CFA ASDQII
Levi Brackman
23 July 2015
ASDQII Please select from the list below how true or untrue each item is / for you.
- I get good grades in ENGLISH LANGUAGE classes. (English)
- Work in ENGLISH LANGUAGE classes is easy for me. (English)
- I learn things quickly in ENGLISH LANGUAGE classes. P (English)
- Compared to others my age I am good at ENGLISH LANGUAGE classes. (English)
I have always done well in ENGLISH LANGUAGE classes. P (English)
- I learn things quickly in MATHEMATICS classes. (Math)
- I have always done well in MATHEMATICS classes. (Math)
- Compared to others my age I am good at MATHEMATICS classes. (Math)
- Work in MATHEMATICS classes is easy for me. (Math)
I get good grades in MATHEMATICS classes. (Math)
- I get good grades in SCIENCE classes. (Science)
- Work in SCIENCE classes is easy for me. (Science)
- I learn things quickly in SCIENCE classes.(Science)
- Compared to others my age I am good at SCIENCE classes. (Science)
I have always done well in SCIENCE classes. (Science)
- I have always done well in SCHOOL SUBJECTS classes. (all subjects)
- Compared to others my age I am good at SCHOOL SUBJECTS classes. (all subjects)
- I get good grades in SCHOOL SUBJECTS classes. (all subjects)
- Work in SCHOOL SUBJECTS classes is easy for me. (all subjects)
I learn things quickly in SCHOOL SUBJECTS classes. (all subjects)
library(lavaan)## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.library(semPlot)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(GPArotation)
library(psych)
library(car)##
## Attaching package: 'car'
##
## The following object is masked from 'package:psych':
##
## logitlibrary(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%library(GGally)##
## Attaching package: 'GGally'
##
## The following object is masked from 'package:dplyr':
##
## nasaloadthedata
data <- read.csv("~/Psychometric_study_data/allsurveysYT1.csv")
data<-tbl_df(data)
ASDQII<-select(data, ASDQII_1, ASDQII_2, ASDQII_3, ASDQII_4, ASDQII_5, ASDQII_6, ASDQII_7, ASDQII_8, ASDQII_9, ASDQII_10, ASDQII_11,ASDQII_12, ASDQII_13, ASDQII_14, ASDQII_15, ASDQII_16, ASDQII_17, ASDQII_18, ASDQII_19, ASDQII_20)
ASDQII<-tbl_df(ASDQII)
ASDQII## Source: local data frame [670 x 20]
##
## ASDQII_1 ASDQII_2 ASDQII_3 ASDQII_4 ASDQII_5 ASDQII_6 ASDQII_7 ASDQII_8
## 1 6 6 5 6 6 5 6 5
## 2 5 5 5 4 5 4 4 4
## 3 4 3 5 3 4 6 3 6
## 4 5 5 5 5 5 6 6 6
## 5 5 4 5 5 4 2 2 4
## 6 5 5 5 5 5 5 6 5
## 7 5 3 5 3 3 5 3 5
## 8 4 4 3 4 4 6 6 5
## 9 6 5 5 5 5 5 6 5
## 10 5 1 2 2 1 3 2 2
## .. ... ... ... ... ... ... ... ...
## Variables not shown: ASDQII_9 (int), ASDQII_10 (int), ASDQII_11 (int),
## ASDQII_12 (int), ASDQII_13 (int), ASDQII_14 (int), ASDQII_15 (int),
## ASDQII_16 (int), ASDQII_17 (int), ASDQII_18 (int), ASDQII_19 (int),
## ASDQII_20 (int)create plots
#ggpairs(ASDQII, columns = 1:15, title="Big 5 Marsh" )create the models
four.model= ' English =~ ASDQII_1 + ASDQII_2 + ASDQII_3 + ASDQII_4 + ASDQII_5
Math =~ ASDQII_6 + ASDQII_7 + ASDQII_8 + ASDQII_9 +ASDQII_10
Science =~ ASDQII_11 + ASDQII_12 +ASDQII_13 + ASDQII_14 + ASDQII_15
All Subjects =~ ASDQII_16 + ASDQII_17 + ASDQII_18 + ASDQII_19 + ASDQII_20'
one.model= 'ASDQII =~ ASDQII_1 + ASDQII_2 + ASDQII_3 + ASDQII_4 + ASDQII_5 + ASDQII_6 + ASDQII_7 + ASDQII_8 + ASDQII_9 + ASDQII_10 + ASDQII_11 + ASDQII_12 + ASDQII_13 + ASDQII_14 + ASDQII_15 + ASDQII_16 + ASDQII_17 + ASDQII_18 + ASDQII_19 + ASDQII_20 'run the models
four.fit=cfa(four.model, data=ASDQII, missing = "fiml")## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 44 45 46 50 57 62 63 86 104 117 118 154 160 168 174 177 181 205 209 226 238 245 246 261 294 295 298 299 302 379 391 393 394 397 404 412 473 474 475 479 480 482 483 487 489 491 494 497 507 509 511 518 519 521 522 523 525 526 530 532 533 534 536 537 540 544one.fit=cfa(one.model, data=ASDQII, missing = "fiml")## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 44 45 46 50 57 62 63 86 104 117 118 154 160 168 174 177 181 205 209 226 238 245 246 261 294 295 298 299 302 379 391 393 394 397 404 412 473 474 475 479 480 482 483 487 489 491 494 497 507 509 511 518 519 521 522 523 525 526 530 532 533 534 536 537 540 544create pictures
semPaths(four.fit, whatLabels = "std", layout = "tree")
semPaths(one.fit, whatLabels = "std", layout = "tree")
#summaries
summary(four.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 103 iterations
##
## Used Total
## Number of observations 598 670
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 564.361
## Degrees of freedom 164
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## English =~
## ASDQII_1 1.000 1.035 0.869
## ASDQII_2 1.018 0.041 24.651 0.000 1.054 0.850
## ASDQII_3 1.000 0.040 25.160 0.000 1.035 0.863
## ASDQII_4 1.017 0.042 24.376 0.000 1.053 0.848
## ASDQII_5 1.034 0.041 25.132 0.000 1.070 0.854
## Math =~
## ASDQII_6 1.000 1.372 0.898
## ASDQII_7 1.031 0.029 35.686 0.000 1.415 0.911
## ASDQII_8 1.022 0.027 37.577 0.000 1.403 0.931
## ASDQII_9 1.025 0.028 36.259 0.000 1.407 0.917
## ASDQII_10 0.959 0.028 34.776 0.000 1.317 0.904
## Science =~
## ASDQII_11 1.000 1.168 0.909
## ASDQII_12 1.027 0.030 34.807 0.000 1.199 0.897
## ASDQII_13 1.095 0.031 35.683 0.000 1.279 0.906
## ASDQII_14 1.022 0.030 34.201 0.000 1.193 0.890
## ASDQII_15 0.997 0.030 33.260 0.000 1.165 0.878
## AllSubjects =~
## ASDQII_16 1.000 1.018 0.848
## ASDQII_17 0.986 0.038 25.946 0.000 1.004 0.837
## ASDQII_18 1.013 0.037 27.226 0.000 1.031 0.858
## ASDQII_19 0.945 0.038 24.848 0.000 0.961 0.818
## ASDQII_20 1.026 0.039 26.552 0.000 1.044 0.851
##
## Covariances:
## English ~~
## Math 0.351 0.068 5.202 0.000 0.247 0.247
## Science 0.567 0.063 9.051 0.000 0.469 0.469
## AllSubjects 0.730 0.063 11.626 0.000 0.693 0.693
## Math ~~
## Science 0.914 0.081 11.234 0.000 0.570 0.570
## AllSubjects 0.986 0.079 12.558 0.000 0.706 0.706
## Science ~~
## AllSubjects 0.883 0.068 12.942 0.000 0.743 0.743
##
## Intercepts:
## ASDQII_1 4.647 0.053 88.304 0.000 4.647 3.902
## ASDQII_2 4.456 0.055 81.181 0.000 4.456 3.594
## ASDQII_3 4.556 0.053 85.950 0.000 4.556 3.800
## ASDQII_4 4.509 0.055 81.995 0.000 4.509 3.631
## ASDQII_5 4.535 0.055 81.869 0.000 4.535 3.623
## ASDQII_6 4.122 0.063 65.925 0.000 4.122 2.696
## ASDQII_7 4.161 0.064 65.506 0.000 4.161 2.679
## ASDQII_8 4.119 0.062 66.823 0.000 4.119 2.733
## ASDQII_9 4.035 0.063 64.301 0.000 4.035 2.629
## ASDQII_10 4.291 0.060 72.022 0.000 4.291 2.945
## ASDQII_11 4.356 0.053 82.913 0.000 4.356 3.391
## ASDQII_12 4.189 0.055 76.593 0.000 4.189 3.132
## ASDQII_13 4.227 0.058 73.250 0.000 4.227 2.995
## ASDQII_14 4.236 0.055 77.301 0.000 4.236 3.161
## ASDQII_15 4.246 0.054 78.249 0.000 4.246 3.200
## ASDQII_16 4.465 0.049 91.006 0.000 4.465 3.722
## ASDQII_17 4.460 0.049 90.916 0.000 4.460 3.718
## ASDQII_18 4.555 0.049 92.690 0.000 4.555 3.790
## ASDQII_19 4.356 0.048 90.687 0.000 4.356 3.708
## ASDQII_20 4.445 0.050 88.616 0.000 4.445 3.624
## English 0.000 0.000 0.000
## Math 0.000 0.000 0.000
## Science 0.000 0.000 0.000
## AllSubjects 0.000 0.000 0.000
##
## Variances:
## ASDQII_1 0.348 0.029 0.348 0.245
## ASDQII_2 0.427 0.034 0.427 0.278
## ASDQII_3 0.366 0.030 0.366 0.255
## ASDQII_4 0.434 0.034 0.434 0.281
## ASDQII_5 0.423 0.034 0.423 0.270
## ASDQII_6 0.455 0.031 0.455 0.194
## ASDQII_7 0.410 0.029 0.410 0.170
## ASDQII_8 0.304 0.024 0.304 0.134
## ASDQII_9 0.376 0.027 0.376 0.160
## ASDQII_10 0.389 0.027 0.389 0.183
## ASDQII_11 0.287 0.022 0.287 0.174
## ASDQII_12 0.350 0.025 0.350 0.196
## ASDQII_13 0.356 0.027 0.356 0.179
## ASDQII_14 0.373 0.026 0.373 0.208
## ASDQII_15 0.404 0.028 0.404 0.230
## ASDQII_16 0.403 0.028 0.403 0.280
## ASDQII_17 0.432 0.029 0.432 0.300
## ASDQII_18 0.380 0.027 0.380 0.263
## ASDQII_19 0.455 0.030 0.455 0.330
## ASDQII_20 0.414 0.029 0.414 0.275
## English 1.071 0.089 1.000 1.000
## Math 1.883 0.134 1.000 1.000
## Science 1.364 0.095 1.000 1.000
## AllSubjects 1.036 0.081 1.000 1.000
##
## R-Square:
##
## ASDQII_1 0.755
## ASDQII_2 0.722
## ASDQII_3 0.745
## ASDQII_4 0.719
## ASDQII_5 0.730
## ASDQII_6 0.806
## ASDQII_7 0.830
## ASDQII_8 0.866
## ASDQII_9 0.840
## ASDQII_10 0.817
## ASDQII_11 0.826
## ASDQII_12 0.804
## ASDQII_13 0.821
## ASDQII_14 0.792
## ASDQII_15 0.770
## ASDQII_16 0.720
## ASDQII_17 0.700
## ASDQII_18 0.737
## ASDQII_19 0.670
## ASDQII_20 0.725summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 71 iterations
##
## Used Total
## Number of observations 598 670
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 5174.878
## Degrees of freedom 170
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## ASDQII =~
## ASDQII_1 1.000 0.708 0.597
## ASDQII_2 0.886 0.087 10.206 0.000 0.627 0.507
## ASDQII_3 0.895 0.085 10.576 0.000 0.634 0.529
## ASDQII_4 0.882 0.087 10.149 0.000 0.625 0.504
## ASDQII_5 0.935 0.088 10.595 0.000 0.662 0.530
## ASDQII_6 1.493 0.116 12.899 0.000 1.058 0.692
## ASDQII_7 1.575 0.119 13.212 0.000 1.115 0.718
## ASDQII_8 1.563 0.117 13.401 0.000 1.107 0.734
## ASDQII_9 1.575 0.118 13.308 0.000 1.115 0.727
## ASDQII_10 1.488 0.112 13.314 0.000 1.054 0.723
## ASDQII_11 1.475 0.102 14.418 0.000 1.044 0.813
## ASDQII_12 1.439 0.104 13.839 0.000 1.019 0.762
## ASDQII_13 1.540 0.110 13.941 0.000 1.090 0.773
## ASDQII_14 1.475 0.105 14.015 0.000 1.045 0.780
## ASDQII_15 1.430 0.103 13.868 0.000 1.013 0.763
## ASDQII_16 1.337 0.093 14.320 0.000 0.947 0.789
## ASDQII_17 1.338 0.093 14.327 0.000 0.948 0.790
## ASDQII_18 1.359 0.094 14.464 0.000 0.963 0.801
## ASDQII_19 1.317 0.092 14.342 0.000 0.933 0.794
## ASDQII_20 1.430 0.097 14.683 0.000 1.013 0.825
##
## Intercepts:
## ASDQII_1 4.670 0.053 88.690 0.000 4.670 3.935
## ASDQII_2 4.483 0.056 80.709 0.000 4.483 3.620
## ASDQII_3 4.582 0.054 85.559 0.000 4.582 3.828
## ASDQII_4 4.536 0.056 81.500 0.000 4.536 3.657
## ASDQII_5 4.562 0.056 81.598 0.000 4.562 3.650
## ASDQII_6 4.122 0.063 65.925 0.000 4.122 2.696
## ASDQII_7 4.161 0.064 65.506 0.000 4.161 2.679
## ASDQII_8 4.119 0.062 66.823 0.000 4.119 2.733
## ASDQII_9 4.035 0.063 64.301 0.000 4.035 2.629
## ASDQII_10 4.291 0.060 72.021 0.000 4.291 2.945
## ASDQII_11 4.356 0.053 82.913 0.000 4.356 3.391
## ASDQII_12 4.189 0.055 76.593 0.000 4.189 3.132
## ASDQII_13 4.227 0.058 73.250 0.000 4.227 2.995
## ASDQII_14 4.236 0.055 77.301 0.000 4.236 3.161
## ASDQII_15 4.246 0.054 78.249 0.000 4.246 3.200
## ASDQII_16 4.465 0.049 91.006 0.000 4.465 3.722
## ASDQII_17 4.460 0.049 90.916 0.000 4.460 3.718
## ASDQII_18 4.555 0.049 92.690 0.000 4.555 3.790
## ASDQII_19 4.356 0.048 90.687 0.000 4.356 3.708
## ASDQII_20 4.445 0.050 88.616 0.000 4.445 3.624
## ASDQII 0.000 0.000 0.000
##
## Variances:
## ASDQII_1 0.907 0.061 0.907 0.644
## ASDQII_2 1.140 0.076 1.140 0.743
## ASDQII_3 1.031 0.069 1.031 0.720
## ASDQII_4 1.149 0.076 1.149 0.746
## ASDQII_5 1.124 0.075 1.124 0.719
## ASDQII_6 1.220 0.076 1.220 0.522
## ASDQII_7 1.169 0.073 1.169 0.485
## ASDQII_8 1.046 0.066 1.046 0.461
## ASDQII_9 1.112 0.070 1.112 0.472
## ASDQII_10 1.013 0.064 1.013 0.477
## ASDQII_11 0.560 0.037 0.560 0.339
## ASDQII_12 0.750 0.048 0.750 0.419
## ASDQII_13 0.803 0.052 0.803 0.403
## ASDQII_14 0.704 0.045 0.704 0.392
## ASDQII_15 0.735 0.047 0.735 0.417
## ASDQII_16 0.543 0.034 0.543 0.378
## ASDQII_17 0.541 0.034 0.541 0.376
## ASDQII_18 0.518 0.033 0.518 0.359
## ASDQII_19 0.510 0.032 0.510 0.370
## ASDQII_20 0.479 0.031 0.479 0.319
## ASDQII 0.501 0.069 1.000 1.000
##
## R-Square:
##
## ASDQII_1 0.356
## ASDQII_2 0.257
## ASDQII_3 0.280
## ASDQII_4 0.254
## ASDQII_5 0.281
## ASDQII_6 0.478
## ASDQII_7 0.515
## ASDQII_8 0.539
## ASDQII_9 0.528
## ASDQII_10 0.523
## ASDQII_11 0.661
## ASDQII_12 0.581
## ASDQII_13 0.597
## ASDQII_14 0.608
## ASDQII_15 0.583
## ASDQII_16 0.622
## ASDQII_17 0.624
## ASDQII_18 0.641
## ASDQII_19 0.630
## ASDQII_20 0.681Residual correlations
correl = residuals(four.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## ASDQII_1 ASDQII_2 ASDQII_3 ASDQII_4 ASDQII_5 ASDQII_6 ASDQII_7
## ASDQII_1 0.000
## ASDQII_2 0.002 0.000
## ASDQII_3 -0.028 0.014 0.000
## ASDQII_4 -0.022 0.027 0.013 0.000
## ASDQII_5 0.026 -0.029 0.011 -0.001 0.000
## ASDQII_6 -0.009 -0.053 -0.016 -0.052 0.000 0.000
## ASDQII_7 0.021 -0.059 -0.054 -0.051 0.020 0.012 0.000
## ASDQII_8 0.041 -0.035 0.003 -0.011 0.044 -0.009 -0.002
## ASDQII_9 0.027 -0.023 -0.008 -0.028 0.039 0.011 -0.009
## ASDQII_10 0.094 -0.034 0.005 -0.017 0.048 -0.007 -0.003
## ASDQII_11 0.116 0.014 0.011 0.017 -0.005 -0.018 0.005
## ASDQII_12 0.032 0.034 0.007 -0.005 -0.054 -0.019 -0.024
## ASDQII_13 0.017 -0.013 0.006 -0.024 -0.041 -0.010 -0.010
## ASDQII_14 0.011 -0.043 -0.045 -0.014 -0.088 -0.005 0.011
## ASDQII_15 0.060 0.013 -0.005 -0.017 -0.016 -0.017 -0.003
## ASDQII_16 0.048 -0.021 -0.014 -0.018 0.025 -0.034 0.002
## ASDQII_17 0.034 -0.059 -0.036 0.000 -0.026 -0.050 0.014
## ASDQII_18 0.096 -0.044 -0.016 -0.035 0.004 -0.012 0.016
## ASDQII_19 0.020 -0.017 0.010 -0.045 0.024 -0.011 0.012
## ASDQII_20 0.057 -0.004 0.021 -0.031 -0.020 0.008 -0.001
## ASDQII_8 ASDQII_9 ASDQII_10 ASDQII_11 ASDQII_12 ASDQII_13
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8 0.000
## ASDQII_9 0.005 0.000
## ASDQII_10 0.008 -0.005 0.000
## ASDQII_11 0.007 0.005 0.026 0.000
## ASDQII_12 -0.011 0.019 -0.031 -0.008 0.000
## ASDQII_13 -0.002 0.010 -0.025 -0.013 0.024 0.000
## ASDQII_14 0.044 0.031 0.006 -0.006 -0.009 0.014
## ASDQII_15 -0.013 0.016 -0.015 0.018 0.001 -0.016
## ASDQII_16 -0.028 -0.019 0.002 0.016 -0.069 -0.057
## ASDQII_17 -0.001 -0.021 0.025 0.035 -0.056 -0.033
## ASDQII_18 -0.008 -0.022 0.063 0.039 -0.092 -0.085
## ASDQII_19 0.013 0.023 -0.023 0.066 0.054 0.040
## ASDQII_20 0.012 0.010 0.029 0.047 0.026 0.035
## ASDQII_14 ASDQII_15 ASDQII_16 ASDQII_17 ASDQII_18 ASDQII_19
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8
## ASDQII_9
## ASDQII_10
## ASDQII_11
## ASDQII_12
## ASDQII_13
## ASDQII_14 0.000
## ASDQII_15 -0.004 0.000
## ASDQII_16 -0.014 -0.005 0.000
## ASDQII_17 0.042 -0.007 0.010 0.000
## ASDQII_18 -0.043 -0.001 0.029 0.033 0.000
## ASDQII_19 0.020 0.017 -0.012 -0.021 -0.017 0.000
## ASDQII_20 0.048 0.013 -0.007 -0.010 -0.033 0.025
## ASDQII_20
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8
## ASDQII_9
## ASDQII_10
## ASDQII_11
## ASDQII_12
## ASDQII_13
## ASDQII_14
## ASDQII_15
## ASDQII_16
## ASDQII_17
## ASDQII_18
## ASDQII_19
## ASDQII_20 0.000
##
## $mean
## ASDQII_1 ASDQII_2 ASDQII_3 ASDQII_4 ASDQII_5 ASDQII_6 ASDQII_7
## -0.002 0.000 0.000 -0.005 -0.010 0.000 0.000
## ASDQII_8 ASDQII_9 ASDQII_10 ASDQII_11 ASDQII_12 ASDQII_13 ASDQII_14
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## ASDQII_15 ASDQII_16 ASDQII_17 ASDQII_18 ASDQII_19 ASDQII_20
## 0.000 0.000 0.000 0.000 0.000 0.000View(correl$cor)
correl1 = residuals(one.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## ASDQII_1 ASDQII_2 ASDQII_3 ASDQII_4 ASDQII_5 ASDQII_6 ASDQII_7
## ASDQII_1 0.000
## ASDQII_2 0.438 0.000
## ASDQII_3 0.406 0.479 0.000
## ASDQII_4 0.414 0.493 0.478 0.000
## ASDQII_5 0.452 0.429 0.468 0.457 0.000
## ASDQII_6 -0.228 -0.215 -0.190 -0.212 -0.177 0.000
## ASDQII_7 -0.211 -0.231 -0.239 -0.221 -0.167 0.333 0.000
## ASDQII_8 -0.197 -0.212 -0.187 -0.186 -0.148 0.318 0.318
## ASDQII_9 -0.209 -0.198 -0.197 -0.202 -0.152 0.331 0.305
## ASDQII_10 -0.143 -0.210 -0.185 -0.191 -0.144 0.304 0.301
## ASDQII_11 0.002 -0.036 -0.051 -0.031 -0.071 -0.115 -0.106
## ASDQII_12 -0.057 0.006 -0.033 -0.032 -0.098 -0.087 -0.105
## ASDQII_13 -0.075 -0.043 -0.036 -0.052 -0.087 -0.081 -0.094
## ASDQII_14 -0.092 -0.083 -0.097 -0.052 -0.144 -0.089 -0.086
## ASDQII_15 -0.038 -0.024 -0.053 -0.052 -0.069 -0.096 -0.095
## ASDQII_16 0.087 0.078 0.076 0.083 0.109 -0.042 -0.019
## ASDQII_17 0.066 0.033 0.047 0.094 0.050 -0.067 -0.015
## ASDQII_18 0.135 0.055 0.073 0.066 0.088 -0.022 -0.007
## ASDQII_19 0.038 0.062 0.079 0.036 0.088 -0.042 -0.031
## ASDQII_20 0.077 0.079 0.093 0.053 0.047 -0.024 -0.046
## ASDQII_8 ASDQII_9 ASDQII_10 ASDQII_11 ASDQII_12 ASDQII_13
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8 0.000
## ASDQII_9 0.324 0.000
## ASDQII_10 0.318 0.298 0.000
## ASDQII_11 -0.108 -0.110 -0.094 0.000
## ASDQII_12 -0.095 -0.066 -0.119 0.188 0.000
## ASDQII_13 -0.088 -0.078 -0.117 0.182 0.248 0.000
## ASDQII_14 -0.056 -0.070 -0.099 0.169 0.196 0.218
## ASDQII_15 -0.108 -0.079 -0.114 0.195 0.206 0.190
## ASDQII_16 -0.050 -0.043 -0.027 -0.052 -0.105 -0.095
## ASDQII_17 -0.032 -0.053 -0.013 -0.042 -0.101 -0.079
## ASDQII_18 -0.032 -0.048 0.031 -0.033 -0.130 -0.126
## ASDQII_19 -0.032 -0.024 -0.075 -0.027 -0.006 -0.022
## ASDQII_20 -0.035 -0.039 -0.025 -0.049 -0.036 -0.030
## ASDQII_14 ASDQII_15 ASDQII_16 ASDQII_17 ASDQII_18 ASDQII_19
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8
## ASDQII_9
## ASDQII_10
## ASDQII_11
## ASDQII_12
## ASDQII_13
## ASDQII_14 0.000
## ASDQII_15 0.182 0.000
## ASDQII_16 -0.067 -0.054 0.000
## ASDQII_17 -0.021 -0.065 0.096 0.000
## ASDQII_18 -0.099 -0.053 0.125 0.118 0.000
## ASDQII_19 -0.057 -0.055 0.056 0.036 0.050 0.000
## ASDQII_20 -0.033 -0.062 0.064 0.050 0.037 0.067
## ASDQII_20
## ASDQII_1
## ASDQII_2
## ASDQII_3
## ASDQII_4
## ASDQII_5
## ASDQII_6
## ASDQII_7
## ASDQII_8
## ASDQII_9
## ASDQII_10
## ASDQII_11
## ASDQII_12
## ASDQII_13
## ASDQII_14
## ASDQII_15
## ASDQII_16
## ASDQII_17
## ASDQII_18
## ASDQII_19
## ASDQII_20 0.000
##
## $mean
## ASDQII_1 ASDQII_2 ASDQII_3 ASDQII_4 ASDQII_5 ASDQII_6 ASDQII_7
## -0.035 -0.026 -0.027 -0.031 -0.038 0.000 0.000
## ASDQII_8 ASDQII_9 ASDQII_10 ASDQII_11 ASDQII_12 ASDQII_13 ASDQII_14
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## ASDQII_15 ASDQII_16 ASDQII_17 ASDQII_18 ASDQII_19 ASDQII_20
## 0.000 0.000 0.000 0.000 0.000 0.000View(correl1$cor)zscore correlation anything over 1.96 is going to be statistically significant at the .05 level
zcorrels = residuals(four.fit, type = "standardized")
View(zcorrels$cov)
zcorrels1 = residuals(one.fit, type = "standardized")
View(zcorrels1$cov)Modification indicies
modindices(four.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 ASDQII_11 ~~ ASDQII_18 30.172 0.092 0.092 0.059 0.059
## 2 ASDQII_10 ~~ ASDQII_18 29.218 0.102 0.102 0.058 0.058
## 3 ASDQII_1 ~~ ASDQII_18 26.926 0.106 0.106 0.074 0.074
## 4 ASDQII_10 ~~ ASDQII_19 26.157 -0.103 -0.103 -0.060 -0.060
## 5 ASDQII_1 ~~ ASDQII_11 23.117 0.088 0.088 0.057 0.057
## 6 ASDQII_1 ~~ ASDQII_10 23.016 0.099 0.099 0.057 0.057
## 7 ASDQII_13 ~~ ASDQII_18 21.436 -0.086 -0.086 -0.050 -0.050
## 8 ASDQII_14 ~~ ASDQII_17 21.304 0.089 0.089 0.056 0.056
## 9 ASDQII_12 ~~ ASDQII_18 21.154 -0.083 -0.083 -0.052 -0.052
## 10 ASDQII_12 ~~ ASDQII_19 19.658 0.085 0.085 0.054 0.054
## 11 ASDQII_12 ~~ ASDQII_13 19.293 0.089 0.089 0.047 0.047
## 12 AllSubjects =~ ASDQII_11 18.499 0.177 0.180 0.140 0.140
## 13 AllSubjects =~ ASDQII_1 17.718 0.194 0.197 0.166 0.166
## 14 ASDQII_10 ~~ ASDQII_11 16.811 0.069 0.069 0.037 0.037
## 15 ASDQII_2 ~~ ASDQII_12 16.511 0.087 0.087 0.053 0.053
## 16 ASDQII_18 ~~ ASDQII_20 14.859 -0.082 -0.082 -0.055 -0.055
## 17 ASDQII_8 ~~ ASDQII_14 13.437 0.063 0.063 0.031 0.031
## 18 ASDQII_17 ~~ ASDQII_18 13.242 0.077 0.077 0.053 0.053
## 19 ASDQII_12 ~~ ASDQII_17 12.628 -0.067 -0.067 -0.042 -0.042
## 20 ASDQII_1 ~~ ASDQII_3 12.123 -0.081 -0.081 -0.056 -0.056
## 21 ASDQII_9 ~~ ASDQII_18 11.935 -0.065 -0.065 -0.035 -0.035
## 22 ASDQII_15 ~~ ASDQII_18 11.510 0.065 0.065 0.040 0.040
## 23 ASDQII_16 ~~ ASDQII_18 11.322 0.070 0.070 0.048 0.048
## 24 Science =~ ASDQII_18 10.984 -0.135 -0.158 -0.132 -0.132
## 25 ASDQII_6 ~~ ASDQII_17 10.919 -0.070 -0.070 -0.038 -0.038
## 26 English =~ ASDQII_11 10.608 0.100 0.104 0.081 0.081
## 27 ASDQII_4 ~~ ASDQII_17 10.354 0.075 0.075 0.051 0.051
## 28 Science =~ ASDQII_19 10.180 0.136 0.159 0.136 0.136
## 29 ASDQII_9 ~~ ASDQII_12 9.922 0.057 0.057 0.028 0.028
## 30 ASDQII_2 ~~ ASDQII_5 9.878 -0.082 -0.082 -0.053 -0.053
## 31 Science =~ ASDQII_1 9.495 0.096 0.112 0.094 0.094
## 32 ASDQII_3 ~~ ASDQII_7 9.294 -0.066 -0.066 -0.036 -0.036
## 33 Science =~ ASDQII_20 9.187 0.128 0.149 0.122 0.122
## 34 ASDQII_11 ~~ ASDQII_15 8.958 0.057 0.057 0.033 0.033
## 35 ASDQII_1 ~~ ASDQII_5 8.942 0.073 0.073 0.049 0.049
## 36 ASDQII_13 ~~ ASDQII_20 8.695 0.056 0.056 0.033 0.033
## 37 AllSubjects =~ ASDQII_12 8.164 -0.127 -0.129 -0.097 -0.097
## 38 ASDQII_4 ~~ ASDQII_14 7.938 0.062 0.062 0.037 0.037
## 39 ASDQII_9 ~~ ASDQII_11 7.825 -0.047 -0.047 -0.024 -0.024
## 40 ASDQII_11 ~~ ASDQII_13 7.546 -0.053 -0.053 -0.029 -0.029
## 41 ASDQII_12 ~~ ASDQII_16 7.417 -0.050 -0.050 -0.031 -0.031
## 42 ASDQII_9 ~~ ASDQII_19 7.249 0.054 0.054 0.030 0.030
## 43 ASDQII_2 ~~ ASDQII_4 7.249 0.070 0.070 0.045 0.045
## 44 ASDQII_1 ~~ ASDQII_4 7.202 -0.065 -0.065 -0.044 -0.044
## 45 ASDQII_5 ~~ ASDQII_20 7.092 -0.061 -0.061 -0.040 -0.040
## 46 ASDQII_13 ~~ ASDQII_15 6.984 -0.055 -0.055 -0.029 -0.029
## 47 ASDQII_1 ~~ ASDQII_6 6.623 -0.057 -0.057 -0.031 -0.031
## 48 ASDQII_19 ~~ ASDQII_20 6.496 0.056 0.056 0.039 0.039
## 49 ASDQII_13 ~~ ASDQII_19 6.242 0.049 0.049 0.030 0.030
## 50 ASDQII_1 ~~ ASDQII_13 6.208 -0.050 -0.050 -0.030 -0.030
## 51 Math =~ ASDQII_1 5.988 0.058 0.080 0.067 0.067
## 52 ASDQII_13 ~~ ASDQII_14 5.820 0.050 0.050 0.026 0.026
## 53 AllSubjects =~ ASDQII_13 5.743 -0.109 -0.111 -0.079 -0.079
## 54 ASDQII_3 ~~ ASDQII_13 5.728 0.049 0.049 0.029 0.029
## 55 ASDQII_14 ~~ ASDQII_19 5.659 -0.047 -0.047 -0.030 -0.030
## 56 ASDQII_3 ~~ ASDQII_6 5.508 0.053 0.053 0.029 0.029
## 57 ASDQII_11 ~~ ASDQII_20 5.464 -0.040 -0.040 -0.026 -0.026
## 58 ASDQII_5 ~~ ASDQII_14 5.391 -0.051 -0.051 -0.030 -0.030
## 59 ASDQII_12 ~~ ASDQII_20 5.302 0.043 0.043 0.026 0.026
## 60 ASDQII_15 ~~ ASDQII_20 5.250 -0.045 -0.045 -0.028 -0.028
## 61 ASDQII_5 ~~ ASDQII_19 5.249 0.054 0.054 0.037 0.037
## 62 Science =~ ASDQII_16 5.140 -0.094 -0.110 -0.092 -0.092
## 63 ASDQII_3 ~~ ASDQII_20 5.121 0.049 0.049 0.033 0.033
## 64 ASDQII_2 ~~ ASDQII_18 5.085 -0.050 -0.050 -0.034 -0.034
## 65 ASDQII_2 ~~ ASDQII_11 4.793 -0.043 -0.043 -0.027 -0.027
## 66 ASDQII_1 ~~ ASDQII_19 4.702 -0.047 -0.047 -0.034 -0.034
## 67 ASDQII_15 ~~ ASDQII_19 4.689 -0.044 -0.044 -0.028 -0.028
## 68 ASDQII_2 ~~ ASDQII_10 4.600 -0.048 -0.048 -0.027 -0.027
## 69 ASDQII_10 ~~ ASDQII_13 4.510 -0.040 -0.040 -0.019 -0.019
## 70 ASDQII_3 ~~ ASDQII_11 4.472 -0.039 -0.039 -0.026 -0.026
## 71 ASDQII_6 ~~ ASDQII_7 4.468 0.049 0.049 0.021 0.021
## 72 English =~ ASDQII_14 4.260 -0.070 -0.073 -0.054 -0.054
## 73 AllSubjects =~ ASDQII_2 4.242 -0.103 -0.104 -0.084 -0.084
## 74 ASDQII_8 ~~ ASDQII_15 4.215 -0.037 -0.037 -0.018 -0.018
## 75 ASDQII_7 ~~ ASDQII_20 4.183 -0.041 -0.041 -0.022 -0.022
## 76 ASDQII_17 ~~ ASDQII_19 4.054 -0.044 -0.044 -0.031 -0.031
## 77 ASDQII_6 ~~ ASDQII_8 3.994 -0.043 -0.043 -0.019 -0.019
## 78 AllSubjects =~ ASDQII_10 3.971 0.085 0.086 0.059 0.059
## 79 ASDQII_8 ~~ ASDQII_18 3.928 -0.035 -0.035 -0.019 -0.019
## 80 ASDQII_6 ~~ ASDQII_9 3.926 0.045 0.045 0.019 0.019
## 81 Science =~ ASDQII_5 3.857 -0.066 -0.078 -0.062 -0.062modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 ASDQII_8 ~~ ASDQII_9 321.779 0.835 0.835 0.361 0.361
## 2 ASDQII_8 ~~ ASDQII_10 306.464 0.777 0.777 0.354 0.354
## 3 ASDQII_7 ~~ ASDQII_8 301.179 0.827 0.827 0.353 0.353
## 4 ASDQII_6 ~~ ASDQII_9 292.206 0.853 0.853 0.364 0.364
## 5 ASDQII_6 ~~ ASDQII_7 287.758 0.867 0.867 0.365 0.365
## 6 ASDQII_6 ~~ ASDQII_8 277.330 0.808 0.808 0.350 0.350
## 7 ASDQII_7 ~~ ASDQII_9 268.544 0.804 0.804 0.337 0.337
## 8 ASDQII_9 ~~ ASDQII_10 261.456 0.739 0.739 0.330 0.330
## 9 ASDQII_7 ~~ ASDQII_10 259.485 0.754 0.754 0.333 0.333
## 10 ASDQII_12 ~~ ASDQII_13 249.674 0.537 0.537 0.285 0.285
## 11 ASDQII_6 ~~ ASDQII_10 243.160 0.742 0.742 0.333 0.333
## 12 ASDQII_1 ~~ ASDQII_5 215.693 0.696 0.696 0.470 0.470
## 13 ASDQII_2 ~~ ASDQII_4 211.475 0.777 0.777 0.506 0.506
## 14 ASDQII_13 ~~ ASDQII_14 208.783 0.478 0.478 0.253 0.253
## 15 ASDQII_2 ~~ ASDQII_3 207.820 0.731 0.731 0.493 0.493
## 16 ASDQII_3 ~~ ASDQII_4 205.852 0.730 0.730 0.492 0.492
## 17 ASDQII_3 ~~ ASDQII_5 205.078 0.722 0.722 0.482 0.482
## 18 ASDQII_1 ~~ ASDQII_2 194.632 0.666 0.666 0.453 0.453
## 19 ASDQII_11 ~~ ASDQII_15 190.018 0.393 0.393 0.230 0.230
## 20 ASDQII_4 ~~ ASDQII_5 188.110 0.728 0.728 0.470 0.470
## 21 ASDQII_11 ~~ ASDQII_12 175.280 0.381 0.381 0.222 0.222
## 22 ASDQII_1 ~~ ASDQII_3 173.629 0.599 0.599 0.421 0.421
## 23 ASDQII_1 ~~ ASDQII_4 172.652 0.629 0.629 0.427 0.427
## 24 ASDQII_11 ~~ ASDQII_13 172.166 0.392 0.392 0.216 0.216
## 25 ASDQII_12 ~~ ASDQII_15 166.336 0.419 0.419 0.236 0.236
## 26 ASDQII_2 ~~ ASDQII_5 166.199 0.682 0.682 0.441 0.441
## 27 ASDQII_12 ~~ ASDQII_14 160.500 0.404 0.404 0.226 0.226
## 28 ASDQII_11 ~~ ASDQII_14 153.419 0.347 0.347 0.202 0.202
## 29 ASDQII_13 ~~ ASDQII_15 146.706 0.408 0.408 0.218 0.218
## 30 ASDQII_14 ~~ ASDQII_15 139.791 0.374 0.374 0.210 0.210
## 31 ASDQII_3 ~~ ASDQII_7 82.823 -0.475 -0.475 -0.255 -0.255
## 32 ASDQII_16 ~~ ASDQII_18 81.957 0.214 0.214 0.148 0.148
## 33 ASDQII_12 ~~ ASDQII_18 78.916 -0.245 -0.245 -0.152 -0.152
## 34 ASDQII_13 ~~ ASDQII_18 77.388 -0.251 -0.251 -0.148 -0.148
## 35 ASDQII_1 ~~ ASDQII_6 74.523 -0.432 -0.432 -0.238 -0.238
## 36 ASDQII_1 ~~ ASDQII_9 74.350 -0.413 -0.413 -0.227 -0.227
## 37 ASDQII_17 ~~ ASDQII_18 73.443 0.202 0.202 0.140 0.140
## 38 ASDQII_2 ~~ ASDQII_7 72.078 -0.465 -0.465 -0.242 -0.242
## 39 ASDQII_1 ~~ ASDQII_7 72.042 -0.417 -0.417 -0.226 -0.226
## 40 ASDQII_1 ~~ ASDQII_8 69.937 -0.390 -0.390 -0.218 -0.218
## 41 ASDQII_4 ~~ ASDQII_7 69.109 -0.457 -0.457 -0.237 -0.237
## 42 ASDQII_2 ~~ ASDQII_8 68.428 -0.430 -0.430 -0.230 -0.230
## 43 ASDQII_4 ~~ ASDQII_9 62.418 -0.424 -0.424 -0.223 -0.223
## 44 ASDQII_2 ~~ ASDQII_10 62.287 -0.403 -0.403 -0.223 -0.223
## 45 ASDQII_3 ~~ ASDQII_9 59.152 -0.392 -0.392 -0.213 -0.213
## 46 ASDQII_2 ~~ ASDQII_9 58.389 -0.409 -0.409 -0.215 -0.215
## 47 ASDQII_4 ~~ ASDQII_6 58.261 -0.427 -0.427 -0.225 -0.225
## 48 ASDQII_2 ~~ ASDQII_6 58.016 -0.425 -0.425 -0.224 -0.224
## 49 ASDQII_3 ~~ ASDQII_8 57.305 -0.375 -0.375 -0.208 -0.208
## 50 ASDQII_4 ~~ ASDQII_8 54.123 -0.384 -0.384 -0.205 -0.205
## 51 ASDQII_4 ~~ ASDQII_10 54.030 -0.376 -0.376 -0.208 -0.208
## 52 ASDQII_9 ~~ ASDQII_11 52.510 -0.252 -0.252 -0.128 -0.128
## 53 ASDQII_8 ~~ ASDQII_11 51.588 -0.243 -0.243 -0.125 -0.125
## 54 ASDQII_6 ~~ ASDQII_11 51.474 -0.260 -0.260 -0.132 -0.132
## 55 ASDQII_3 ~~ ASDQII_10 50.218 -0.344 -0.344 -0.197 -0.197
## 56 ASDQII_14 ~~ ASDQII_18 49.331 -0.188 -0.188 -0.117 -0.117
## 57 ASDQII_10 ~~ ASDQII_13 47.868 -0.271 -0.271 -0.132 -0.132
## 58 ASDQII_12 ~~ ASDQII_16 47.857 -0.194 -0.194 -0.121 -0.121
## 59 ASDQII_10 ~~ ASDQII_12 47.818 -0.261 -0.261 -0.134 -0.134
## 60 ASDQII_7 ~~ ASDQII_11 47.255 -0.245 -0.245 -0.123 -0.123
## 61 ASDQII_3 ~~ ASDQII_6 46.675 -0.363 -0.363 -0.198 -0.198
## 62 ASDQII_16 ~~ ASDQII_17 45.805 0.163 0.163 0.113 0.113
## 63 ASDQII_12 ~~ ASDQII_17 44.548 -0.187 -0.187 -0.117 -0.117
## 64 ASDQII_10 ~~ ASDQII_15 43.964 -0.248 -0.248 -0.128 -0.128
## 65 ASDQII_1 ~~ ASDQII_18 42.795 0.218 0.218 0.153 0.153
## 66 ASDQII_13 ~~ ASDQII_16 41.411 -0.188 -0.188 -0.111 -0.111
## 67 ASDQII_8 ~~ ASDQII_15 40.818 -0.244 -0.244 -0.122 -0.122
## 68 ASDQII_5 ~~ ASDQII_6 40.539 -0.353 -0.353 -0.185 -0.185
## 69 ASDQII_5 ~~ ASDQII_7 40.368 -0.346 -0.346 -0.178 -0.178
## 70 ASDQII_5 ~~ ASDQII_14 40.072 -0.271 -0.271 -0.162 -0.162
## 71 ASDQII_10 ~~ ASDQII_11 37.643 -0.203 -0.203 -0.109 -0.109
## 72 ASDQII_7 ~~ ASDQII_12 36.520 -0.245 -0.245 -0.118 -0.118
## 73 ASDQII_5 ~~ ASDQII_8 35.833 -0.309 -0.309 -0.164 -0.164
## 74 ASDQII_5 ~~ ASDQII_9 35.774 -0.318 -0.318 -0.166 -0.166
## 75 ASDQII_10 ~~ ASDQII_14 35.305 -0.219 -0.219 -0.112 -0.112
## 76 ASDQII_8 ~~ ASDQII_12 31.301 -0.215 -0.215 -0.107 -0.107
## 77 ASDQII_1 ~~ ASDQII_10 30.691 -0.253 -0.253 -0.147 -0.147
## 78 ASDQII_5 ~~ ASDQII_10 30.502 -0.280 -0.280 -0.154 -0.154
## 79 ASDQII_7 ~~ ASDQII_13 30.284 -0.232 -0.232 -0.106 -0.106
## 80 ASDQII_7 ~~ ASDQII_15 29.842 -0.219 -0.219 -0.106 -0.106
## 81 ASDQII_13 ~~ ASDQII_17 29.055 -0.157 -0.157 -0.093 -0.093
## 82 ASDQII_8 ~~ ASDQII_13 28.287 -0.212 -0.212 -0.100 -0.100
## 83 ASDQII_6 ~~ ASDQII_15 28.160 -0.217 -0.217 -0.107 -0.107
## 84 ASDQII_19 ~~ ASDQII_20 27.572 0.117 0.117 0.081 0.081
## 85 ASDQII_7 ~~ ASDQII_14 26.396 -0.203 -0.203 -0.097 -0.097
## 86 ASDQII_6 ~~ ASDQII_14 25.892 -0.204 -0.204 -0.100 -0.100
## 87 ASDQII_16 ~~ ASDQII_20 24.569 0.114 0.114 0.077 0.077
## 88 ASDQII_6 ~~ ASDQII_12 22.983 -0.198 -0.198 -0.097 -0.097
## 89 ASDQII_1 ~~ ASDQII_14 22.822 -0.184 -0.184 -0.116 -0.116
## 90 ASDQII_10 ~~ ASDQII_19 21.698 -0.146 -0.146 -0.086 -0.086
## 91 ASDQII_9 ~~ ASDQII_13 21.514 -0.191 -0.191 -0.088 -0.088
## 92 ASDQII_14 ~~ ASDQII_16 21.464 -0.127 -0.127 -0.079 -0.079
## 93 ASDQII_9 ~~ ASDQII_15 21.446 -0.182 -0.182 -0.089 -0.089
## 94 ASDQII_15 ~~ ASDQII_20 20.748 -0.121 -0.121 -0.074 -0.074
## 95 ASDQII_6 ~~ ASDQII_13 20.694 -0.195 -0.195 -0.090 -0.090
## 96 ASDQII_3 ~~ ASDQII_14 20.645 -0.186 -0.186 -0.116 -0.116
## 97 ASDQII_3 ~~ ASDQII_20 19.699 0.152 0.152 0.104 0.104
## 98 ASDQII_15 ~~ ASDQII_17 18.507 -0.119 -0.119 -0.075 -0.075
## 99 ASDQII_9 ~~ ASDQII_14 18.147 -0.164 -0.164 -0.080 -0.080
## 100 ASDQII_5 ~~ ASDQII_16 17.987 0.160 0.160 0.106 0.106
## 101 ASDQII_4 ~~ ASDQII_17 17.810 0.160 0.160 0.108 0.108
## 102 ASDQII_5 ~~ ASDQII_12 17.752 -0.185 -0.185 -0.111 -0.111
## 103 ASDQII_11 ~~ ASDQII_20 16.274 -0.095 -0.095 -0.060 -0.060
## 104 ASDQII_5 ~~ ASDQII_13 16.060 -0.183 -0.183 -0.104 -0.104
## 105 ASDQII_1 ~~ ASDQII_20 15.950 0.129 0.129 0.089 0.089
## 106 ASDQII_5 ~~ ASDQII_18 15.885 0.147 0.147 0.098 0.098
## 107 ASDQII_16 ~~ ASDQII_19 15.816 0.093 0.093 0.066 0.066
## 108 ASDQII_14 ~~ ASDQII_19 15.772 -0.105 -0.105 -0.067 -0.067
## 109 ASDQII_1 ~~ ASDQII_13 15.633 -0.163 -0.163 -0.097 -0.097
## 110 ASDQII_6 ~~ ASDQII_17 15.243 -0.138 -0.138 -0.075 -0.075
## 111 ASDQII_11 ~~ ASDQII_16 15.218 -0.096 -0.096 -0.062 -0.062
## 112 ASDQII_17 ~~ ASDQII_20 15.003 0.089 0.089 0.060 0.060
## 113 ASDQII_2 ~~ ASDQII_14 14.778 -0.165 -0.165 -0.100 -0.100
## 114 ASDQII_9 ~~ ASDQII_12 14.626 -0.152 -0.152 -0.074 -0.074
## 115 ASDQII_2 ~~ ASDQII_20 14.490 0.137 0.137 0.090 0.090
## 116 ASDQII_5 ~~ ASDQII_19 13.815 0.136 0.136 0.092 0.092
## 117 ASDQII_15 ~~ ASDQII_19 13.545 -0.099 -0.099 -0.064 -0.064
## 118 ASDQII_18 ~~ ASDQII_19 13.278 0.084 0.084 0.059 0.059
## 119 ASDQII_15 ~~ ASDQII_18 13.099 -0.099 -0.099 -0.062 -0.062
## 120 ASDQII_1 ~~ ASDQII_16 12.952 0.122 0.122 0.086 0.086
## 121 ASDQII_15 ~~ ASDQII_16 12.902 -0.100 -0.100 -0.063 -0.063
## 122 ASDQII_5 ~~ ASDQII_11 12.072 -0.134 -0.134 -0.083 -0.083
## 123 ASDQII_8 ~~ ASDQII_14 11.705 -0.128 -0.128 -0.063 -0.063
## 124 ASDQII_5 ~~ ASDQII_15 11.414 -0.147 -0.147 -0.089 -0.089
## 125 ASDQII_3 ~~ ASDQII_18 11.282 0.119 0.119 0.082 0.082
## 126 ASDQII_9 ~~ ASDQII_17 10.929 -0.112 -0.112 -0.061 -0.061
## 127 ASDQII_1 ~~ ASDQII_17 10.880 0.112 0.112 0.078 0.078
## 128 ASDQII_4 ~~ ASDQII_16 10.732 0.124 0.124 0.084 0.084
## 129 ASDQII_11 ~~ ASDQII_17 9.965 -0.078 -0.078 -0.050 -0.050
## 130 ASDQII_7 ~~ ASDQII_20 9.727 -0.103 -0.103 -0.054 -0.054
## 131 ASDQII_8 ~~ ASDQII_16 9.690 -0.103 -0.103 -0.057 -0.057
## 132 ASDQII_9 ~~ ASDQII_18 9.555 -0.103 -0.103 -0.056 -0.056
## 133 ASDQII_3 ~~ ASDQII_19 9.376 0.107 0.107 0.076 0.076
## 134 ASDQII_2 ~~ ASDQII_16 9.300 0.115 0.115 0.078 0.078
## 135 ASDQII_3 ~~ ASDQII_16 9.125 0.109 0.109 0.076 0.076
## 136 ASDQII_1 ~~ ASDQII_12 9.096 -0.120 -0.120 -0.075 -0.075
## 137 ASDQII_18 ~~ ASDQII_20 8.706 0.066 0.066 0.045 0.045
## 138 ASDQII_3 ~~ ASDQII_11 8.443 -0.107 -0.107 -0.070 -0.070
## 139 ASDQII_3 ~~ ASDQII_15 8.127 -0.119 -0.119 -0.075 -0.075
## 140 ASDQII_4 ~~ ASDQII_15 7.686 -0.122 -0.122 -0.074 -0.074
## 141 ASDQII_4 ~~ ASDQII_18 7.308 0.101 0.101 0.067 0.067
## 142 ASDQII_4 ~~ ASDQII_13 7.104 -0.123 -0.123 -0.070 -0.070
## 143 ASDQII_9 ~~ ASDQII_20 6.958 -0.085 -0.085 -0.045 -0.045
## 144 ASDQII_9 ~~ ASDQII_16 6.956 -0.090 -0.090 -0.049 -0.049
## 145 ASDQII_12 ~~ ASDQII_20 6.896 -0.070 -0.070 -0.043 -0.043
## 146 ASDQII_4 ~~ ASDQII_14 6.699 -0.112 -0.112 -0.067 -0.067
## 147 ASDQII_17 ~~ ASDQII_19 6.666 0.060 0.060 0.043 0.043
## 148 ASDQII_11 ~~ ASDQII_18 6.401 -0.061 -0.061 -0.040 -0.040
## 149 ASDQII_14 ~~ ASDQII_20 6.177 -0.065 -0.065 -0.039 -0.039
## 150 ASDQII_6 ~~ ASDQII_16 6.165 -0.088 -0.088 -0.048 -0.048
## 151 ASDQII_6 ~~ ASDQII_19 6.091 -0.085 -0.085 -0.047 -0.047
## 152 ASDQII_5 ~~ ASDQII_17 5.990 0.092 0.092 0.061 0.061
## 153 ASDQII_8 ~~ ASDQII_20 5.932 -0.076 -0.076 -0.041 -0.041
## 154 ASDQII_1 ~~ ASDQII_15 5.855 -0.095 -0.095 -0.060 -0.060
## 155 ASDQII_2 ~~ ASDQII_19 5.730 0.088 0.088 0.060 0.060
## 156 ASDQII_2 ~~ ASDQII_18 5.668 0.088 0.088 0.059 0.059
## 157 ASDQII_4 ~~ ASDQII_20 5.622 0.086 0.086 0.056 0.056
## 158 ASDQII_2 ~~ ASDQII_13 5.036 -0.103 -0.103 -0.059 -0.059
## 159 ASDQII_13 ~~ ASDQII_20 4.874 -0.061 -0.061 -0.035 -0.035
## 160 ASDQII_3 ~~ ASDQII_13 4.850 -0.096 -0.096 -0.057 -0.057
## 161 ASDQII_3 ~~ ASDQII_17 4.808 0.079 0.079 0.055 0.055
## 162 ASDQII_2 ~~ ASDQII_11 4.747 -0.084 -0.084 -0.053 -0.053
## 163 ASDQII_5 ~~ ASDQII_20 4.544 0.076 0.076 0.050 0.050
## 164 ASDQII_8 ~~ ASDQII_18 4.272 -0.067 -0.067 -0.037 -0.037
## 165 ASDQII_11 ~~ ASDQII_19 4.123 -0.049 -0.049 -0.032 -0.032
## 166 ASDQII_8 ~~ ASDQII_19 4.076 -0.065 -0.065 -0.037 -0.037
## 167 ASDQII_4 ~~ ASDQII_11 4.039 -0.078 -0.078 -0.049 -0.049
## 168 ASDQII_10 ~~ ASDQII_18 3.974 0.063 0.063 0.036 0.036
## 169 ASDQII_8 ~~ ASDQII_17 3.940 -0.065 -0.065 -0.036 -0.036Fit Measures
fitmeasures(four.fit)## npar fmin chisq
## 66.000 0.472 564.361
## df pvalue baseline.chisq
## 164.000 0.000 12120.073
## baseline.df baseline.pvalue cfi
## 190.000 0.000 0.966
## tli nnfi rfi
## 0.961 0.961 0.946
## nfi pnfi ifi
## 0.953 0.823 0.967
## rni logl unrestricted.logl
## 0.966 -13454.178 -13171.998
## aic bic ntotal
## 27040.356 27330.333 598.000
## bic2 rmsea rmsea.ci.lower
## 27120.802 0.064 0.058
## rmsea.ci.upper rmsea.pvalue rmr
## 0.070 0.000 0.048
## rmr_nomean srmr srmr_bentler
## 0.050 0.029 0.029
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.030 0.029 0.030
## srmr_mplus srmr_mplus_nomean cn_05
## 0.029 0.030 207.499
## cn_01 gfi agfi
## 222.508 0.967 0.954
## pgfi mfi ecvi
## 0.690 0.716 NAfitmeasures(one.fit)## npar fmin chisq
## 60.000 4.327 5174.878
## df pvalue baseline.chisq
## 170.000 0.000 12120.073
## baseline.df baseline.pvalue cfi
## 190.000 0.000 0.580
## tli nnfi rfi
## 0.531 0.531 0.523
## nfi pnfi ifi
## 0.573 0.513 0.581
## rni logl unrestricted.logl
## 0.580 -15759.437 -13171.998
## aic bic ntotal
## 31638.874 31902.489 598.000
## bic2 rmsea rmsea.ci.lower
## 31712.006 0.222 0.217
## rmsea.ci.upper rmsea.pvalue rmr
## 0.227 0.000 0.269
## rmr_nomean srmr srmr_bentler
## 0.281 0.149 0.149
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.156 0.149 0.156
## srmr_mplus srmr_mplus_nomean cn_05
## 0.149 0.156 24.276
## cn_01 gfi agfi
## 25.939 0.735 0.641
## pgfi mfi ecvi
## 0.543 0.015 NACreate dataset for Target rotation
ASDQIITR<-select(data, ASDQII_1 , ASDQII_2 , ASDQII_3, ASDQII_4 , ASDQII_5 , ASDQII_6 ,ASDQII_7 , ASDQII_8 , ASDQII_9 , ASDQII_10 , ASDQII_11 , ASDQII_12 , ASDQII_13 , ASDQII_14 , ASDQII_15, ASDQII_16, ASDQII_17, ASDQII_18, ASDQII_19, ASDQII_20)
colnames(ASDQIITR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20")
#Target RorationTarg_key <- make.keys(20,list(f1=1:5,f2=6:10, f3=11:15, f4=16:10))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
ASDQIITR_cor<- corFiml(ASDQIITR)
out_targetQ <- fa(ASDQIITR_cor,4,rotate="TargetQ", n.obs = 670, Target=Targ_key) #TargetT for orthogonal rotation## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A Heywood case was detected. Examine the loadings carefully.out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA","uniquenesses")]## $loadings
##
## Loadings:
## MR3 MR2 MR1 MR4
## 1 0.794 -0.130
## 2 0.926 0.119
## 3 0.932
## 4 0.903
## 5 -0.174 0.915
## 6 0.960
## 7 0.913
## 8 0.965
## 9 0.963
## 10 0.896 -0.112
## 11 1.077 0.211
## 12 1.142 0.531
## 13 1.149 0.476
## 14 1.109 0.310
## 15 1.074 0.298
## 16 0.215 0.182 0.219 -0.404
## 17 0.277 0.162 0.152 -0.408
## 18 0.165 0.187 0.177 -0.535
## 19 0.359 0.225 0.246 -0.131
## 20 0.342 0.247 0.264 -0.152
##
## MR3 MR2 MR1 MR4
## SS loadings 6.604 4.642 4.260 1.466
## Proportion Var 0.330 0.232 0.213 0.073
## Cumulative Var 0.330 0.562 0.775 0.849
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.6089581 0.5209355 -0.7461335
## [2,] 0.6089581 1.0000000 0.2289255 -0.6390801
## [3,] 0.5209355 0.2289255 1.0000000 -0.6175495
## [4,] -0.7461335 -0.6390801 -0.6175495 1.0000000
##
## $TLI
## [1] 0.9618415
##
## $RMSEA
## RMSEA lower upper confidence
## 0.06513303 0.05806549 0.07087888 0.10000000
##
## $uniquenesses
## 1 2 3 4 5 6 7
## 0.2401260 0.2598505 0.2416251 0.2788012 0.2599476 0.1872853 0.1677475
## 8 9 10 11 12 13 14
## 0.1347453 0.1505092 0.1806419 0.1706160 0.1635214 0.1633345 0.2038688
## 15 16 17 18 19 20
## 0.2381045 0.2814359 0.2881553 0.1909557 0.3636819 0.3093925out_targetQ## Factor Analysis using method = minres
## Call: fa(r = ASDQIITR_cor, nfactors = 4, n.obs = 670, rotate = "TargetQ",
## Target = Targ_key)
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR3 MR2 MR1 MR4 h2 u2 com
## 1 0.04 -0.08 0.79 -0.13 0.76 0.24 1.1
## 2 0.02 -0.06 0.93 0.12 0.74 0.26 1.0
## 3 -0.03 -0.02 0.93 0.07 0.76 0.24 1.0
## 4 -0.02 -0.06 0.90 0.05 0.72 0.28 1.0
## 5 -0.17 0.06 0.91 -0.04 0.74 0.26 1.1
## 6 -0.04 0.96 -0.03 0.05 0.81 0.19 1.0
## 7 -0.01 0.91 -0.09 -0.07 0.83 0.17 1.0
## 8 -0.02 0.96 0.00 0.04 0.87 0.13 1.0
## 9 0.01 0.96 0.00 0.10 0.85 0.15 1.0
## 10 -0.06 0.90 -0.03 -0.11 0.82 0.18 1.0
## 11 1.08 -0.04 -0.02 0.21 0.83 0.17 1.1
## 12 1.14 0.03 0.06 0.53 0.84 0.16 1.4
## 13 1.15 0.03 0.02 0.48 0.84 0.16 1.3
## 14 1.11 0.02 -0.08 0.31 0.80 0.20 1.2
## 15 1.07 -0.02 -0.02 0.30 0.76 0.24 1.2
## 16 0.21 0.18 0.22 -0.40 0.72 0.28 2.6
## 17 0.28 0.16 0.15 -0.41 0.71 0.29 2.5
## 18 0.17 0.19 0.18 -0.54 0.81 0.19 1.7
## 19 0.36 0.22 0.25 -0.13 0.64 0.36 2.9
## 20 0.34 0.25 0.26 -0.15 0.69 0.31 3.2
##
## MR3 MR2 MR1 MR4
## SS loadings 5.64 4.88 4.43 0.59
## Proportion Var 0.28 0.24 0.22 0.03
## Cumulative Var 0.28 0.53 0.75 0.78
## Proportion Explained 0.36 0.31 0.29 0.04
## Cumulative Proportion 0.36 0.68 0.96 1.00
##
## With factor correlations of
## MR3 MR2 MR1 MR4
## MR3 1.00 0.65 0.60 -0.69
## MR2 0.65 1.00 0.34 -0.53
## MR1 0.60 0.34 1.00 -0.55
## MR4 -0.69 -0.53 -0.55 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 21.3 with Chi Square of 14087.95
## The degrees of freedom for the model are 116 and the objective function was 0.67
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 670 with the empirical chi square 54.91 with prob < 1
## The total number of observations was 670 with MLE Chi Square = 438.45 with prob < 5.5e-39
##
## Tucker Lewis Index of factoring reliability = 0.962
## RMSEA index = 0.065 and the 90 % confidence intervals are 0.058 0.071
## BIC = -316.39
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR3 MR2 MR1 MR4
## Correlation of scores with factors 0.98 0.98 0.97 0.92
## Multiple R square of scores with factors 0.96 0.97 0.94 0.84
## Minimum correlation of possible factor scores 0.92 0.93 0.89 0.68CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9767984