) & 0.03(0.01) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 165 & 0.96 & 0.9(0.13) & 0.88(0.16) & 0.01(0.01) & 0.09(0.02) & 0.15(0.02) \\ 
  50 & 5 & 243 & 0.97 & 0.95(0.07) & 0.94(0.08) & 0.01(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 5 & 291 & 0.97 & 0.97(0.03) & 0.97(0.04) & 0.01(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 5 & 378 & 0.94 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 235 & 1 & 0.97(0.06) & 0.97(0.07) & 0(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  50 & 10 & 247 & 0.99 & 0.97(0.04) & 0.96(0.05) & 0.01(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  100 & 10 & 327 & 0.95 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  200 & 10 & 399 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 242 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0.01) & 0.12(0.01) \\ 
  50 & 30 & 283 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.09(0.01) \\ 
  100 & 30 & 359 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 416 & 0.99 & 1(0.01) & 1(0.01) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 421 & 0.99 & 0.91(0.11) & 0.89(0.13) & 0.01(0.01) & 0.11(0.02) & 0.12(0.02) \\ 
  50 & 5 & 465 & 0.95 & 0.93(0.08) & 0.92(0.1) & 0.01(0.01) & 0.08(0.01) & 0.09(0.01) \\ 
  100 & 5 & 483 & 0.74 & 0.93(0.05) & 0.92(0.06) & 0.02(0.01) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 5 & 498 & 0.33 & 0.93(0.04) & 0.92(0.05) & 0.02(0.01) & 0.05(0.01) & 0.06(0.01) \\ 
  30 & 10 & 457 & 1 & 0.98(0.05) & 0.98(0.06) & 0(0.01) & 0.08(0.02) & 0.11(0.02) \\ 
  50 & 10 & 473 & 0.96 & 0.96(0.06) & 0.95(0.07) & 0.01(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  100 & 10 & 488 & 0.71 & 0.94(0.05) & 0.93(0.06) & 0.01(0.01) & 0.05(0.01) & 0.06(0.01) \\ 
  200 & 10 & 499 & 0.19 & 0.93(0.03) & 0.92(0.04) & 0.02(0) & 0.04(0.01) & 0.05(0.01) \\ 
  30 & 30 & 462 & 1 & 1(0) & 1(0) & 0(0) & 0.06(0.02) & 0.1(0.01) \\ 
  50 & 30 & 485 & 1 & 1(0) & 1(0) & 0(0) & 0.05(0.01) & 0.08(0.01) \\ 
  100 & 30 & 491 & 1 & 1(0.01) & 1(0.01) & 0(0) & 0.04(0.01) & 0.06(0.01) \\ 
  200 & 30 & 500 & 0.65 & 0.97(0.03) & 0.96(0.03) & 0.01(0) & 0.03(0.01) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M2
Estimator:   WLSMV 

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 29 & 1 & 0.98(0.03) & 0.98(0.04) & 0.01(0.01) & 0.07(0.01) & 0.35(0.06) \\ 
  50 & 5 & 85 & 1 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.06(0.01) & 0.3(0.06) \\ 
  100 & 5 & 275 & 0.93 & 0.99(0.02) & 0.98(0.02) & 0.01(0.01) & 0.04(0.01) & 0.22(0.04) \\ 
  200 & 5 & 460 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.15(0.03) \\ 
  30 & 10 & 189 & 1 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.25(0.04) \\ 
  50 & 10 & 363 & 0.96 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0) & 0.19(0.03) \\ 
  100 & 10 & 483 & 0.93 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.13(0.02) \\ 
  200 & 10 & 500 & 0.83 & 1(0) & 0.99(0.01) & 0.01(0) & 0.02(0) & 0.1(0.01) \\ 
  30 & 30 & 457 & 0.98 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.16(0.02) \\ 
  50 & 30 & 497 & 0.95 & 1(0) & 0.99(0.01) & 0.01(0) & 0.02(0) & 0.12(0.02) \\ 
  100 & 30 & 500 & 0.79 & 1(0) & 1(0) & 0.01(0) & 0.01(0) & 0.09(0.01) \\ 
  200 & 30 & 500 & 0.49 & 1(0) & 1(0) & 0.01(0) & 0.01(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 22 & 1 & 0.97(0.03) & 0.97(0.04) & 0.01(0.01) & 0.07(0.01) & 0.19(0.05) \\ 
  50 & 5 & 47 & 0.74 & 0.95(0.04) & 0.94(0.05) & 0.02(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  100 & 5 & 164 & 0.43 & 0.95(0.03) & 0.94(0.03) & 0.02(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  200 & 5 & 358 & 0.01 & 0.94(0.02) & 0.93(0.02) & 0.03(0.01) & 0.03(0) & 0.11(0.01) \\ 
  30 & 10 & 59 & 0.97 & 0.97(0.02) & 0.97(0.03) & 0.02(0.01) & 0.05(0.01) & 0.15(0.03) \\ 
  50 & 10 & 136 & 0.7 & 0.97(0.02) & 0.96(0.03) & 0.02(0.01) & 0.04(0.01) & 0.13(0.02) \\ 
  100 & 10 & 307 & 0.05 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.03(0) & 0.11(0.01) \\ 
  200 & 10 & 461 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.02(0) & 0.1(0.01) \\ 
  30 & 30 & 131 & 0.97 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.13(0.02) \\ 
  50 & 30 & 274 & 0.6 & 0.98(0.01) & 0.98(0.01) & 0.01(0) & 0.02(0) & 0.12(0.01) \\ 
  100 & 30 & 419 & 0 & 0.97(0.01) & 0.97(0.01) & 0.02(0) & 0.02(0) & 0.11(0.01) \\ 
  200 & 30 & 498 & 0 & 0.97(0.01) & 0.96(0.01) & 0.02(0) & 0.01(0) & 0.11(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 118 & 0.97 & 0.96(0.04) & 0.96(0.05) & 0.02(0.01) & 0.08(0.01) & 0.21(0.04) \\ 
  50 & 5 & 212 & 0.95 & 0.98(0.03) & 0.97(0.04) & 0.01(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  100 & 5 & 341 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.11(0.01) \\ 
  200 & 5 & 425 & 0.92 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  30 & 10 & 205 & 0.97 & 0.98(0.02) & 0.98(0.03) & 0.01(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 10 & 307 & 0.98 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0) & 0.12(0.01) \\ 
  100 & 10 & 423 & 0.94 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  200 & 10 & 487 & 0.92 & 1(0) & 1(0) & 0.01(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 319 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.13(0.01) \\ 
  50 & 30 & 410 & 0.99 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 478 & 0.96 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 497 & 0.91 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 347 & 0.94 & 0.95(0.05) & 0.94(0.06) & 0.02(0.01) & 0.08(0.01) & 0.16(0.03) \\ 
  50 & 5 & 455 & 0.89 & 0.96(0.03) & 0.95(0.04) & 0.02(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  100 & 5 & 498 & 0.61 & 0.96(0.02) & 0.96(0.03) & 0.02(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  200 & 5 & 500 & 0.14 & 0.96(0.02) & 0.96(0.02) & 0.02(0.01) & 0.03(0) & 0.08(0.01) \\ 
  30 & 10 & 454 & 0.97 & 0.98(0.02) & 0.97(0.03) & 0.01(0.01) & 0.05(0.01) & 0.14(0.02) \\ 
  50 & 10 & 495 & 0.85 & 0.98(0.02) & 0.97(0.02) & 0.02(0.01) & 0.04(0.01) & 0.11(0.02) \\ 
  100 & 10 & 500 & 0.34 & 0.97(0.01) & 0.97(0.02) & 0.02(0.01) & 0.03(0) & 0.09(0.01) \\ 
  200 & 10 & 500 & 0.01 & 0.97(0.01) & 0.97(0.01) & 0.02(0) & 0.02(0) & 0.08(0.01) \\ 
  30 & 30 & 485 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.12(0.02) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 500 & 0.52 & 0.99(0) & 0.99(0.01) & 0.01(0) & 0.02(0) & 0.09(0.01) \\ 
  200 & 30 & 500 & 0 & 0.99(0) & 0.99(0) & 0.01(0) & 0.01(0) & 0.08(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 105 & 0.95 & 0.96(0.05) & 0.95(0.06) & 0.02(0.01) & 0.08(0.01) & 0.16(0.02) \\ 
  50 & 5 & 196 & 0.97 & 0.98(0.03) & 0.97(0.03) & 0.01(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 5 & 266 & 0.97 & 0.99(0.01) & 0.99(0.02) & 0.01(0.01) & 0.04(0.01) & 0.08(0.01) \\ 
  200 & 5 & 360 & 0.96 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 195 & 0.99 & 0.99(0.02) & 0.99(0.02) & 0.01(0.01) & 0.05(0.01) & 0.13(0.01) \\ 
  50 & 10 & 219 & 0.99 & 0.99(0.01) & 0.99(0.01) & 0.01(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  100 & 10 & 307 & 0.96 & 1(0.01) & 0.99(0.01) & 0.01(0.01) & 0.03(0) & 0.07(0.01) \\ 
  200 & 10 & 390 & 0.94 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.05(0.01) \\ 
  30 & 30 & 217 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.12(0.01) \\ 
  50 & 30 & 275 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.09(0.01) \\ 
  100 & 30 & 346 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.07(0.01) \\ 
  200 & 30 & 414 & 1 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.05(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M2
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 458 & 0.98 & 0.96(0.04) & 0.95(0.05) & 0.02(0.01) & 0.08(0.01) & 0.15(0.02) \\ 
  50 & 5 & 498 & 0.95 & 0.97(0.03) & 0.96(0.04) & 0.02(0.01) & 0.06(0.01) & 0.11(0.01) \\ 
  100 & 5 & 500 & 0.77 & 0.97(0.02) & 0.97(0.03) & 0.02(0.01) & 0.05(0.01) & 0.08(0.01) \\ 
  200 & 5 & 500 & 0.42 & 0.98(0.01) & 0.97(0.02) & 0.02(0.01) & 0.03(0) & 0.06(0.01) \\ 
  30 & 10 & 487 & 0.99 & 0.99(0.02) & 0.99(0.02) & 0(0.01) & 0.05(0.01) & 0.13(0.02) \\ 
  50 & 10 & 500 & 0.97 & 0.99(0.01) & 0.98(0.02) & 0.01(0.01) & 0.04(0.01) & 0.1(0.01) \\ 
  100 & 10 & 500 & 0.77 & 0.99(0.01) & 0.98(0.01) & 0.01(0.01) & 0.03(0) & 0.08(0.01) \\ 
  200 & 10 & 500 & 0.27 & 0.99(0.01) & 0.98(0.01) & 0.01(0) & 0.02(0) & 0.06(0.01) \\ 
  30 & 30 & 495 & 1 & 1(0) & 1(0) & 0(0) & 0.03(0) & 0.12(0.02) \\ 
  50 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.1(0.01) \\ 
  100 & 30 & 500 & 1 & 1(0) & 1(0) & 0(0) & 0.02(0) & 0.08(0.01) \\ 
  200 & 30 & 500 & 0.73 & 1(0) & 1(0) & 0(0) & 0.01(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M2, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M12
Estimator:   MLR 

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 255 & 0.1 & 0.77(0.14) & 0.73(0.16) & 0.07(0.02) & 0.08(0.04) & 0.32(0.06) \\ 
  50 & 5 & 276 & 0.1 & 0.86(0.06) & 0.83(0.07) & 0.05(0.01) & 0.06(0.01) & 0.28(0.04) \\ 
  100 & 5 & 376 & 0.01 & 0.89(0.04) & 0.87(0.04) & 0.04(0.01) & 0.05(0.01) & 0.22(0.03) \\ 
  200 & 5 & 468 & 0 & 0.91(0.02) & 0.9(0.03) & 0.04(0) & 0.05(0.01) & 0.17(0.02) \\ 
  30 & 10 & 301 & 0.03 & 0.86(0.05) & 0.83(0.06) & 0.05(0.01) & 0.06(0.01) & 0.26(0.04) \\ 
  50 & 10 & 414 & 0.01 & 0.89(0.04) & 0.87(0.04) & 0.04(0.01) & 0.05(0.01) & 0.22(0.03) \\ 
  100 & 10 & 486 & 0 & 0.91(0.02) & 0.9(0.03) & 0.04(0) & 0.05(0) & 0.16(0.02) \\ 
  200 & 10 & 499 & 0 & 0.92(0.01) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.12(0.02) \\ 
  30 & 30 & 472 & 0 & 0.9(0.03) & 0.88(0.03) & 0.04(0.01) & 0.05(0) & 0.19(0.03) \\ 
  50 & 30 & 496 & 0 & 0.91(0.02) & 0.89(0.02) & 0.04(0) & 0.04(0) & 0.15(0.02) \\ 
  100 & 30 & 500 & 0 & 0.91(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.11(0.01) \\ 
  200 & 30 & 500 & 0 & 0.91(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 169 & 0.03 & 0.83(0.06) & 0.8(0.07) & 0.07(0.02) & 0.08(0.01) & 0.2(0.04) \\ 
  50 & 5 & 247 & 0.01 & 0.87(0.04) & 0.85(0.05) & 0.06(0.01) & 0.07(0.01) & 0.17(0.03) \\ 
  100 & 5 & 388 & 0 & 0.89(0.03) & 0.88(0.03) & 0.05(0.01) & 0.06(0.01) & 0.14(0.02) \\ 
  200 & 5 & 491 & 0 & 0.9(0.02) & 0.88(0.02) & 0.05(0) & 0.05(0.01) & 0.13(0.02) \\ 
  30 & 10 & 264 & 0.02 & 0.86(0.05) & 0.84(0.05) & 0.06(0.01) & 0.06(0.01) & 0.17(0.04) \\ 
  50 & 10 & 393 & 0 & 0.89(0.03) & 0.87(0.03) & 0.05(0.01) & 0.05(0.01) & 0.15(0.03) \\ 
  100 & 10 & 496 & 0 & 0.9(0.02) & 0.88(0.02) & 0.05(0) & 0.05(0.01) & 0.13(0.02) \\ 
  200 & 10 & 500 & 0 & 0.9(0.01) & 0.89(0.01) & 0.04(0) & 0.05(0) & 0.12(0.01) \\ 
  30 & 30 & 469 & 0 & 0.9(0.02) & 0.88(0.03) & 0.04(0.01) & 0.05(0) & 0.16(0.04) \\ 
  50 & 30 & 500 & 0 & 0.91(0.02) & 0.89(0.02) & 0.04(0) & 0.04(0) & 0.14(0.02) \\ 
  100 & 30 & 500 & 0 & 0.91(0.01) & 0.89(0.01) & 0.04(0) & 0.04(0) & 0.13(0.02) \\ 
  200 & 30 & 500 & 0 & 0.91(0.01) & 0.89(0.01) & 0.04(0) & 0.04(0) & 0.12(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 205 & 0.28 & 0.8(0.13) & 0.76(0.15) & 0.05(0.02) & 0.07(0.01) & 0.23(0.03) \\ 
  50 & 5 & 274 & 0.35 & 0.88(0.07) & 0.86(0.08) & 0.04(0.01) & 0.06(0.01) & 0.19(0.02) \\ 
  100 & 5 & 373 & 0.14 & 0.91(0.05) & 0.89(0.05) & 0.03(0.01) & 0.05(0.01) & 0.14(0.02) \\ 
  200 & 5 & 443 & 0 & 0.91(0.03) & 0.9(0.03) & 0.03(0.01) & 0.05(0.01) & 0.1(0.01) \\ 
  30 & 10 & 295 & 0.13 & 0.87(0.06) & 0.84(0.07) & 0.04(0.01) & 0.06(0.01) & 0.2(0.02) \\ 
  50 & 10 & 350 & 0.06 & 0.9(0.04) & 0.88(0.05) & 0.04(0.01) & 0.05(0.01) & 0.15(0.02) \\ 
  100 & 10 & 440 & 0 & 0.91(0.02) & 0.89(0.03) & 0.03(0.01) & 0.05(0.01) & 0.11(0.01) \\ 
  200 & 10 & 490 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  30 & 30 & 347 & 0 & 0.9(0.03) & 0.88(0.03) & 0.04(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 30 & 418 & 0 & 0.91(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.13(0.01) \\ 
  100 & 30 & 485 & 0 & 0.91(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
  200 & 30 & 497 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 382 & 0.17 & 0.83(0.09) & 0.8(0.1) & 0.06(0.02) & 0.08(0.01) & 0.18(0.03) \\ 
  50 & 5 & 465 & 0.15 & 0.88(0.05) & 0.86(0.06) & 0.05(0.01) & 0.07(0.01) & 0.15(0.02) \\ 
  100 & 5 & 494 & 0.01 & 0.9(0.03) & 0.89(0.04) & 0.04(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  200 & 5 & 500 & 0 & 0.91(0.02) & 0.89(0.02) & 0.04(0) & 0.05(0) & 0.1(0.01) \\ 
  30 & 10 & 473 & 0.06 & 0.87(0.05) & 0.85(0.06) & 0.05(0.01) & 0.06(0.01) & 0.17(0.03) \\ 
  50 & 10 & 494 & 0.01 & 0.9(0.03) & 0.88(0.04) & 0.04(0.01) & 0.05(0.01) & 0.13(0.02) \\ 
  100 & 10 & 500 & 0 & 0.91(0.02) & 0.89(0.03) & 0.04(0) & 0.05(0) & 0.1(0.01) \\ 
  200 & 10 & 500 & 0 & 0.91(0.01) & 0.9(0.02) & 0.04(0) & 0.04(0) & 0.09(0.01) \\ 
  30 & 30 & 489 & 0 & 0.9(0.02) & 0.89(0.03) & 0.04(0.01) & 0.05(0) & 0.15(0.02) \\ 
  50 & 30 & 496 & 0 & 0.91(0.02) & 0.9(0.02) & 0.04(0) & 0.04(0) & 0.12(0.02) \\ 
  100 & 30 & 500 & 0 & 0.91(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.1(0.01) \\ 
  200 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 172 & 0.38 & 0.81(0.1) & 0.78(0.12) & 0.05(0.02) & 0.07(0.01) & 0.19(0.02) \\ 
  50 & 5 & 246 & 0.39 & 0.88(0.07) & 0.86(0.08) & 0.03(0.01) & 0.06(0.01) & 0.15(0.02) \\ 
  100 & 5 & 304 & 0.21 & 0.91(0.04) & 0.89(0.05) & 0.03(0.01) & 0.05(0.01) & 0.11(0.01) \\ 
  200 & 5 & 377 & 0.01 & 0.91(0.03) & 0.9(0.03) & 0.03(0) & 0.05(0.01) & 0.07(0.01) \\ 
  30 & 10 & 216 & 0.26 & 0.88(0.06) & 0.86(0.07) & 0.04(0.01) & 0.06(0.01) & 0.17(0.02) \\ 
  50 & 10 & 243 & 0.09 & 0.9(0.04) & 0.88(0.04) & 0.03(0.01) & 0.05(0.01) & 0.13(0.01) \\ 
  100 & 10 & 327 & 0 & 0.91(0.03) & 0.9(0.03) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
  200 & 10 & 393 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
  30 & 30 & 249 & 0 & 0.9(0.02) & 0.88(0.03) & 0.04(0) & 0.05(0) & 0.16(0.02) \\ 
  50 & 30 & 277 & 0 & 0.91(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.12(0.01) \\ 
  100 & 30 & 357 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.08(0.01) \\ 
  200 & 30 & 418 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   MLR
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 459 & 0.3 & 0.82(0.09) & 0.79(0.11) & 0.05(0.02) & 0.07(0.01) & 0.18(0.03) \\ 
  50 & 5 & 493 & 0.36 & 0.89(0.07) & 0.87(0.08) & 0.04(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  100 & 5 & 499 & 0.07 & 0.9(0.04) & 0.89(0.04) & 0.03(0.01) & 0.05(0.01) & 0.1(0.01) \\ 
  200 & 5 & 500 & 0 & 0.91(0.02) & 0.9(0.03) & 0.03(0) & 0.05(0.01) & 0.08(0.01) \\ 
  30 & 10 & 481 & 0.18 & 0.88(0.06) & 0.85(0.07) & 0.04(0.01) & 0.06(0.01) & 0.16(0.02) \\ 
  50 & 10 & 499 & 0.07 & 0.9(0.04) & 0.89(0.04) & 0.03(0.01) & 0.05(0.01) & 0.12(0.02) \\ 
  100 & 10 & 500 & 0 & 0.91(0.02) & 0.9(0.03) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
  200 & 10 & 500 & 0 & 0.92(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.07(0.01) \\ 
  30 & 30 & 491 & 0 & 0.9(0.03) & 0.89(0.03) & 0.03(0.01) & 0.04(0) & 0.15(0.02) \\ 
  50 & 30 & 500 & 0 & 0.91(0.02) & 0.9(0.02) & 0.03(0) & 0.04(0) & 0.12(0.02) \\ 
  100 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.09(0.01) \\ 
  200 & 30 & 500 & 0 & 0.92(0.01) & 0.9(0.01) & 0.03(0) & 0.04(0) & 0.07(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator MLR, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M12
Estimator:   ULSMV 

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 20 & 1 & 0.95(0.05) & 0.94(0.05) & 0.02(0.01) & 0.09(0.01) & 0.35(0.06) \\ 
  50 & 5 & 74 & 0.61 & 0.93(0.05) & 0.92(0.06) & 0.03(0.01) & 0.07(0.01) & 0.32(0.06) \\ 
  100 & 5 & 226 & 0.09 & 0.92(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.22(0.05) \\ 
  200 & 5 & 410 & 0 & 0.93(0.02) & 0.91(0.03) & 0.04(0.01) & 0.05(0.01) & 0.15(0.03) \\ 
  30 & 10 & 174 & 0.43 & 0.93(0.04) & 0.91(0.04) & 0.03(0.01) & 0.07(0.01) & 0.26(0.05) \\ 
  50 & 10 & 313 & 0.05 & 0.93(0.03) & 0.91(0.03) & 0.03(0.01) & 0.06(0.01) & 0.2(0.04) \\ 
  100 & 10 & 422 & 0 & 0.92(0.02) & 0.91(0.02) & 0.04(0.01) & 0.05(0.01) & 0.13(0.02) \\ 
  200 & 10 & 469 & 0 & 0.92(0.01) & 0.91(0.02) & 0.04(0) & 0.05(0) & 0.09(0.01) \\ 
  30 & 30 & 390 & 0.01 & 0.93(0.02) & 0.92(0.02) & 0.03(0.01) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 30 & 453 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.12(0.02) \\ 
  100 & 30 & 485 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.09(0.01) \\ 
  200 & 30 & 499 & 0 & 0.92(0.01) & 0.91(0.01) & 0.04(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 11 & 0.82 & 0.9(0.12) & 0.88(0.14) & 0.02(0.02) & 0.09(0.01) & 0.16(0.04) \\ 
  50 & 5 & 32 & 0.44 & 0.87(0.07) & 0.85(0.08) & 0.03(0.01) & 0.08(0.01) & 0.14(0.03) \\ 
  100 & 5 & 33 & 0.06 & 0.87(0.04) & 0.85(0.05) & 0.04(0.01) & 0.07(0.01) & 0.12(0.02) \\ 
  200 & 5 & 34 & 0 & 0.86(0.04) & 0.83(0.05) & 0.04(0.01) & 0.06(0.01) & 0.11(0.02) \\ 
  30 & 10 & 30 & 0.47 & 0.86(0.08) & 0.83(0.1) & 0.03(0.01) & 0.07(0.01) & 0.15(0.04) \\ 
  50 & 10 & 42 & 0.14 & 0.85(0.07) & 0.82(0.08) & 0.03(0.01) & 0.06(0.01) & 0.12(0.03) \\ 
  100 & 10 & 26 & 0 & 0.82(0.05) & 0.79(0.06) & 0.04(0.01) & 0.06(0.01) & 0.1(0.02) \\ 
  200 & 10 & 8 & 0 & 0.77(0.05) & 0.73(0.05) & 0.04(0) & 0.05(0.01) & 0.11(0.02) \\ 
  30 & 30 & 42 & 0.36 & 0.88(0.05) & 0.86(0.06) & 0.02(0.01) & 0.06(0.01) & 0.12(0.03) \\ 
  50 & 30 & 28 & 0 & 0.81(0.05) & 0.78(0.06) & 0.03(0) & 0.06(0) & 0.11(0.02) \\ 
  100 & 30 & 5 & 0 & 0.75(0.03) & 0.7(0.04) & 0.03(0) & 0.05(0) & 0.09(0.02) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 173 & 0.95 & 0.92(0.08) & 0.9(0.09) & 0.02(0.01) & 0.09(0.01) & 0.21(0.04) \\ 
  50 & 5 & 246 & 0.76 & 0.92(0.06) & 0.91(0.07) & 0.02(0.01) & 0.07(0.01) & 0.16(0.02) \\ 
  100 & 5 & 365 & 0.3 & 0.93(0.04) & 0.92(0.04) & 0.03(0.01) & 0.06(0.01) & 0.11(0.01) \\ 
  200 & 5 & 440 & 0.02 & 0.93(0.02) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  30 & 10 & 277 & 0.84 & 0.92(0.05) & 0.91(0.06) & 0.02(0.01) & 0.07(0.01) & 0.16(0.02) \\ 
  50 & 10 & 351 & 0.38 & 0.93(0.03) & 0.91(0.04) & 0.02(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 10 & 444 & 0.02 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 10 & 488 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 343 & 0.93 & 0.97(0.03) & 0.96(0.03) & 0.01(0.01) & 0.06(0.01) & 0.13(0.01) \\ 
  50 & 30 & 419 & 0.31 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.05(0) & 0.1(0.01) \\ 
  100 & 30 & 485 & 0 & 0.94(0.01) & 0.93(0.01) & 0.02(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 496 & 0 & 0.94(0.01) & 0.93(0.01) & 0.02(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 292 & 0.93 & 0.9(0.1) & 0.88(0.12) & 0.02(0.01) & 0.11(0.01) & 0.14(0.03) \\ 
  50 & 5 & 362 & 0.66 & 0.89(0.08) & 0.87(0.09) & 0.03(0.01) & 0.09(0.01) & 0.11(0.02) \\ 
  100 & 5 & 393 & 0.17 & 0.89(0.05) & 0.87(0.06) & 0.03(0.01) & 0.07(0.01) & 0.09(0.01) \\ 
  200 & 5 & 432 & 0 & 0.89(0.03) & 0.87(0.04) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  30 & 10 & 353 & 0.89 & 0.91(0.08) & 0.89(0.1) & 0.02(0.01) & 0.08(0.01) & 0.12(0.02) \\ 
  50 & 10 & 379 & 0.48 & 0.89(0.06) & 0.87(0.07) & 0.02(0.01) & 0.07(0.01) & 0.1(0.02) \\ 
  100 & 10 & 419 & 0.02 & 0.88(0.04) & 0.85(0.05) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 452 & 0 & 0.87(0.03) & 0.84(0.04) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 381 & 1 & 1(0.02) & 1(0.02) & 0(0) & 0.06(0.01) & 0.1(0.02) \\ 
  50 & 30 & 413 & 0.93 & 0.97(0.04) & 0.96(0.04) & 0.01(0) & 0.06(0.01) & 0.08(0.01) \\ 
  100 & 30 & 447 & 0.01 & 0.9(0.03) & 0.88(0.04) & 0.02(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 477 & 0 & 0.86(0.03) & 0.84(0.03) & 0.02(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:50 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 153 & 0.95 & 0.87(0.14) & 0.84(0.17) & 0.02(0.01) & 0.1(0.02) & 0.15(0.02) \\ 
  50 & 5 & 243 & 0.94 & 0.9(0.09) & 0.89(0.1) & 0.02(0.01) & 0.08(0.01) & 0.12(0.01) \\ 
  100 & 5 & 299 & 0.77 & 0.93(0.05) & 0.92(0.06) & 0.02(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 5 & 378 & 0.36 & 0.94(0.03) & 0.93(0.03) & 0.02(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 213 & 0.99 & 0.95(0.07) & 0.94(0.09) & 0.01(0.01) & 0.07(0.01) & 0.13(0.01) \\ 
  50 & 10 & 241 & 0.94 & 0.93(0.06) & 0.92(0.07) & 0.01(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 10 & 329 & 0.66 & 0.94(0.04) & 0.93(0.04) & 0.01(0.01) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 397 & 0.13 & 0.94(0.02) & 0.93(0.02) & 0.02(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 249 & 1 & 1(0) & 1(0) & 0(0) & 0.06(0.01) & 0.12(0.01) \\ 
  50 & 30 & 283 & 1 & 1(0) & 1(0) & 0(0) & 0.05(0) & 0.09(0.01) \\ 
  100 & 30 & 358 & 0.99 & 0.98(0.02) & 0.98(0.02) & 0(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 416 & 0.22 & 0.96(0.01) & 0.95(0.02) & 0.01(0) & 0.05(0) & 0.05(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   ULSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 400 & 0.99 & 0.89(0.12) & 0.87(0.14) & 0.02(0.01) & 0.12(0.02) & 0.12(0.02) \\ 
  50 & 5 & 446 & 0.93 & 0.91(0.09) & 0.89(0.1) & 0.02(0.01) & 0.09(0.01) & 0.09(0.01) \\ 
  100 & 5 & 477 & 0.57 & 0.9(0.06) & 0.89(0.07) & 0.02(0.01) & 0.08(0.01) & 0.07(0.01) \\ 
  200 & 5 & 498 & 0.11 & 0.91(0.04) & 0.89(0.04) & 0.02(0) & 0.06(0.01) & 0.05(0.01) \\ 
  30 & 10 & 441 & 0.99 & 0.97(0.07) & 0.96(0.08) & 0(0.01) & 0.09(0.01) & 0.11(0.02) \\ 
  50 & 10 & 464 & 0.94 & 0.93(0.07) & 0.92(0.08) & 0.01(0.01) & 0.08(0.01) & 0.08(0.01) \\ 
  100 & 10 & 483 & 0.48 & 0.91(0.05) & 0.9(0.06) & 0.02(0.01) & 0.06(0.01) & 0.06(0.01) \\ 
  200 & 10 & 499 & 0.02 & 0.91(0.03) & 0.89(0.04) & 0.02(0) & 0.06(0.01) & 0.05(0.01) \\ 
  30 & 30 & 444 & 1 & 1(0) & 1(0) & 0(0) & 0.08(0.01) & 0.1(0.01) \\ 
  50 & 30 & 478 & 1 & 1(0) & 1(0) & 0(0) & 0.07(0.01) & 0.08(0.01) \\ 
  100 & 30 & 493 & 1 & 0.99(0.02) & 0.99(0.02) & 0(0) & 0.06(0.01) & 0.06(0.01) \\ 
  200 & 30 & 499 & 0.32 & 0.94(0.03) & 0.93(0.03) & 0.01(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator ULSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}


 ===============================

Model:   M12
Estimator:   WLSMV 

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 31 & 0.94 & 0.94(0.05) & 0.93(0.06) & 0.02(0.01) & 0.09(0.01) & 0.35(0.06) \\ 
  50 & 5 & 78 & 0.68 & 0.93(0.05) & 0.92(0.06) & 0.03(0.01) & 0.07(0.01) & 0.3(0.05) \\ 
  100 & 5 & 230 & 0.2 & 0.93(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.22(0.04) \\ 
  200 & 5 & 418 & 0 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.05(0.01) & 0.15(0.03) \\ 
  30 & 10 & 182 & 0.62 & 0.93(0.04) & 0.92(0.05) & 0.03(0.01) & 0.07(0.01) & 0.25(0.05) \\ 
  50 & 10 & 325 & 0.15 & 0.93(0.03) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.19(0.03) \\ 
  100 & 10 & 444 & 0 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.05(0.01) & 0.13(0.02) \\ 
  200 & 10 & 481 & 0 & 0.93(0.01) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.09(0.01) \\ 
  30 & 30 & 421 & 0 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.05(0.01) & 0.16(0.02) \\ 
  50 & 30 & 475 & 0 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.12(0.01) \\ 
  100 & 30 & 495 & 0 & 0.93(0.01) & 0.92(0.01) & 0.03(0) & 0.05(0) & 0.09(0.01) \\ 
  200 & 30 & 499 & 0 & 0.93(0.01) & 0.91(0.01) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.1
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 19 & 0.79 & 0.91(0.09) & 0.89(0.1) & 0.03(0.02) & 0.09(0.01) & 0.2(0.06) \\ 
  50 & 5 & 36 & 0.22 & 0.89(0.04) & 0.87(0.05) & 0.04(0.01) & 0.08(0.01) & 0.16(0.03) \\ 
  100 & 5 & 54 & 0.02 & 0.9(0.04) & 0.88(0.04) & 0.04(0.01) & 0.06(0.01) & 0.12(0.02) \\ 
  200 & 5 & 69 & 0 & 0.88(0.03) & 0.86(0.03) & 0.04(0) & 0.06(0.01) & 0.11(0.02) \\ 
  30 & 10 & 32 & 0.34 & 0.91(0.06) & 0.89(0.07) & 0.03(0.01) & 0.07(0.01) & 0.16(0.04) \\ 
  50 & 10 & 60 & 0.03 & 0.89(0.04) & 0.87(0.05) & 0.04(0.01) & 0.06(0.01) & 0.12(0.02) \\ 
  100 & 10 & 67 & 0 & 0.87(0.04) & 0.85(0.04) & 0.04(0.01) & 0.06(0.01) & 0.1(0.02) \\ 
  200 & 10 & 41 & 0 & 0.87(0.02) & 0.84(0.03) & 0.04(0) & 0.05(0) & 0.1(0.02) \\ 
  30 & 30 & 69 & 0.03 & 0.92(0.03) & 0.91(0.03) & 0.03(0.01) & 0.06(0.01) & 0.13(0.02) \\ 
  50 & 30 & 81 & 0 & 0.9(0.02) & 0.88(0.03) & 0.03(0) & 0.05(0) & 0.11(0.02) \\ 
  100 & 30 & 55 & 0 & 0.87(0.02) & 0.84(0.03) & 0.04(0) & 0.05(0) & 0.1(0.01) \\ 
  200 & 30 & 18 & 0 & 0.86(0.02) & 0.83(0.02) & 0.04(0) & 0.05(0) & 0.09(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.1 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 117 & 0.9 & 0.91(0.07) & 0.9(0.08) & 0.03(0.01) & 0.09(0.01) & 0.21(0.04) \\ 
  50 & 5 & 200 & 0.63 & 0.93(0.05) & 0.91(0.06) & 0.03(0.01) & 0.07(0.01) & 0.16(0.02) \\ 
  100 & 5 & 333 & 0.21 & 0.93(0.04) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.11(0.01) \\ 
  200 & 5 & 420 & 0.01 & 0.93(0.02) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  30 & 10 & 193 & 0.57 & 0.93(0.04) & 0.91(0.05) & 0.03(0.01) & 0.07(0.01) & 0.15(0.02) \\ 
  50 & 10 & 311 & 0.16 & 0.93(0.03) & 0.91(0.04) & 0.03(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  100 & 10 & 418 & 0.01 & 0.93(0.02) & 0.92(0.02) & 0.03(0) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 10 & 485 & 0 & 0.94(0.01) & 0.92(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 319 & 0.14 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.06(0.01) & 0.13(0.01) \\ 
  50 & 30 & 403 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.05(0) & 0.1(0.01) \\ 
  100 & 30 & 482 & 0 & 0.94(0.01) & 0.93(0.01) & 0.03(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 498 & 0 & 0.94(0.01) & 0.92(0.01) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.3
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 286 & 0.81 & 0.91(0.07) & 0.89(0.08) & 0.03(0.01) & 0.09(0.01) & 0.16(0.03) \\ 
  50 & 5 & 318 & 0.51 & 0.91(0.05) & 0.89(0.06) & 0.03(0.01) & 0.08(0.01) & 0.13(0.02) \\ 
  100 & 5 & 354 & 0.06 & 0.91(0.03) & 0.9(0.04) & 0.03(0.01) & 0.06(0.01) & 0.09(0.01) \\ 
  200 & 5 & 389 & 0 & 0.91(0.02) & 0.9(0.03) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  30 & 10 & 342 & 0.53 & 0.92(0.04) & 0.91(0.05) & 0.03(0.01) & 0.07(0.01) & 0.14(0.02) \\ 
  50 & 10 & 356 & 0.11 & 0.92(0.03) & 0.9(0.04) & 0.03(0.01) & 0.06(0.01) & 0.11(0.02) \\ 
  100 & 10 & 371 & 0 & 0.91(0.03) & 0.9(0.03) & 0.03(0) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 10 & 401 & 0 & 0.91(0.02) & 0.89(0.02) & 0.04(0) & 0.05(0) & 0.07(0.01) \\ 
  30 & 30 & 341 & 0.74 & 0.97(0.02) & 0.96(0.02) & 0.01(0.01) & 0.06(0.01) & 0.12(0.02) \\ 
  50 & 30 & 370 & 0.04 & 0.95(0.02) & 0.94(0.02) & 0.02(0) & 0.05(0.01) & 0.09(0.01) \\ 
  100 & 30 & 406 & 0 & 0.93(0.01) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.08(0.01) \\ 
  200 & 30 & 413 & 0 & 0.91(0.01) & 0.9(0.01) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.3 and ICC_L 0.5} 
\end{table}

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.1 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 95 & 0.8 & 0.91(0.07) & 0.89(0.08) & 0.03(0.02) & 0.09(0.01) & 0.15(0.02) \\ 
  50 & 5 & 179 & 0.7 & 0.92(0.05) & 0.91(0.06) & 0.03(0.01) & 0.08(0.01) & 0.12(0.01) \\ 
  100 & 5 & 266 & 0.27 & 0.93(0.03) & 0.92(0.04) & 0.03(0.01) & 0.06(0.01) & 0.08(0.01) \\ 
  200 & 5 & 353 & 0 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 185 & 0.83 & 0.95(0.04) & 0.94(0.04) & 0.02(0.01) & 0.07(0.01) & 0.13(0.01) \\ 
  50 & 10 & 217 & 0.29 & 0.94(0.03) & 0.92(0.03) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 10 & 305 & 0 & 0.94(0.02) & 0.93(0.02) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 387 & 0 & 0.94(0.01) & 0.93(0.02) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
  30 & 30 & 211 & 0.98 & 0.99(0.02) & 0.99(0.02) & 0(0.01) & 0.06(0.01) & 0.12(0.01) \\ 
  50 & 30 & 265 & 0.41 & 0.97(0.01) & 0.96(0.02) & 0.01(0) & 0.05(0.01) & 0.09(0.01) \\ 
  100 & 30 & 343 & 0 & 0.95(0.01) & 0.94(0.01) & 0.02(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 409 & 0 & 0.94(0.01) & 0.93(0.01) & 0.03(0) & 0.05(0) & 0.05(0) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.1} 
\end{table}

===============================

Model:   M12
Estimator:   WLSMV
ICC Obs. Var.:   0.5
ICC Lat. Var.:   0.5 
% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Wed Oct 30 21:17:51 2019
\begin{table}[ht]
\centering
\begin{tabular}{lllllllll}
  \toprule
N2 & N1 & Num\_Rep & chi2 & CFI & TLI & RMSEA & SRMRW & SRMRB \\ 
  \midrule
30 & 5 & 407 & 0.87 & 0.92(0.06) & 0.9(0.07) & 0.03(0.01) & 0.09(0.01) & 0.14(0.02) \\ 
  50 & 5 & 448 & 0.65 & 0.92(0.05) & 0.91(0.06) & 0.03(0.01) & 0.08(0.01) & 0.11(0.01) \\ 
  100 & 5 & 481 & 0.15 & 0.92(0.03) & 0.91(0.04) & 0.03(0.01) & 0.07(0.01) & 0.08(0.01) \\ 
  200 & 5 & 498 & 0 & 0.92(0.02) & 0.91(0.03) & 0.03(0) & 0.06(0.01) & 0.06(0.01) \\ 
  30 & 10 & 440 & 0.87 & 0.95(0.04) & 0.95(0.05) & 0.02(0.01) & 0.07(0.01) & 0.13(0.02) \\ 
  50 & 10 & 476 & 0.35 & 0.94(0.03) & 0.93(0.04) & 0.03(0.01) & 0.06(0.01) & 0.1(0.01) \\ 
  100 & 10 & 486 & 0.01 & 0.93(0.02) & 0.92(0.03) & 0.03(0) & 0.06(0.01) & 0.07(0.01) \\ 
  200 & 10 & 500 & 0 & 0.93(0.02) & 0.91(0.02) & 0.03(0) & 0.05(0) & 0.06(0.01) \\ 
  30 & 30 & 432 & 1 & 1(0) & 1(0.01) & 0(0) & 0.06(0.01) & 0.12(0.01) \\ 
  50 & 30 & 466 & 0.85 & 0.98(0.02) & 0.98(0.02) & 0.01(0) & 0.06(0.01) & 0.09(0.01) \\ 
  100 & 30 & 495 & 0 & 0.95(0.01) & 0.94(0.01) & 0.02(0) & 0.05(0) & 0.07(0.01) \\ 
  200 & 30 & 500 & 0 & 0.94(0.01) & 0.93(0.01) & 0.03(0) & 0.05(0) & 0.05(0.01) \\ 
   \bottomrule
\end{tabular}
\caption{Summary of Fit Statistics Across Conditions: Model M12, Estimator WLSMV, ICC_O 0.5 and ICC_L 0.5} 
\end{table}

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] xtable_1.8-4     kableExtra_1.1.0 forcats_0.4.0    stringr_1.4.0   
 [5] dplyr_0.8.1      purrr_0.3.2      readr_1.3.1      tidyr_0.8.3     
 [9] tibble_2.1.1     ggplot2_3.2.0    tidyverse_1.2.1 

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  xfun_0.7          haven_2.1.0      
 [4] lattice_0.20-38   colorspace_1.4-1  generics_0.0.2   
 [7] htmltools_0.3.6   viridisLite_0.3.0 yaml_2.2.0       
[10] rlang_0.3.4       pillar_1.4.1      glue_1.3.1       
[13] withr_2.1.2       modelr_0.1.4      readxl_1.3.1     
[16] munsell_0.5.0     gtable_0.3.0      workflowr_1.4.0  
[19] cellranger_1.1.0  rvest_0.3.4       evaluate_0.14    
[22] knitr_1.23        highr_0.8         broom_0.5.2      
[25] Rcpp_1.0.1        scales_1.0.0      backports_1.1.4  
[28] webshot_0.5.1     jsonlite_1.6      fs_1.3.1         
[31] hms_0.4.2         digest_0.6.19     stringi_1.4.3    
[34] grid_3.6.0        rprojroot_1.3-2   cli_1.1.0        
[37] tools_3.6.0       magrittr_1.5      lazyeval_0.2.2   
[40] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.1 
[46] rmarkdown_1.13    httr_1.4.0        rstudioapi_0.10  
[49] R6_2.4.0          nlme_3.1-139      git2r_0.26.1     
[52] compiler_3.6.0