Primary Theoretical Model
8 Factor CFA
sps.cfa.8 <- '
tacphys =~ A1Q1+ A1Q2+ A1Q3
musclejointbone =~ A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3
soundjr =~ B2Q1+ B2Q2
tactilejr =~ B3Q1+ B3Q2
inc =~ .90*CQ1
energy =~ .90*DQ1
urge =~ .90*EQ1
'
fit.sps.cfa.8 <- cfa(sps.cfa.8, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.8, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 33 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 55
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 56.089 91.394
## Degrees of freedom 65 65
## P-value (Chi-square) 0.777 0.017
## Scaling correction factor 0.725
## Shift parameter 13.980
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.992
## Tucker-Lewis Index (TLI) 1.003 0.986
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.029
## 90 Percent confidence interval - lower 0.000 0.013
## 90 Percent confidence interval - upper 0.019 0.042
## P-value RMSEA <= 0.05 1.000 0.998
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.061 0.061
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys =~
## A1Q1 0.595 0.069 8.592 0.000 0.595 0.595
## A1Q2 0.690 0.077 8.904 0.000 0.690 0.690
## A1Q3 0.673 0.073 9.221 0.000 0.673 0.673
## musclejointbone =~
## A2Q1 0.761 0.072 10.622 0.000 0.761 0.761
## A2Q2 0.864 0.071 12.208 0.000 0.864 0.864
## lookjr =~
## B1Q1 0.968 0.025 38.202 0.000 0.968 0.968
## B1Q2 0.952 0.023 42.025 0.000 0.952 0.952
## B1Q3 0.853 0.032 26.274 0.000 0.853 0.853
## soundjr =~
## B2Q1 0.851 0.051 16.608 0.000 0.851 0.851
## B2Q2 0.906 0.048 18.734 0.000 0.906 0.906
## tactilejr =~
## B3Q1 0.860 0.061 14.062 0.000 0.860 0.860
## B3Q2 0.769 0.063 12.192 0.000 0.769 0.769
## inc =~
## CQ1 0.900 0.900 0.900
## energy =~
## DQ1 0.900 0.900 0.900
## urge =~
## EQ1 0.900 0.900 0.900
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys ~~
## musclejointbon 0.863 0.104 8.293 0.000 0.863 0.863
## lookjr 0.747 0.072 10.405 0.000 0.747 0.747
## soundjr 0.753 0.101 7.468 0.000 0.753 0.753
## tactilejr 0.854 0.085 10.064 0.000 0.854 0.854
## inc 0.414 0.111 3.726 0.000 0.414 0.414
## energy 0.494 0.118 4.188 0.000 0.494 0.494
## urge 0.457 0.112 4.071 0.000 0.457 0.457
## musclejointbone ~~
## lookjr 0.511 0.086 5.945 0.000 0.511 0.511
## soundjr 0.715 0.094 7.618 0.000 0.715 0.715
## tactilejr 0.694 0.108 6.397 0.000 0.694 0.694
## inc 0.397 0.119 3.325 0.001 0.397 0.397
## energy 0.622 0.106 5.865 0.000 0.622 0.622
## urge 0.466 0.109 4.264 0.000 0.466 0.466
## lookjr ~~
## soundjr 0.663 0.067 9.894 0.000 0.663 0.663
## tactilejr 0.627 0.072 8.676 0.000 0.627 0.627
## inc 0.432 0.077 5.642 0.000 0.432 0.432
## energy 0.489 0.081 6.043 0.000 0.489 0.489
## urge 0.467 0.071 6.545 0.000 0.467 0.467
## soundjr ~~
## tactilejr 0.852 0.074 11.497 0.000 0.852 0.852
## inc 0.452 0.104 4.339 0.000 0.452 0.452
## energy 0.524 0.105 4.995 0.000 0.524 0.524
## urge 0.614 0.093 6.606 0.000 0.614 0.614
## tactilejr ~~
## inc 0.353 0.113 3.109 0.002 0.353 0.353
## energy 0.523 0.110 4.755 0.000 0.523 0.523
## urge 0.582 0.097 6.028 0.000 0.582 0.582
## inc ~~
## energy 0.589 0.098 6.003 0.000 0.589 0.589
## urge 0.439 0.097 4.528 0.000 0.439 0.439
## energy ~~
## urge 0.399 0.106 3.766 0.000 0.399 0.399
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tacphys 0.000 0.000 0.000
## musclejointbon 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## soundjr 0.000 0.000 0.000
## tactilejr 0.000 0.000 0.000
## inc 0.000 0.000 0.000
## energy 0.000 0.000 0.000
## urge 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.646 0.646 0.646
## .A1Q2 0.525 0.525 0.525
## .A1Q3 0.548 0.548 0.548
## .A2Q1 0.420 0.420 0.420
## .A2Q2 0.253 0.253 0.253
## .B1Q1 0.063 0.063 0.063
## .B1Q2 0.094 0.094 0.094
## .B1Q3 0.272 0.272 0.272
## .B2Q1 0.275 0.275 0.275
## .B2Q2 0.180 0.180 0.180
## .B3Q1 0.261 0.261 0.261
## .B3Q2 0.409 0.409 0.409
## .CQ1 0.190 0.190 0.190
## .DQ1 0.190 0.190 0.190
## .EQ1 0.190 0.190 0.190
## tacphys 1.000 1.000 1.000
## musclejointbon 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## soundjr 1.000 1.000 1.000
## tactilejr 1.000 1.000 1.000
## inc 1.000 1.000 1.000
## energy 1.000 1.000 1.000
## urge 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.8, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tacphys =~ A1Q1 0.595 0.069 8.592 0.000 0.481
## 2 tacphys =~ A1Q2 0.690 0.077 8.904 0.000 0.562
## 3 tacphys =~ A1Q3 0.673 0.073 9.221 0.000 0.553
## 4 musclejointbone =~ A2Q1 0.761 0.072 10.622 0.000 0.644
## 5 musclejointbone =~ A2Q2 0.864 0.071 12.208 0.000 0.748
## 6 lookjr =~ B1Q1 0.968 0.025 38.202 0.000 0.926
## 7 lookjr =~ B1Q2 0.952 0.023 42.025 0.000 0.915
## 8 lookjr =~ B1Q3 0.853 0.032 26.274 0.000 0.800
## 9 soundjr =~ B2Q1 0.851 0.051 16.608 0.000 0.767
## 10 soundjr =~ B2Q2 0.906 0.048 18.734 0.000 0.826
## 11 tactilejr =~ B3Q1 0.860 0.061 14.062 0.000 0.759
## 12 tactilejr =~ B3Q2 0.769 0.063 12.192 0.000 0.665
## 13 inc =~ CQ1 0.900 0.000 NA NA 0.900
## 14 energy =~ DQ1 0.900 0.000 NA NA 0.900
## 15 urge =~ EQ1 0.900 0.000 NA NA 0.900
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130
## 19 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 20 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359
## 21 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203
## 22 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561
## 23 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787
## 24 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 25 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162
## 26 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218
## 27 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592
## 31 A1Q1 ~~ A1Q1 0.646 0.082 7.850 0.000 0.511
## 32 A1Q2 ~~ A1Q2 0.525 0.107 4.911 0.000 0.349
## 33 A1Q3 ~~ A1Q3 0.548 0.098 5.584 0.000 0.386
## 34 A2Q1 ~~ A2Q1 0.420 0.109 3.848 0.000 0.241
## 35 A2Q2 ~~ A2Q2 0.253 0.122 2.071 0.038 0.052
## 36 B1Q1 ~~ B1Q1 0.063 0.049 1.281 0.200 -0.018
## 37 B1Q2 ~~ B1Q2 0.094 0.043 2.183 0.029 0.023
## 38 B1Q3 ~~ B1Q3 0.272 0.055 4.906 0.000 0.181
## 39 B2Q1 ~~ B2Q1 0.275 0.087 3.157 0.002 0.132
## 40 B2Q2 ~~ B2Q2 0.180 0.088 2.056 0.040 0.036
## 41 B3Q1 ~~ B3Q1 0.261 0.105 2.483 0.013 0.088
## 42 B3Q2 ~~ B3Q2 0.409 0.097 4.213 0.000 0.249
## 43 CQ1 ~~ CQ1 0.190 0.000 NA NA 0.190
## 44 DQ1 ~~ DQ1 0.190 0.000 NA NA 0.190
## 45 EQ1 ~~ EQ1 0.190 0.000 NA NA 0.190
## 46 tacphys ~~ tacphys 1.000 0.000 NA NA 1.000
## 47 musclejointbone ~~ musclejointbone 1.000 0.000 NA NA 1.000
## 48 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000
## 49 soundjr ~~ soundjr 1.000 0.000 NA NA 1.000
## 50 tactilejr ~~ tactilejr 1.000 0.000 NA NA 1.000
## 51 inc ~~ inc 1.000 0.000 NA NA 1.000
## 52 energy ~~ energy 1.000 0.000 NA NA 1.000
## 53 urge ~~ urge 1.000 0.000 NA NA 1.000
## 54 tacphys ~~ musclejointbone 0.863 0.104 8.293 0.000 0.692
## 55 tacphys ~~ lookjr 0.747 0.072 10.405 0.000 0.629
## 56 tacphys ~~ soundjr 0.753 0.101 7.468 0.000 0.587
## 57 tacphys ~~ tactilejr 0.854 0.085 10.064 0.000 0.715
## 58 tacphys ~~ inc 0.414 0.111 3.726 0.000 0.231
## 59 tacphys ~~ energy 0.494 0.118 4.188 0.000 0.300
## 60 tacphys ~~ urge 0.457 0.112 4.071 0.000 0.272
## 61 musclejointbone ~~ lookjr 0.511 0.086 5.945 0.000 0.370
## 62 musclejointbone ~~ soundjr 0.715 0.094 7.618 0.000 0.560
## 63 musclejointbone ~~ tactilejr 0.694 0.108 6.397 0.000 0.515
## 64 musclejointbone ~~ inc 0.397 0.119 3.325 0.001 0.200
## 65 musclejointbone ~~ energy 0.622 0.106 5.865 0.000 0.448
## 66 musclejointbone ~~ urge 0.466 0.109 4.264 0.000 0.286
## 67 lookjr ~~ soundjr 0.663 0.067 9.894 0.000 0.553
## 68 lookjr ~~ tactilejr 0.627 0.072 8.676 0.000 0.508
## 69 lookjr ~~ inc 0.432 0.077 5.642 0.000 0.306
## 70 lookjr ~~ energy 0.489 0.081 6.043 0.000 0.356
## 71 lookjr ~~ urge 0.467 0.071 6.545 0.000 0.350
## 72 soundjr ~~ tactilejr 0.852 0.074 11.497 0.000 0.730
## 73 soundjr ~~ inc 0.452 0.104 4.339 0.000 0.280
## 74 soundjr ~~ energy 0.524 0.105 4.995 0.000 0.351
## 75 soundjr ~~ urge 0.614 0.093 6.606 0.000 0.461
## 76 tactilejr ~~ inc 0.353 0.113 3.109 0.002 0.166
## 77 tactilejr ~~ energy 0.523 0.110 4.755 0.000 0.342
## 78 tactilejr ~~ urge 0.582 0.097 6.028 0.000 0.423
## 79 inc ~~ energy 0.589 0.098 6.003 0.000 0.428
## 80 inc ~~ urge 0.439 0.097 4.528 0.000 0.280
## 81 energy ~~ urge 0.399 0.106 3.766 0.000 0.225
## 82 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000
## 83 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000
## 84 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000
## 85 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000
## 86 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000
## 87 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000
## 88 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000
## 89 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000
## 90 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000
## 91 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000
## 92 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000
## 93 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000
## 94 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000
## 95 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000
## 96 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000
## 97 A1Q1 ~1 0.000 0.000 NA NA 0.000
## 98 A1Q2 ~1 0.000 0.000 NA NA 0.000
## 99 A1Q3 ~1 0.000 0.000 NA NA 0.000
## 100 A2Q1 ~1 0.000 0.000 NA NA 0.000
## 101 A2Q2 ~1 0.000 0.000 NA NA 0.000
## 102 B1Q1 ~1 0.000 0.000 NA NA 0.000
## 103 B1Q2 ~1 0.000 0.000 NA NA 0.000
## 104 B1Q3 ~1 0.000 0.000 NA NA 0.000
## 105 B2Q1 ~1 0.000 0.000 NA NA 0.000
## 106 B2Q2 ~1 0.000 0.000 NA NA 0.000
## 107 B3Q1 ~1 0.000 0.000 NA NA 0.000
## 108 B3Q2 ~1 0.000 0.000 NA NA 0.000
## 109 CQ1 ~1 0.000 0.000 NA NA 0.000
## 110 DQ1 ~1 0.000 0.000 NA NA 0.000
## 111 EQ1 ~1 0.000 0.000 NA NA 0.000
## 112 tacphys ~1 0.000 0.000 NA NA 0.000
## 113 musclejointbone ~1 0.000 0.000 NA NA 0.000
## 114 lookjr ~1 0.000 0.000 NA NA 0.000
## 115 soundjr ~1 0.000 0.000 NA NA 0.000
## 116 tactilejr ~1 0.000 0.000 NA NA 0.000
## 117 inc ~1 0.000 0.000 NA NA 0.000
## 118 energy ~1 0.000 0.000 NA NA 0.000
## 119 urge ~1 0.000 0.000 NA NA 0.000
## ci.upper
## 1 0.709
## 2 0.817
## 3 0.792
## 4 0.879
## 5 0.981
## 6 1.010
## 7 0.989
## 8 0.907
## 9 0.935
## 10 0.985
## 11 0.960
## 12 0.873
## 13 0.900
## 14 0.900
## 15 0.900
## 16 0.949
## 17 1.466
## 18 1.379
## 19 1.493
## 20 1.645
## 21 0.392
## 22 0.762
## 23 1.002
## 24 1.493
## 25 1.415
## 26 1.480
## 27 1.311
## 28 1.002
## 29 1.209
## 30 0.795
## 31 0.782
## 32 0.700
## 33 0.709
## 34 0.600
## 35 0.455
## 36 0.144
## 37 0.165
## 38 0.363
## 39 0.419
## 40 0.324
## 41 0.434
## 42 0.568
## 43 0.190
## 44 0.190
## 45 0.190
## 46 1.000
## 47 1.000
## 48 1.000
## 49 1.000
## 50 1.000
## 51 1.000
## 52 1.000
## 53 1.000
## 54 1.035
## 55 0.865
## 56 0.919
## 57 0.994
## 58 0.597
## 59 0.688
## 60 0.641
## 61 0.653
## 62 0.869
## 63 0.872
## 64 0.593
## 65 0.796
## 66 0.645
## 67 0.774
## 68 0.746
## 69 0.558
## 70 0.622
## 71 0.585
## 72 0.973
## 73 0.623
## 74 0.697
## 75 0.766
## 76 0.539
## 77 0.703
## 78 0.741
## 79 0.750
## 80 0.599
## 81 0.573
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 1.000
## 90 1.000
## 91 1.000
## 92 1.000
## 93 1.000
## 94 1.000
## 95 1.000
## 96 1.000
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
## 111 0.000
## 112 0.000
## 113 0.000
## 114 0.000
## 115 0.000
## 116 0.000
## 117 0.000
## 118 0.000
## 119 0.000
8 Factor CFA with Higher Order Factors
sps.cfa.8.h <- '
tacphys =~ A1Q1+ A1Q2+ A1Q3
musclejointbone =~ A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3
soundjr =~ B2Q1+ B2Q2
tactilejr =~ B3Q1+ B3Q2
internal =~ .90*CQ1
energy =~ .90*DQ1
urge =~ .90*EQ1
fac1 =~ tacphys + musclejointbone
fac2 =~ lookjr + soundjr + tactilejr + internal
'
fit.sps.cfa.8.h <- cfa(sps.cfa.8.h, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1")) # Model does not converge
Alternative Models
6 Factor CFA
C, D, and E (the emotional/internally-focused items) are considered as loadings on a single factor instead of 3 separate factors
sps.cfa.6 <- '
tacphys =~ A1Q1+ A1Q2+ A1Q3
musclejointbone =~ A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3
soundjr =~ B2Q1+ B2Q2
tactilejr =~ B3Q1+ B3Q2
internaljr =~ .9*CQ1
emo =~ DQ1 + EQ1
'
fit.sps.cfa.6<- cfa(sps.cfa.6, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.6, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 38 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 50
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 59.382 93.892
## Degrees of freedom 70 70
## P-value (Chi-square) 0.813 0.030
## Scaling correction factor 0.757
## Shift parameter 15.410
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.992
## Tucker-Lewis Index (TLI) 1.003 0.989
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.026
## 90 Percent confidence interval - lower 0.000 0.009
## 90 Percent confidence interval - upper 0.017 0.039
## P-value RMSEA <= 0.05 1.000 0.999
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.063 0.063
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys =~
## A1Q1 0.595 0.069 8.587 0.000 0.595 0.595
## A1Q2 0.690 0.077 8.913 0.000 0.690 0.690
## A1Q3 0.672 0.073 9.214 0.000 0.672 0.672
## musclejointbone =~
## A2Q1 0.761 0.072 10.613 0.000 0.761 0.761
## A2Q2 0.864 0.071 12.145 0.000 0.864 0.864
## lookjr =~
## B1Q1 0.968 0.025 38.185 0.000 0.968 0.968
## B1Q2 0.952 0.023 41.985 0.000 0.952 0.952
## B1Q3 0.853 0.032 26.271 0.000 0.853 0.853
## soundjr =~
## B2Q1 0.851 0.051 16.584 0.000 0.851 0.851
## B2Q2 0.906 0.048 18.775 0.000 0.906 0.906
## tactilejr =~
## B3Q1 0.860 0.061 14.037 0.000 0.860 0.860
## B3Q2 0.769 0.063 12.177 0.000 0.769 0.769
## internaljr =~
## CQ1 0.900 0.900 0.900
## emo =~
## DQ1 0.581 0.090 6.487 0.000 0.581 0.581
## EQ1 0.556 0.085 6.545 0.000 0.556 0.556
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys ~~
## musclejointbon 0.863 0.104 8.293 0.000 0.863 0.863
## lookjr 0.747 0.072 10.405 0.000 0.747 0.747
## soundjr 0.753 0.101 7.469 0.000 0.753 0.753
## tactilejr 0.854 0.085 10.064 0.000 0.854 0.854
## internaljr 0.414 0.111 3.726 0.000 0.414 0.414
## emo 0.752 0.156 4.829 0.000 0.752 0.752
## musclejointbone ~~
## lookjr 0.511 0.086 5.944 0.000 0.511 0.511
## soundjr 0.714 0.094 7.621 0.000 0.714 0.714
## tactilejr 0.694 0.108 6.397 0.000 0.694 0.694
## internaljr 0.397 0.119 3.325 0.001 0.397 0.397
## emo 0.865 0.152 5.703 0.000 0.865 0.865
## lookjr ~~
## soundjr 0.663 0.067 9.894 0.000 0.663 0.663
## tactilejr 0.627 0.072 8.676 0.000 0.627 0.627
## internaljr 0.432 0.077 5.642 0.000 0.432 0.432
## emo 0.757 0.117 6.485 0.000 0.757 0.757
## soundjr ~~
## tactilejr 0.852 0.074 11.497 0.000 0.852 0.852
## internaljr 0.452 0.104 4.339 0.000 0.452 0.452
## emo 0.911 0.141 6.478 0.000 0.911 0.911
## tactilejr ~~
## internaljr 0.353 0.113 3.109 0.002 0.353 0.353
## emo 0.881 0.145 6.074 0.000 0.881 0.881
## internaljr ~~
## emo 0.815 0.134 6.084 0.000 0.815 0.815
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tacphys 0.000 0.000 0.000
## musclejointbon 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## soundjr 0.000 0.000 0.000
## tactilejr 0.000 0.000 0.000
## internaljr 0.000 0.000 0.000
## emo 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.646 0.646 0.646
## .A1Q2 0.524 0.524 0.524
## .A1Q3 0.548 0.548 0.548
## .A2Q1 0.420 0.420 0.420
## .A2Q2 0.253 0.253 0.253
## .B1Q1 0.063 0.063 0.063
## .B1Q2 0.094 0.094 0.094
## .B1Q3 0.272 0.272 0.272
## .B2Q1 0.276 0.276 0.276
## .B2Q2 0.180 0.180 0.180
## .B3Q1 0.261 0.261 0.261
## .B3Q2 0.409 0.409 0.409
## .CQ1 0.190 0.190 0.190
## .DQ1 0.663 0.663 0.663
## .EQ1 0.691 0.691 0.691
## tacphys 1.000 1.000 1.000
## musclejointbon 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## soundjr 1.000 1.000 1.000
## tactilejr 1.000 1.000 1.000
## internaljr 1.000 1.000 1.000
## emo 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.6, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tacphys =~ A1Q1 0.595 0.069 8.587 0.000 0.481
## 2 tacphys =~ A1Q2 0.690 0.077 8.913 0.000 0.562
## 3 tacphys =~ A1Q3 0.672 0.073 9.214 0.000 0.552
## 4 musclejointbone =~ A2Q1 0.761 0.072 10.613 0.000 0.643
## 5 musclejointbone =~ A2Q2 0.864 0.071 12.145 0.000 0.747
## 6 lookjr =~ B1Q1 0.968 0.025 38.185 0.000 0.926
## 7 lookjr =~ B1Q2 0.952 0.023 41.985 0.000 0.914
## 8 lookjr =~ B1Q3 0.853 0.032 26.271 0.000 0.800
## 9 soundjr =~ B2Q1 0.851 0.051 16.584 0.000 0.767
## 10 soundjr =~ B2Q2 0.906 0.048 18.775 0.000 0.826
## 11 tactilejr =~ B3Q1 0.860 0.061 14.037 0.000 0.759
## 12 tactilejr =~ B3Q2 0.769 0.063 12.177 0.000 0.665
## 13 internaljr =~ CQ1 0.900 0.000 NA NA 0.900
## 14 emo =~ DQ1 0.581 0.090 6.487 0.000 0.434
## 15 emo =~ EQ1 0.556 0.085 6.545 0.000 0.416
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130
## 19 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 20 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359
## 21 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203
## 22 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561
## 23 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787
## 24 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 25 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162
## 26 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218
## 27 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592
## 31 A1Q1 ~~ A1Q1 0.646 0.082 7.847 0.000 0.511
## 32 A1Q2 ~~ A1Q2 0.524 0.107 4.912 0.000 0.349
## 33 A1Q3 ~~ A1Q3 0.548 0.098 5.582 0.000 0.386
## 34 A2Q1 ~~ A2Q1 0.420 0.109 3.847 0.000 0.241
## 35 A2Q2 ~~ A2Q2 0.253 0.123 2.058 0.040 0.051
## 36 B1Q1 ~~ B1Q1 0.063 0.049 1.280 0.201 -0.018
## 37 B1Q2 ~~ B1Q2 0.094 0.043 2.181 0.029 0.023
## 38 B1Q3 ~~ B1Q3 0.272 0.055 4.905 0.000 0.181
## 39 B2Q1 ~~ B2Q1 0.276 0.087 3.160 0.002 0.132
## 40 B2Q2 ~~ B2Q2 0.180 0.087 2.054 0.040 0.036
## 41 B3Q1 ~~ B3Q1 0.261 0.105 2.479 0.013 0.088
## 42 B3Q2 ~~ B3Q2 0.409 0.097 4.208 0.000 0.249
## 43 CQ1 ~~ CQ1 0.190 0.000 NA NA 0.190
## 44 DQ1 ~~ DQ1 0.663 0.104 6.370 0.000 0.492
## 45 EQ1 ~~ EQ1 0.691 0.094 7.310 0.000 0.535
## 46 tacphys ~~ tacphys 1.000 0.000 NA NA 1.000
## 47 musclejointbone ~~ musclejointbone 1.000 0.000 NA NA 1.000
## 48 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000
## 49 soundjr ~~ soundjr 1.000 0.000 NA NA 1.000
## 50 tactilejr ~~ tactilejr 1.000 0.000 NA NA 1.000
## 51 internaljr ~~ internaljr 1.000 0.000 NA NA 1.000
## 52 emo ~~ emo 1.000 0.000 NA NA 1.000
## 53 tacphys ~~ musclejointbone 0.863 0.104 8.293 0.000 0.692
## 54 tacphys ~~ lookjr 0.747 0.072 10.405 0.000 0.629
## 55 tacphys ~~ soundjr 0.753 0.101 7.469 0.000 0.587
## 56 tacphys ~~ tactilejr 0.854 0.085 10.064 0.000 0.715
## 57 tacphys ~~ internaljr 0.414 0.111 3.726 0.000 0.231
## 58 tacphys ~~ emo 0.752 0.156 4.829 0.000 0.496
## 59 musclejointbone ~~ lookjr 0.511 0.086 5.944 0.000 0.370
## 60 musclejointbone ~~ soundjr 0.714 0.094 7.621 0.000 0.560
## 61 musclejointbone ~~ tactilejr 0.694 0.108 6.397 0.000 0.515
## 62 musclejointbone ~~ internaljr 0.397 0.119 3.325 0.001 0.200
## 63 musclejointbone ~~ emo 0.865 0.152 5.703 0.000 0.616
## 64 lookjr ~~ soundjr 0.663 0.067 9.894 0.000 0.553
## 65 lookjr ~~ tactilejr 0.627 0.072 8.676 0.000 0.508
## 66 lookjr ~~ internaljr 0.432 0.077 5.642 0.000 0.306
## 67 lookjr ~~ emo 0.757 0.117 6.485 0.000 0.565
## 68 soundjr ~~ tactilejr 0.852 0.074 11.497 0.000 0.730
## 69 soundjr ~~ internaljr 0.452 0.104 4.339 0.000 0.280
## 70 soundjr ~~ emo 0.911 0.141 6.478 0.000 0.680
## 71 tactilejr ~~ internaljr 0.353 0.113 3.109 0.002 0.166
## 72 tactilejr ~~ emo 0.881 0.145 6.074 0.000 0.643
## 73 internaljr ~~ emo 0.815 0.134 6.084 0.000 0.594
## 74 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000
## 75 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000
## 76 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000
## 77 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000
## 78 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000
## 79 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000
## 80 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000
## 81 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000
## 82 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000
## 83 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000
## 84 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000
## 85 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000
## 86 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000
## 87 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000
## 88 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000
## 89 A1Q1 ~1 0.000 0.000 NA NA 0.000
## 90 A1Q2 ~1 0.000 0.000 NA NA 0.000
## 91 A1Q3 ~1 0.000 0.000 NA NA 0.000
## 92 A2Q1 ~1 0.000 0.000 NA NA 0.000
## 93 A2Q2 ~1 0.000 0.000 NA NA 0.000
## 94 B1Q1 ~1 0.000 0.000 NA NA 0.000
## 95 B1Q2 ~1 0.000 0.000 NA NA 0.000
## 96 B1Q3 ~1 0.000 0.000 NA NA 0.000
## 97 B2Q1 ~1 0.000 0.000 NA NA 0.000
## 98 B2Q2 ~1 0.000 0.000 NA NA 0.000
## 99 B3Q1 ~1 0.000 0.000 NA NA 0.000
## 100 B3Q2 ~1 0.000 0.000 NA NA 0.000
## 101 CQ1 ~1 0.000 0.000 NA NA 0.000
## 102 DQ1 ~1 0.000 0.000 NA NA 0.000
## 103 EQ1 ~1 0.000 0.000 NA NA 0.000
## 104 tacphys ~1 0.000 0.000 NA NA 0.000
## 105 musclejointbone ~1 0.000 0.000 NA NA 0.000
## 106 lookjr ~1 0.000 0.000 NA NA 0.000
## 107 soundjr ~1 0.000 0.000 NA NA 0.000
## 108 tactilejr ~1 0.000 0.000 NA NA 0.000
## 109 internaljr ~1 0.000 0.000 NA NA 0.000
## 110 emo ~1 0.000 0.000 NA NA 0.000
## ci.upper
## 1 0.709
## 2 0.817
## 3 0.792
## 4 0.879
## 5 0.981
## 6 1.010
## 7 0.989
## 8 0.907
## 9 0.935
## 10 0.985
## 11 0.960
## 12 0.873
## 13 0.900
## 14 0.728
## 15 0.696
## 16 0.949
## 17 1.466
## 18 1.379
## 19 1.493
## 20 1.645
## 21 0.392
## 22 0.762
## 23 1.002
## 24 1.493
## 25 1.415
## 26 1.480
## 27 1.311
## 28 1.002
## 29 1.209
## 30 0.795
## 31 0.782
## 32 0.700
## 33 0.709
## 34 0.600
## 35 0.455
## 36 0.144
## 37 0.165
## 38 0.363
## 39 0.420
## 40 0.323
## 41 0.434
## 42 0.568
## 43 0.190
## 44 0.834
## 45 0.846
## 46 1.000
## 47 1.000
## 48 1.000
## 49 1.000
## 50 1.000
## 51 1.000
## 52 1.000
## 53 1.035
## 54 0.865
## 55 0.919
## 56 0.994
## 57 0.597
## 58 1.008
## 59 0.653
## 60 0.869
## 61 0.872
## 62 0.593
## 63 1.115
## 64 0.774
## 65 0.746
## 66 0.558
## 67 0.949
## 68 0.973
## 69 0.623
## 70 1.143
## 71 0.539
## 72 1.120
## 73 1.035
## 74 1.000
## 75 1.000
## 76 1.000
## 77 1.000
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 0.000
## 90 0.000
## 91 0.000
## 92 0.000
## 93 0.000
## 94 0.000
## 95 0.000
## 96 0.000
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
inspect(fit.sps.cfa.6, "cov.lv")
## tcphys mscljn lookjr sondjr tctljr intrnl emo
## tacphys 1.000
## musclejointbone 0.863 1.000
## lookjr 0.747 0.511 1.000
## soundjr 0.753 0.714 0.663 1.000
## tactilejr 0.854 0.694 0.627 0.852 1.000
## internaljr 0.414 0.397 0.432 0.452 0.353 1.000
## emo 0.752 0.865 0.757 0.911 0.881 0.815 1.000
6 Factor CFA with Higher Order Factors
sps.cfa.6.h <- '
tacphys =~ A1Q1+ A1Q2+ A1Q3
musclejointbone =~ A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3
soundjr =~ B2Q1+ B2Q2
tactilejr =~ B3Q1+ B3Q2
emo =~ CQ1 + DQ1 + EQ1
fac1 =~ tacphys + musclejointbone
fac2 =~ lookjr + soundjr + tactilejr + CQ1
'
fit.sps.cfa.6.h<- cfa(sps.cfa.6.h, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1")) # Model did not converge
5 Factor CFA
C, D, and E (the emotional/internally-focused items) are considered as loadings on a single factor instead of 3 separate factors. The tacphys and tactilejr items are considered as loading onto one “tactile” factor.
sps.cfa.5 <- '
tactile =~ A1Q1+ A1Q2+ A1Q3 + B3Q1+ B3Q2
musclejointbone =~ A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3
soundjr =~ B2Q1+ B2Q2
emo =~ CQ1 + DQ1 + EQ1
'
fit.sps.cfa.5<- cfa(sps.cfa.5, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.5, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 25 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 72.162 107.454
## Degrees of freedom 80 80
## P-value (Chi-square) 0.722 0.022
## Scaling correction factor 0.813
## Shift parameter 18.686
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.991
## Tucker-Lewis Index (TLI) 1.002 0.988
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.026
## 90 Percent confidence interval - lower 0.000 0.011
## 90 Percent confidence interval - upper 0.020 0.038
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.069 0.069
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tactile =~
## A1Q1 0.582 0.067 8.740 0.000 0.582 0.582
## A1Q2 0.673 0.070 9.634 0.000 0.673 0.673
## A1Q3 0.657 0.067 9.804 0.000 0.657 0.657
## B3Q1 0.805 0.055 14.655 0.000 0.805 0.805
## B3Q2 0.721 0.059 12.139 0.000 0.721 0.721
## musclejointbone =~
## A2Q1 0.761 0.072 10.599 0.000 0.761 0.761
## A2Q2 0.864 0.072 12.068 0.000 0.864 0.864
## lookjr =~
## B1Q1 0.968 0.025 38.203 0.000 0.968 0.968
## B1Q2 0.952 0.023 41.814 0.000 0.952 0.952
## B1Q3 0.854 0.032 26.313 0.000 0.854 0.854
## soundjr =~
## B2Q1 0.852 0.051 16.556 0.000 0.852 0.852
## B2Q2 0.905 0.048 18.808 0.000 0.905 0.905
## emo =~
## CQ1 0.559 0.070 8.042 0.000 0.559 0.559
## DQ1 0.667 0.073 9.134 0.000 0.667 0.667
## EQ1 0.640 0.076 8.464 0.000 0.640 0.640
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tactile ~~
## musclejointbon 0.817 0.091 8.969 0.000 0.817 0.817
## lookjr 0.719 0.054 13.385 0.000 0.719 0.719
## soundjr 0.851 0.068 12.509 0.000 0.851 0.851
## emo 0.721 0.088 8.185 0.000 0.721 0.721
## musclejointbone ~~
## lookjr 0.511 0.086 5.942 0.000 0.511 0.511
## soundjr 0.715 0.094 7.622 0.000 0.715 0.715
## emo 0.726 0.104 7.000 0.000 0.726 0.726
## lookjr ~~
## soundjr 0.663 0.067 9.898 0.000 0.663 0.663
## emo 0.668 0.077 8.629 0.000 0.668 0.668
## soundjr ~~
## emo 0.777 0.091 8.574 0.000 0.777 0.777
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tactile 0.000 0.000 0.000
## musclejointbon 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## soundjr 0.000 0.000 0.000
## emo 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.662 0.662 0.662
## .A1Q2 0.547 0.547 0.547
## .A1Q3 0.569 0.569 0.569
## .B3Q1 0.352 0.352 0.352
## .B3Q2 0.480 0.480 0.480
## .A2Q1 0.420 0.420 0.420
## .A2Q2 0.253 0.253 0.253
## .B1Q1 0.063 0.063 0.063
## .B1Q2 0.094 0.094 0.094
## .B1Q3 0.271 0.271 0.271
## .B2Q1 0.275 0.275 0.275
## .B2Q2 0.181 0.181 0.181
## .CQ1 0.687 0.687 0.687
## .DQ1 0.555 0.555 0.555
## .EQ1 0.591 0.591 0.591
## tactile 1.000 1.000 1.000
## musclejointbon 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## soundjr 1.000 1.000 1.000
## emo 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.5, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tactile =~ A1Q1 0.582 0.067 8.740 0.000 0.472
## 2 tactile =~ A1Q2 0.673 0.070 9.634 0.000 0.558
## 3 tactile =~ A1Q3 0.657 0.067 9.804 0.000 0.546
## 4 tactile =~ B3Q1 0.805 0.055 14.655 0.000 0.715
## 5 tactile =~ B3Q2 0.721 0.059 12.139 0.000 0.623
## 6 musclejointbone =~ A2Q1 0.761 0.072 10.599 0.000 0.643
## 7 musclejointbone =~ A2Q2 0.864 0.072 12.068 0.000 0.746
## 8 lookjr =~ B1Q1 0.968 0.025 38.203 0.000 0.926
## 9 lookjr =~ B1Q2 0.952 0.023 41.814 0.000 0.914
## 10 lookjr =~ B1Q3 0.854 0.032 26.313 0.000 0.800
## 11 soundjr =~ B2Q1 0.852 0.051 16.556 0.000 0.767
## 12 soundjr =~ B2Q2 0.905 0.048 18.808 0.000 0.826
## 13 emo =~ CQ1 0.559 0.070 8.042 0.000 0.445
## 14 emo =~ DQ1 0.667 0.073 9.134 0.000 0.547
## 15 emo =~ EQ1 0.640 0.076 8.464 0.000 0.515
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130
## 19 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218
## 20 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069
## 21 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 22 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359
## 23 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203
## 24 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561
## 25 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787
## 26 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230
## 27 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592
## 31 A1Q1 ~~ A1Q1 0.662 0.077 8.549 0.000 0.534
## 32 A1Q2 ~~ A1Q2 0.547 0.094 5.809 0.000 0.392
## 33 A1Q3 ~~ A1Q3 0.569 0.088 6.470 0.000 0.424
## 34 B3Q1 ~~ B3Q1 0.352 0.088 3.977 0.000 0.206
## 35 B3Q2 ~~ B3Q2 0.480 0.086 5.602 0.000 0.339
## 36 A2Q1 ~~ A2Q1 0.420 0.109 3.843 0.000 0.240
## 37 A2Q2 ~~ A2Q2 0.253 0.124 2.045 0.041 0.049
## 38 B1Q1 ~~ B1Q1 0.063 0.049 1.280 0.201 -0.018
## 39 B1Q2 ~~ B1Q2 0.094 0.043 2.179 0.029 0.023
## 40 B1Q3 ~~ B1Q3 0.271 0.055 4.901 0.000 0.180
## 41 B2Q1 ~~ B2Q1 0.275 0.088 3.139 0.002 0.131
## 42 B2Q2 ~~ B2Q2 0.181 0.087 2.073 0.038 0.037
## 43 CQ1 ~~ CQ1 0.687 0.078 8.832 0.000 0.559
## 44 DQ1 ~~ DQ1 0.555 0.097 5.695 0.000 0.395
## 45 EQ1 ~~ EQ1 0.591 0.097 6.114 0.000 0.432
## 46 tactile ~~ tactile 1.000 0.000 NA NA 1.000
## 47 musclejointbone ~~ musclejointbone 1.000 0.000 NA NA 1.000
## 48 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000
## 49 soundjr ~~ soundjr 1.000 0.000 NA NA 1.000
## 50 emo ~~ emo 1.000 0.000 NA NA 1.000
## 51 tactile ~~ musclejointbone 0.817 0.091 8.969 0.000 0.667
## 52 tactile ~~ lookjr 0.719 0.054 13.385 0.000 0.631
## 53 tactile ~~ soundjr 0.851 0.068 12.509 0.000 0.739
## 54 tactile ~~ emo 0.721 0.088 8.185 0.000 0.576
## 55 musclejointbone ~~ lookjr 0.511 0.086 5.942 0.000 0.370
## 56 musclejointbone ~~ soundjr 0.715 0.094 7.622 0.000 0.560
## 57 musclejointbone ~~ emo 0.726 0.104 7.000 0.000 0.555
## 58 lookjr ~~ soundjr 0.663 0.067 9.898 0.000 0.553
## 59 lookjr ~~ emo 0.668 0.077 8.629 0.000 0.541
## 60 soundjr ~~ emo 0.777 0.091 8.574 0.000 0.628
## 61 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000
## 62 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000
## 63 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000
## 64 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000
## 65 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000
## 66 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000
## 67 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000
## 68 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000
## 69 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000
## 70 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000
## 71 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000
## 72 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000
## 73 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000
## 74 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000
## 75 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000
## 76 A1Q1 ~1 0.000 0.000 NA NA 0.000
## 77 A1Q2 ~1 0.000 0.000 NA NA 0.000
## 78 A1Q3 ~1 0.000 0.000 NA NA 0.000
## 79 B3Q1 ~1 0.000 0.000 NA NA 0.000
## 80 B3Q2 ~1 0.000 0.000 NA NA 0.000
## 81 A2Q1 ~1 0.000 0.000 NA NA 0.000
## 82 A2Q2 ~1 0.000 0.000 NA NA 0.000
## 83 B1Q1 ~1 0.000 0.000 NA NA 0.000
## 84 B1Q2 ~1 0.000 0.000 NA NA 0.000
## 85 B1Q3 ~1 0.000 0.000 NA NA 0.000
## 86 B2Q1 ~1 0.000 0.000 NA NA 0.000
## 87 B2Q2 ~1 0.000 0.000 NA NA 0.000
## 88 CQ1 ~1 0.000 0.000 NA NA 0.000
## 89 DQ1 ~1 0.000 0.000 NA NA 0.000
## 90 EQ1 ~1 0.000 0.000 NA NA 0.000
## 91 tactile ~1 0.000 0.000 NA NA 0.000
## 92 musclejointbone ~1 0.000 0.000 NA NA 0.000
## 93 lookjr ~1 0.000 0.000 NA NA 0.000
## 94 soundjr ~1 0.000 0.000 NA NA 0.000
## 95 emo ~1 0.000 0.000 NA NA 0.000
## ci.upper
## 1 0.691
## 2 0.788
## 3 0.767
## 4 0.895
## 5 0.819
## 6 0.880
## 7 0.982
## 8 1.010
## 9 0.989
## 10 0.907
## 11 0.936
## 12 0.984
## 13 0.674
## 14 0.787
## 15 0.764
## 16 0.949
## 17 1.466
## 18 1.379
## 19 1.480
## 20 1.311
## 21 1.493
## 22 1.645
## 23 0.392
## 24 0.762
## 25 1.002
## 26 1.493
## 27 1.415
## 28 1.002
## 29 1.209
## 30 0.795
## 31 0.789
## 32 0.701
## 33 0.714
## 34 0.497
## 35 0.621
## 36 0.600
## 37 0.457
## 38 0.144
## 39 0.166
## 40 0.362
## 41 0.419
## 42 0.324
## 43 0.815
## 44 0.715
## 45 0.750
## 46 1.000
## 47 1.000
## 48 1.000
## 49 1.000
## 50 1.000
## 51 0.966
## 52 0.808
## 53 0.963
## 54 0.866
## 55 0.653
## 56 0.869
## 57 0.896
## 58 0.774
## 59 0.796
## 60 0.926
## 61 1.000
## 62 1.000
## 63 1.000
## 64 1.000
## 65 1.000
## 66 1.000
## 67 1.000
## 68 1.000
## 69 1.000
## 70 1.000
## 71 1.000
## 72 1.000
## 73 1.000
## 74 1.000
## 75 1.000
## 76 0.000
## 77 0.000
## 78 0.000
## 79 0.000
## 80 0.000
## 81 0.000
## 82 0.000
## 83 0.000
## 84 0.000
## 85 0.000
## 86 0.000
## 87 0.000
## 88 0.000
## 89 0.000
## 90 0.000
## 91 0.000
## 92 0.000
## 93 0.000
## 94 0.000
## 95 0.000
4 Factor CFA
Even though the USP-SPS lists the items according to 8 different groups of items, when we developed the USP-SPS we conceptualized 4 factor-models based on the literature. They were: a) Physical; b) Just-Right; c) Energy; d) Urge only
sps.cfa.4 <- '
phys =~ A1Q1+ A1Q2+ A1Q3 + A2Q1+ A2Q2
justright =~ B1Q1+ B1Q2+ B1Q3 + B2Q1+ B2Q2 + B3Q1+ B3Q2 + CQ1
energy =~ .90*DQ1
urge =~ .90*EQ1
'
fit.sps.cfa.4 <- cfa(sps.cfa.4, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.4, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 20 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 34
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 172.764 209.034
## Degrees of freedom 86 86
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.929
## Shift parameter 23.064
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.984 0.961
## Tucker-Lewis Index (TLI) 0.981 0.952
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045 0.054
## 90 Percent confidence interval - lower 0.035 0.045
## 90 Percent confidence interval - upper 0.055 0.063
## P-value RMSEA <= 0.05 0.786 0.242
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.101 0.101
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## phys =~
## A1Q1 0.607 0.068 8.974 0.000 0.607 0.607
## A1Q2 0.701 0.076 9.263 0.000 0.701 0.701
## A1Q3 0.685 0.068 10.100 0.000 0.685 0.685
## A2Q1 0.681 0.068 10.021 0.000 0.681 0.681
## A2Q2 0.773 0.069 11.225 0.000 0.773 0.773
## justright =~
## B1Q1 0.941 0.023 40.440 0.000 0.941 0.941
## B1Q2 0.935 0.023 41.166 0.000 0.935 0.935
## B1Q3 0.808 0.033 24.705 0.000 0.808 0.808
## B2Q1 0.763 0.050 15.141 0.000 0.763 0.763
## B2Q2 0.795 0.047 17.049 0.000 0.795 0.795
## B3Q1 0.741 0.056 13.154 0.000 0.741 0.741
## B3Q2 0.663 0.057 11.687 0.000 0.663 0.663
## CQ1 0.460 0.063 7.328 0.000 0.460 0.460
## energy =~
## DQ1 0.900 0.900 0.900
## urge =~
## EQ1 0.900 0.900 0.900
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## phys ~~
## justright 0.783 0.047 16.614 0.000 0.783 0.783
## energy 0.582 0.094 6.170 0.000 0.582 0.582
## urge 0.478 0.094 5.060 0.000 0.478 0.478
## justright ~~
## energy 0.572 0.072 7.981 0.000 0.572 0.572
## urge 0.575 0.063 9.173 0.000 0.575 0.575
## energy ~~
## urge 0.399 0.106 3.766 0.000 0.399 0.399
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## phys 0.000 0.000 0.000
## justright 0.000 0.000 0.000
## energy 0.000 0.000 0.000
## urge 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.632 0.632 0.632
## .A1Q2 0.509 0.509 0.509
## .A1Q3 0.531 0.531 0.531
## .A2Q1 0.536 0.536 0.536
## .A2Q2 0.402 0.402 0.402
## .B1Q1 0.115 0.115 0.115
## .B1Q2 0.126 0.126 0.126
## .B1Q3 0.347 0.347 0.347
## .B2Q1 0.417 0.417 0.417
## .B2Q2 0.368 0.368 0.368
## .B3Q1 0.451 0.451 0.451
## .B3Q2 0.561 0.561 0.561
## .CQ1 0.788 0.788 0.788
## .DQ1 0.190 0.190 0.190
## .EQ1 0.190 0.190 0.190
## phys 1.000 1.000 1.000
## justright 1.000 1.000 1.000
## energy 1.000 1.000 1.000
## urge 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.4, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 phys =~ A1Q1 0.607 0.068 8.974 0.000 0.495 0.718
## 2 phys =~ A1Q2 0.701 0.076 9.263 0.000 0.577 0.825
## 3 phys =~ A1Q3 0.685 0.068 10.100 0.000 0.573 0.797
## 4 phys =~ A2Q1 0.681 0.068 10.021 0.000 0.569 0.793
## 5 phys =~ A2Q2 0.773 0.069 11.225 0.000 0.660 0.886
## 6 justright =~ B1Q1 0.941 0.023 40.440 0.000 0.903 0.979
## 7 justright =~ B1Q2 0.935 0.023 41.166 0.000 0.898 0.972
## 8 justright =~ B1Q3 0.808 0.033 24.705 0.000 0.754 0.862
## 9 justright =~ B2Q1 0.763 0.050 15.141 0.000 0.680 0.846
## 10 justright =~ B2Q2 0.795 0.047 17.049 0.000 0.718 0.871
## 11 justright =~ B3Q1 0.741 0.056 13.154 0.000 0.649 0.834
## 12 justright =~ B3Q2 0.663 0.057 11.687 0.000 0.570 0.756
## 13 justright =~ CQ1 0.460 0.063 7.328 0.000 0.357 0.564
## 14 energy =~ DQ1 0.900 0.000 NA NA 0.900 0.900
## 15 urge =~ EQ1 0.900 0.000 NA NA 0.900 0.900
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737 0.949
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206 1.466
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130 1.379
## 19 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 20 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359 1.645
## 21 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203 0.392
## 22 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561 0.762
## 23 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 24 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 25 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162 1.415
## 26 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218 1.480
## 27 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069 1.311
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978 1.209
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592 0.795
## 31 A1Q1 ~~ A1Q1 0.632 0.082 7.707 0.000 0.497 0.767
## 32 A1Q2 ~~ A1Q2 0.509 0.106 4.794 0.000 0.334 0.683
## 33 A1Q3 ~~ A1Q3 0.531 0.093 5.713 0.000 0.378 0.684
## 34 A2Q1 ~~ A2Q1 0.536 0.093 5.792 0.000 0.384 0.688
## 35 A2Q2 ~~ A2Q2 0.402 0.107 3.776 0.000 0.227 0.577
## 36 B1Q1 ~~ B1Q1 0.115 0.044 2.619 0.009 0.043 0.187
## 37 B1Q2 ~~ B1Q2 0.126 0.042 2.963 0.003 0.056 0.196
## 38 B1Q3 ~~ B1Q3 0.347 0.053 6.563 0.000 0.260 0.434
## 39 B2Q1 ~~ B2Q1 0.417 0.077 5.425 0.000 0.291 0.544
## 40 B2Q2 ~~ B2Q2 0.368 0.074 4.972 0.000 0.247 0.490
## 41 B3Q1 ~~ B3Q1 0.451 0.084 5.394 0.000 0.313 0.588
## 42 B3Q2 ~~ B3Q2 0.561 0.075 7.456 0.000 0.437 0.684
## 43 CQ1 ~~ CQ1 0.788 0.058 13.636 0.000 0.693 0.883
## 44 DQ1 ~~ DQ1 0.190 0.000 NA NA 0.190 0.190
## 45 EQ1 ~~ EQ1 0.190 0.000 NA NA 0.190 0.190
## 46 phys ~~ phys 1.000 0.000 NA NA 1.000 1.000
## 47 justright ~~ justright 1.000 0.000 NA NA 1.000 1.000
## 48 energy ~~ energy 1.000 0.000 NA NA 1.000 1.000
## 49 urge ~~ urge 1.000 0.000 NA NA 1.000 1.000
## 50 phys ~~ justright 0.783 0.047 16.614 0.000 0.705 0.860
## 51 phys ~~ energy 0.582 0.094 6.170 0.000 0.427 0.737
## 52 phys ~~ urge 0.478 0.094 5.060 0.000 0.322 0.633
## 53 justright ~~ energy 0.572 0.072 7.981 0.000 0.454 0.690
## 54 justright ~~ urge 0.575 0.063 9.173 0.000 0.472 0.678
## 55 energy ~~ urge 0.399 0.106 3.766 0.000 0.225 0.573
## 56 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000 1.000
## 57 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000 1.000
## 58 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000 1.000
## 59 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000 1.000
## 60 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000 1.000
## 61 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000 1.000
## 62 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000 1.000
## 63 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000 1.000
## 64 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000 1.000
## 65 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000 1.000
## 66 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000 1.000
## 67 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000 1.000
## 68 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000 1.000
## 69 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000 1.000
## 70 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000 1.000
## 71 A1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 72 A1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 73 A1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 74 A2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 75 A2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 76 B1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 77 B1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 78 B1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 79 B2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 80 B2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 81 B3Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 82 B3Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 83 CQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 84 DQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 85 EQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 86 phys ~1 0.000 0.000 NA NA 0.000 0.000
## 87 justright ~1 0.000 0.000 NA NA 0.000 0.000
## 88 energy ~1 0.000 0.000 NA NA 0.000 0.000
## 89 urge ~1 0.000 0.000 NA NA 0.000 0.000
3 Factor CFA
sps.cfa.3 <- '
tacphys =~ A1Q1+ A1Q2+ A1Q3 + A2Q1+ A2Q2
lookjr =~ B1Q1+ B1Q2+ B1Q3 + B2Q1+ B2Q2 + B3Q1+ B3Q2+ CQ1
emo =~ DQ1 + EQ1
'
fit.sps.cfa.3<- cfa(sps.cfa.3, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.3, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 22 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 33
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 173.671 209.079
## Degrees of freedom 87 87
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.935
## Shift parameter 23.337
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.984 0.961
## Tucker-Lewis Index (TLI) 0.981 0.953
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045 0.053
## 90 Percent confidence interval - lower 0.035 0.044
## 90 Percent confidence interval - upper 0.055 0.063
## P-value RMSEA <= 0.05 0.801 0.271
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.101 0.101
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys =~
## A1Q1 0.606 0.068 8.961 0.000 0.606 0.606
## A1Q2 0.702 0.075 9.300 0.000 0.702 0.702
## A1Q3 0.685 0.068 10.091 0.000 0.685 0.685
## A2Q1 0.681 0.068 10.017 0.000 0.681 0.681
## A2Q2 0.773 0.069 11.184 0.000 0.773 0.773
## lookjr =~
## B1Q1 0.941 0.023 40.441 0.000 0.941 0.941
## B1Q2 0.935 0.023 41.129 0.000 0.935 0.935
## B1Q3 0.808 0.033 24.700 0.000 0.808 0.808
## B2Q1 0.763 0.050 15.121 0.000 0.763 0.763
## B2Q2 0.795 0.047 17.050 0.000 0.795 0.795
## B3Q1 0.741 0.056 13.147 0.000 0.741 0.741
## B3Q2 0.663 0.057 11.684 0.000 0.663 0.663
## CQ1 0.460 0.063 7.336 0.000 0.460 0.460
## emo =~
## DQ1 0.581 0.090 6.422 0.000 0.581 0.581
## EQ1 0.556 0.086 6.498 0.000 0.556 0.556
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys ~~
## lookjr 0.783 0.047 16.613 0.000 0.783 0.783
## emo 0.837 0.133 6.312 0.000 0.837 0.837
## lookjr ~~
## emo 0.910 0.113 8.023 0.000 0.910 0.910
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tacphys 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## emo 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.632 0.632 0.632
## .A1Q2 0.508 0.508 0.508
## .A1Q3 0.531 0.531 0.531
## .A2Q1 0.536 0.536 0.536
## .A2Q2 0.402 0.402 0.402
## .B1Q1 0.115 0.115 0.115
## .B1Q2 0.126 0.126 0.126
## .B1Q3 0.347 0.347 0.347
## .B2Q1 0.418 0.418 0.418
## .B2Q2 0.368 0.368 0.368
## .B3Q1 0.451 0.451 0.451
## .B3Q2 0.561 0.561 0.561
## .CQ1 0.788 0.788 0.788
## .DQ1 0.663 0.663 0.663
## .EQ1 0.690 0.690 0.690
## tacphys 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## emo 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.3, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 tacphys =~ A1Q1 0.606 0.068 8.961 0.000 0.495 0.718
## 2 tacphys =~ A1Q2 0.702 0.075 9.300 0.000 0.578 0.826
## 3 tacphys =~ A1Q3 0.685 0.068 10.091 0.000 0.573 0.796
## 4 tacphys =~ A2Q1 0.681 0.068 10.017 0.000 0.569 0.793
## 5 tacphys =~ A2Q2 0.773 0.069 11.184 0.000 0.659 0.887
## 6 lookjr =~ B1Q1 0.941 0.023 40.441 0.000 0.903 0.979
## 7 lookjr =~ B1Q2 0.935 0.023 41.129 0.000 0.898 0.972
## 8 lookjr =~ B1Q3 0.808 0.033 24.700 0.000 0.754 0.862
## 9 lookjr =~ B2Q1 0.763 0.050 15.121 0.000 0.680 0.846
## 10 lookjr =~ B2Q2 0.795 0.047 17.050 0.000 0.718 0.871
## 11 lookjr =~ B3Q1 0.741 0.056 13.147 0.000 0.648 0.834
## 12 lookjr =~ B3Q2 0.663 0.057 11.684 0.000 0.570 0.756
## 13 lookjr =~ CQ1 0.460 0.063 7.336 0.000 0.357 0.564
## 14 emo =~ DQ1 0.581 0.090 6.422 0.000 0.432 0.729
## 15 emo =~ EQ1 0.556 0.086 6.498 0.000 0.416 0.697
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737 0.949
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206 1.466
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130 1.379
## 19 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 20 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359 1.645
## 21 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203 0.392
## 22 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561 0.762
## 23 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 24 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 25 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162 1.415
## 26 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218 1.480
## 27 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069 1.311
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978 1.209
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592 0.795
## 31 A1Q1 ~~ A1Q1 0.632 0.082 7.700 0.000 0.497 0.767
## 32 A1Q2 ~~ A1Q2 0.508 0.106 4.796 0.000 0.334 0.682
## 33 A1Q3 ~~ A1Q3 0.531 0.093 5.723 0.000 0.379 0.684
## 34 A2Q1 ~~ A2Q1 0.536 0.093 5.786 0.000 0.384 0.688
## 35 A2Q2 ~~ A2Q2 0.402 0.107 3.767 0.000 0.227 0.578
## 36 B1Q1 ~~ B1Q1 0.115 0.044 2.618 0.009 0.043 0.187
## 37 B1Q2 ~~ B1Q2 0.126 0.043 2.960 0.003 0.056 0.196
## 38 B1Q3 ~~ B1Q3 0.347 0.053 6.561 0.000 0.260 0.434
## 39 B2Q1 ~~ B2Q1 0.418 0.077 5.423 0.000 0.291 0.544
## 40 B2Q2 ~~ B2Q2 0.368 0.074 4.973 0.000 0.247 0.490
## 41 B3Q1 ~~ B3Q1 0.451 0.084 5.393 0.000 0.313 0.588
## 42 B3Q2 ~~ B3Q2 0.561 0.075 7.456 0.000 0.437 0.684
## 43 CQ1 ~~ CQ1 0.788 0.058 13.633 0.000 0.693 0.883
## 44 DQ1 ~~ DQ1 0.663 0.105 6.317 0.000 0.490 0.836
## 45 EQ1 ~~ EQ1 0.690 0.095 7.245 0.000 0.534 0.847
## 46 tacphys ~~ tacphys 1.000 0.000 NA NA 1.000 1.000
## 47 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000 1.000
## 48 emo ~~ emo 1.000 0.000 NA NA 1.000 1.000
## 49 tacphys ~~ lookjr 0.783 0.047 16.613 0.000 0.705 0.860
## 50 tacphys ~~ emo 0.837 0.133 6.312 0.000 0.619 1.055
## 51 lookjr ~~ emo 0.910 0.113 8.023 0.000 0.724 1.097
## 52 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000 1.000
## 53 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000 1.000
## 54 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000 1.000
## 55 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000 1.000
## 56 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000 1.000
## 57 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000 1.000
## 58 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000 1.000
## 59 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000 1.000
## 60 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000 1.000
## 61 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000 1.000
## 62 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000 1.000
## 63 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000 1.000
## 64 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000 1.000
## 65 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000 1.000
## 66 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000 1.000
## 67 A1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 68 A1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 69 A1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 70 A2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 71 A2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 72 B1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 73 B1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 74 B1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 75 B2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 76 B2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 77 B3Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 78 B3Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 79 CQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 80 DQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 81 EQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 82 tacphys ~1 0.000 0.000 NA NA 0.000 0.000
## 83 lookjr ~1 0.000 0.000 NA NA 0.000 0.000
## 84 emo ~1 0.000 0.000 NA NA 0.000 0.000
1-Factor CFA
sps.cfa.1 <- '
uspsps =~ A1Q1+ A1Q2+ A1Q3 + A2Q1+ A2Q2 + B1Q1+ B1Q2+ B1Q3 + B2Q1+ B2Q2 + B3Q1+ B3Q2 + CQ1 + DQ1 + EQ1
'
fit.sps.cfa.1<- cfa(sps.cfa.1, data=train.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.1, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 18 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 496 500
##
## Model Test User Model:
## Standard Robust
## Test Statistic 198.368 232.485
## Degrees of freedom 90 90
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.955
## Shift parameter 24.855
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5539.417 3227.221
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.741
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.980 0.954
## Tucker-Lewis Index (TLI) 0.977 0.947
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.040 0.048
## 90 Percent confidence interval - upper 0.059 0.066
## P-value RMSEA <= 0.05 0.534 0.110
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.109 0.109
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## uspsps =~
## A1Q1 0.528 0.058 9.073 0.000 0.528 0.528
## A1Q2 0.611 0.068 8.994 0.000 0.611 0.611
## A1Q3 0.593 0.061 9.789 0.000 0.593 0.593
## A2Q1 0.593 0.066 8.968 0.000 0.593 0.593
## A2Q2 0.672 0.064 10.572 0.000 0.672 0.672
## B1Q1 0.936 0.023 40.230 0.000 0.936 0.936
## B1Q2 0.932 0.023 40.872 0.000 0.932 0.932
## B1Q3 0.801 0.033 24.214 0.000 0.801 0.801
## B2Q1 0.753 0.050 14.961 0.000 0.753 0.753
## B2Q2 0.785 0.047 16.743 0.000 0.785 0.785
## B3Q1 0.732 0.055 13.206 0.000 0.732 0.732
## B3Q2 0.654 0.056 11.603 0.000 0.654 0.654
## CQ1 0.454 0.062 7.333 0.000 0.454 0.454
## DQ1 0.542 0.063 8.594 0.000 0.542 0.542
## EQ1 0.519 0.057 9.122 0.000 0.519 0.519
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## uspsps 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.843 0.064 13.122 0.000 0.843 0.843
## A1Q2|t1 1.336 0.079 16.910 0.000 1.336 1.336
## A1Q3|t1 1.254 0.076 16.548 0.000 1.254 1.254
## A2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## A2Q2|t1 1.502 0.087 17.314 0.000 1.502 1.502
## B1Q1|t1 0.297 0.057 5.196 0.000 0.297 0.297
## B1Q2|t1 0.662 0.061 10.840 0.000 0.662 0.662
## B1Q3|t1 0.895 0.065 13.692 0.000 0.895 0.895
## B2Q1|t1 1.361 0.080 16.999 0.000 1.361 1.361
## B2Q2|t1 1.288 0.077 16.712 0.000 1.288 1.288
## B3Q1|t1 1.349 0.080 16.956 0.000 1.349 1.349
## B3Q2|t1 1.190 0.074 16.189 0.000 1.190 1.190
## CQ1|t1 0.895 0.065 13.692 0.000 0.895 0.895
## DQ1|t1 1.093 0.070 15.520 0.000 1.093 1.093
## EQ1|t1 0.694 0.062 11.271 0.000 0.694 0.694
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.721 0.721 0.721
## .A1Q2 0.627 0.627 0.627
## .A1Q3 0.648 0.648 0.648
## .A2Q1 0.648 0.648 0.648
## .A2Q2 0.548 0.548 0.548
## .B1Q1 0.123 0.123 0.123
## .B1Q2 0.132 0.132 0.132
## .B1Q3 0.358 0.358 0.358
## .B2Q1 0.433 0.433 0.433
## .B2Q2 0.384 0.384 0.384
## .B3Q1 0.464 0.464 0.464
## .B3Q2 0.572 0.572 0.572
## .CQ1 0.794 0.794 0.794
## .DQ1 0.706 0.706 0.706
## .EQ1 0.730 0.730 0.730
## uspsps 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.1, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 uspsps =~ A1Q1 0.528 0.058 9.073 0.000 0.432 0.624
## 2 uspsps =~ A1Q2 0.611 0.068 8.994 0.000 0.499 0.722
## 3 uspsps =~ A1Q3 0.593 0.061 9.789 0.000 0.494 0.693
## 4 uspsps =~ A2Q1 0.593 0.066 8.968 0.000 0.484 0.702
## 5 uspsps =~ A2Q2 0.672 0.064 10.572 0.000 0.568 0.777
## 6 uspsps =~ B1Q1 0.936 0.023 40.230 0.000 0.898 0.975
## 7 uspsps =~ B1Q2 0.932 0.023 40.872 0.000 0.894 0.969
## 8 uspsps =~ B1Q3 0.801 0.033 24.214 0.000 0.747 0.856
## 9 uspsps =~ B2Q1 0.753 0.050 14.961 0.000 0.670 0.836
## 10 uspsps =~ B2Q2 0.785 0.047 16.743 0.000 0.708 0.862
## 11 uspsps =~ B3Q1 0.732 0.055 13.206 0.000 0.641 0.823
## 12 uspsps =~ B3Q2 0.654 0.056 11.603 0.000 0.561 0.747
## 13 uspsps =~ CQ1 0.454 0.062 7.333 0.000 0.352 0.556
## 14 uspsps =~ DQ1 0.542 0.063 8.594 0.000 0.438 0.645
## 15 uspsps =~ EQ1 0.519 0.057 9.122 0.000 0.426 0.613
## 16 A1Q1 | t1 0.843 0.064 13.122 0.000 0.737 0.949
## 17 A1Q2 | t1 1.336 0.079 16.910 0.000 1.206 1.466
## 18 A1Q3 | t1 1.254 0.076 16.548 0.000 1.130 1.379
## 19 A2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 20 A2Q2 | t1 1.502 0.087 17.314 0.000 1.359 1.645
## 21 B1Q1 | t1 0.297 0.057 5.196 0.000 0.203 0.392
## 22 B1Q2 | t1 0.662 0.061 10.840 0.000 0.561 0.762
## 23 B1Q3 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 24 B2Q1 | t1 1.361 0.080 16.999 0.000 1.230 1.493
## 25 B2Q2 | t1 1.288 0.077 16.712 0.000 1.162 1.415
## 26 B3Q1 | t1 1.349 0.080 16.956 0.000 1.218 1.480
## 27 B3Q2 | t1 1.190 0.074 16.189 0.000 1.069 1.311
## 28 CQ1 | t1 0.895 0.065 13.692 0.000 0.787 1.002
## 29 DQ1 | t1 1.093 0.070 15.520 0.000 0.978 1.209
## 30 EQ1 | t1 0.694 0.062 11.271 0.000 0.592 0.795
## 31 A1Q1 ~~ A1Q1 0.721 0.061 11.738 0.000 0.620 0.822
## 32 A1Q2 ~~ A1Q2 0.627 0.083 7.558 0.000 0.491 0.763
## 33 A1Q3 ~~ A1Q3 0.648 0.072 9.016 0.000 0.530 0.766
## 34 A2Q1 ~~ A2Q1 0.648 0.078 8.264 0.000 0.519 0.777
## 35 A2Q2 ~~ A2Q2 0.548 0.086 6.404 0.000 0.407 0.689
## 36 B1Q1 ~~ B1Q1 0.123 0.044 2.828 0.005 0.052 0.195
## 37 B1Q2 ~~ B1Q2 0.132 0.042 3.116 0.002 0.062 0.202
## 38 B1Q3 ~~ B1Q3 0.358 0.053 6.757 0.000 0.271 0.445
## 39 B2Q1 ~~ B2Q1 0.433 0.076 5.705 0.000 0.308 0.557
## 40 B2Q2 ~~ B2Q2 0.384 0.074 5.225 0.000 0.263 0.505
## 41 B3Q1 ~~ B3Q1 0.464 0.081 5.727 0.000 0.331 0.598
## 42 B3Q2 ~~ B3Q2 0.572 0.074 7.768 0.000 0.451 0.694
## 43 CQ1 ~~ CQ1 0.794 0.056 14.092 0.000 0.701 0.886
## 44 DQ1 ~~ DQ1 0.706 0.068 10.344 0.000 0.594 0.819
## 45 EQ1 ~~ EQ1 0.730 0.059 12.363 0.000 0.633 0.828
## 46 uspsps ~~ uspsps 1.000 0.000 NA NA 1.000 1.000
## 47 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000 1.000
## 48 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000 1.000
## 49 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000 1.000
## 50 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000 1.000
## 51 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000 1.000
## 52 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000 1.000
## 53 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000 1.000
## 54 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000 1.000
## 55 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000 1.000
## 56 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000 1.000
## 57 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000 1.000
## 58 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000 1.000
## 59 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000 1.000
## 60 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000 1.000
## 61 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000 1.000
## 62 A1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 63 A1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 64 A1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 65 A2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 66 A2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 67 B1Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 68 B1Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 69 B1Q3 ~1 0.000 0.000 NA NA 0.000 0.000
## 70 B2Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 71 B2Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 72 B3Q1 ~1 0.000 0.000 NA NA 0.000 0.000
## 73 B3Q2 ~1 0.000 0.000 NA NA 0.000 0.000
## 74 CQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 75 DQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 76 EQ1 ~1 0.000 0.000 NA NA 0.000 0.000
## 77 uspsps ~1 0.000 0.000 NA NA 0.000 0.000
Compare Fits for Models that Converged
for (mod in modnames) {
fits[count,]$chisq <- fitMeasures(mod, c("chisq"))
fits[count,]$df <- fitMeasures(mod, c("df"))
fits[count,]$pvalue <- fitMeasures(mod, c("pvalue"))
fits[count,]$cfi <- fitMeasures(mod, c("cfi"))
fits[count,]$rmsea <- fitMeasures(mod, c("rmsea"))
fits[count,]$srmr <- fitMeasures(mod, c("srmr"))
count <- count + 1
}
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
fits %>%
kable(digits=3, bootstrap_options = "striped", font_size = 10) %>%
kable_styling()
|
Model
|
chisq
|
df
|
pvalue
|
cfi
|
rmsea
|
srmr
|
|
8 Factor Model
|
56.089
|
65
|
0.777
|
1.000
|
0.000
|
0.061
|
|
6 Factor Model
|
59.382
|
70
|
0.813
|
1.000
|
0.000
|
0.063
|
|
5 Factor Model
|
72.162
|
80
|
0.722
|
1.000
|
0.000
|
0.069
|
|
4 Factor Model
|
172.764
|
86
|
0.000
|
0.984
|
0.045
|
0.101
|
|
3 Factor Model
|
173.671
|
87
|
0.000
|
0.984
|
0.045
|
0.101
|
|
1 Factor Model
|
198.368
|
90
|
0.000
|
0.980
|
0.049
|
0.109
|
LRT Comparing 8-Factor and 6-Factor Models
anova(fit.sps.cfa.8, fit.sps.cfa.6)
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.sps.cfa.8 65 56.089
## fit.sps.cfa.6 70 59.382 3.8214 5 0.5754
LRT Comparing 6-Factor and 5-Factor Models
anova(fit.sps.cfa.6, fit.sps.cfa.5)
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.sps.cfa.6 70 59.382
## fit.sps.cfa.5 80 72.162 13.472 10 0.1985
Cross-Validation: Assess the two best-fitting models in Test Set of Sample
6 Factor CFA - Cross-Validation
C, D, and E (the emotional/internally-focused items) are considered as loadings on a single factor instead of 3 separate factors
fit.sps.cfa.6.test<- cfa(sps.cfa.6, data=test.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.6.test, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 35 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 50
##
## Used Total
## Number of observations 497 501
##
## Model Test User Model:
## Standard Robust
## Test Statistic 71.728 97.681
## Degrees of freedom 70 70
## P-value (Chi-square) 0.420 0.016
## Scaling correction factor 0.871
## Shift parameter 15.375
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 3548.794 2322.326
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.553
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.988
## Tucker-Lewis Index (TLI) 0.999 0.981
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.007 0.028
## 90 Percent confidence interval - lower 0.000 0.013
## 90 Percent confidence interval - upper 0.027 0.041
## P-value RMSEA <= 0.05 1.000 0.999
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys =~
## A1Q1 0.633 0.061 10.394 0.000 0.633 0.633
## A1Q2 0.793 0.071 11.244 0.000 0.793 0.793
## A1Q3 0.632 0.068 9.252 0.000 0.632 0.632
## musclejointbone =~
## A2Q1 0.797 0.088 9.025 0.000 0.797 0.797
## A2Q2 0.747 0.113 6.594 0.000 0.747 0.747
## lookjr =~
## B1Q1 0.995 0.032 30.811 0.000 0.995 0.995
## B1Q2 0.847 0.033 25.934 0.000 0.847 0.847
## B1Q3 0.820 0.035 23.240 0.000 0.820 0.820
## soundjr =~
## B2Q1 0.746 0.077 9.678 0.000 0.746 0.746
## B2Q2 0.736 0.077 9.553 0.000 0.736 0.736
## tactilejr =~
## B3Q1 0.891 0.054 16.471 0.000 0.891 0.891
## B3Q2 0.771 0.057 13.600 0.000 0.771 0.771
## internaljr =~
## CQ1 0.900 0.900 0.900
## emo =~
## DQ1 0.712 0.067 10.552 0.000 0.712 0.712
## EQ1 0.742 0.072 10.274 0.000 0.742 0.742
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys ~~
## musclejointbon 0.665 0.112 5.954 0.000 0.665 0.665
## lookjr 0.737 0.061 12.000 0.000 0.737 0.737
## soundjr 0.633 0.116 5.444 0.000 0.633 0.633
## tactilejr 0.845 0.085 9.941 0.000 0.845 0.845
## internaljr 0.514 0.103 4.984 0.000 0.514 0.514
## emo 0.572 0.109 5.265 0.000 0.572 0.572
## musclejointbone ~~
## lookjr 0.364 0.091 3.987 0.000 0.364 0.364
## soundjr 0.511 0.150 3.416 0.001 0.511 0.511
## tactilejr 0.644 0.108 5.974 0.000 0.644 0.644
## internaljr 0.267 0.139 1.920 0.055 0.267 0.267
## emo 0.447 0.112 3.980 0.000 0.447 0.447
## lookjr ~~
## soundjr 0.643 0.081 7.938 0.000 0.643 0.643
## tactilejr 0.524 0.068 7.735 0.000 0.524 0.524
## internaljr 0.428 0.073 5.850 0.000 0.428 0.428
## emo 0.539 0.076 7.088 0.000 0.539 0.539
## soundjr ~~
## tactilejr 0.761 0.097 7.871 0.000 0.761 0.761
## internaljr 0.634 0.113 5.608 0.000 0.634 0.634
## emo 0.759 0.110 6.931 0.000 0.759 0.759
## tactilejr ~~
## internaljr 0.456 0.104 4.387 0.000 0.456 0.456
## emo 0.718 0.095 7.538 0.000 0.718 0.718
## internaljr ~~
## emo 0.725 0.095 7.670 0.000 0.725 0.725
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tacphys 0.000 0.000 0.000
## musclejointbon 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## soundjr 0.000 0.000 0.000
## tactilejr 0.000 0.000 0.000
## internaljr 0.000 0.000 0.000
## emo 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.728 0.062 11.729 0.000 0.728 0.728
## A1Q2|t1 1.278 0.077 16.681 0.000 1.278 1.278
## A1Q3|t1 1.245 0.075 16.514 0.000 1.245 1.245
## A2Q1|t1 1.350 0.080 16.977 0.000 1.350 1.350
## A2Q2|t1 1.703 0.099 17.250 0.000 1.703 1.703
## B1Q1|t1 0.139 0.056 2.464 0.014 0.139 0.139
## B1Q2|t1 0.457 0.058 7.812 0.000 0.457 0.457
## B1Q3|t1 0.728 0.062 11.729 0.000 0.728 0.728
## B2Q1|t1 1.337 0.079 16.931 0.000 1.337 1.337
## B2Q2|t1 1.256 0.076 16.570 0.000 1.256 1.256
## B3Q1|t1 1.212 0.074 16.336 0.000 1.212 1.212
## B3Q2|t1 1.123 0.071 15.753 0.000 1.123 1.123
## CQ1|t1 0.999 0.068 14.739 0.000 0.999 0.999
## DQ1|t1 1.077 0.070 15.403 0.000 1.077 1.077
## EQ1|t1 0.708 0.062 11.474 0.000 0.708 0.708
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.600 0.600 0.600
## .A1Q2 0.371 0.371 0.371
## .A1Q3 0.601 0.601 0.601
## .A2Q1 0.365 0.365 0.365
## .A2Q2 0.441 0.441 0.441
## .B1Q1 0.009 0.009 0.009
## .B1Q2 0.282 0.282 0.282
## .B1Q3 0.327 0.327 0.327
## .B2Q1 0.443 0.443 0.443
## .B2Q2 0.458 0.458 0.458
## .B3Q1 0.206 0.206 0.206
## .B3Q2 0.406 0.406 0.406
## .CQ1 0.190 0.190 0.190
## .DQ1 0.493 0.493 0.493
## .EQ1 0.450 0.450 0.450
## tacphys 1.000 1.000 1.000
## musclejointbon 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## soundjr 1.000 1.000 1.000
## tactilejr 1.000 1.000 1.000
## internaljr 1.000 1.000 1.000
## emo 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.6.test, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tacphys =~ A1Q1 0.633 0.061 10.394 0.000 0.532
## 2 tacphys =~ A1Q2 0.793 0.071 11.244 0.000 0.677
## 3 tacphys =~ A1Q3 0.632 0.068 9.252 0.000 0.519
## 4 musclejointbone =~ A2Q1 0.797 0.088 9.025 0.000 0.652
## 5 musclejointbone =~ A2Q2 0.747 0.113 6.594 0.000 0.561
## 6 lookjr =~ B1Q1 0.995 0.032 30.811 0.000 0.942
## 7 lookjr =~ B1Q2 0.847 0.033 25.934 0.000 0.794
## 8 lookjr =~ B1Q3 0.820 0.035 23.240 0.000 0.762
## 9 soundjr =~ B2Q1 0.746 0.077 9.678 0.000 0.619
## 10 soundjr =~ B2Q2 0.736 0.077 9.553 0.000 0.610
## 11 tactilejr =~ B3Q1 0.891 0.054 16.471 0.000 0.802
## 12 tactilejr =~ B3Q2 0.771 0.057 13.600 0.000 0.678
## 13 internaljr =~ CQ1 0.900 0.000 NA NA 0.900
## 14 emo =~ DQ1 0.712 0.067 10.552 0.000 0.601
## 15 emo =~ EQ1 0.742 0.072 10.274 0.000 0.623
## 16 A1Q1 | t1 0.728 0.062 11.729 0.000 0.626
## 17 A1Q2 | t1 1.278 0.077 16.681 0.000 1.152
## 18 A1Q3 | t1 1.245 0.075 16.514 0.000 1.121
## 19 A2Q1 | t1 1.350 0.080 16.977 0.000 1.219
## 20 A2Q2 | t1 1.703 0.099 17.250 0.000 1.541
## 21 B1Q1 | t1 0.139 0.056 2.464 0.014 0.046
## 22 B1Q2 | t1 0.457 0.058 7.812 0.000 0.361
## 23 B1Q3 | t1 0.728 0.062 11.729 0.000 0.626
## 24 B2Q1 | t1 1.337 0.079 16.931 0.000 1.207
## 25 B2Q2 | t1 1.256 0.076 16.570 0.000 1.131
## 26 B3Q1 | t1 1.212 0.074 16.336 0.000 1.090
## 27 B3Q2 | t1 1.123 0.071 15.753 0.000 1.005
## 28 CQ1 | t1 0.999 0.068 14.739 0.000 0.887
## 29 DQ1 | t1 1.077 0.070 15.403 0.000 0.962
## 30 EQ1 | t1 0.708 0.062 11.474 0.000 0.607
## 31 A1Q1 ~~ A1Q1 0.600 0.077 7.793 0.000 0.473
## 32 A1Q2 ~~ A1Q2 0.371 0.112 3.313 0.001 0.187
## 33 A1Q3 ~~ A1Q3 0.601 0.086 6.973 0.000 0.459
## 34 A2Q1 ~~ A2Q1 0.365 0.141 2.595 0.009 0.134
## 35 A2Q2 ~~ A2Q2 0.441 0.169 2.604 0.009 0.163
## 36 B1Q1 ~~ B1Q1 0.009 0.064 0.140 0.889 -0.097
## 37 B1Q2 ~~ B1Q2 0.282 0.055 5.090 0.000 0.191
## 38 B1Q3 ~~ B1Q3 0.327 0.058 5.652 0.000 0.232
## 39 B2Q1 ~~ B2Q1 0.443 0.115 3.853 0.000 0.254
## 40 B2Q2 ~~ B2Q2 0.458 0.114 4.034 0.000 0.271
## 41 B3Q1 ~~ B3Q1 0.206 0.096 2.141 0.032 0.048
## 42 B3Q2 ~~ B3Q2 0.406 0.087 4.639 0.000 0.262
## 43 CQ1 ~~ CQ1 0.190 0.000 NA NA 0.190
## 44 DQ1 ~~ DQ1 0.493 0.096 5.140 0.000 0.336
## 45 EQ1 ~~ EQ1 0.450 0.107 4.196 0.000 0.273
## 46 tacphys ~~ tacphys 1.000 0.000 NA NA 1.000
## 47 musclejointbone ~~ musclejointbone 1.000 0.000 NA NA 1.000
## 48 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000
## 49 soundjr ~~ soundjr 1.000 0.000 NA NA 1.000
## 50 tactilejr ~~ tactilejr 1.000 0.000 NA NA 1.000
## 51 internaljr ~~ internaljr 1.000 0.000 NA NA 1.000
## 52 emo ~~ emo 1.000 0.000 NA NA 1.000
## 53 tacphys ~~ musclejointbone 0.665 0.112 5.954 0.000 0.481
## 54 tacphys ~~ lookjr 0.737 0.061 12.000 0.000 0.636
## 55 tacphys ~~ soundjr 0.633 0.116 5.444 0.000 0.442
## 56 tacphys ~~ tactilejr 0.845 0.085 9.941 0.000 0.705
## 57 tacphys ~~ internaljr 0.514 0.103 4.984 0.000 0.345
## 58 tacphys ~~ emo 0.572 0.109 5.265 0.000 0.393
## 59 musclejointbone ~~ lookjr 0.364 0.091 3.987 0.000 0.214
## 60 musclejointbone ~~ soundjr 0.511 0.150 3.416 0.001 0.265
## 61 musclejointbone ~~ tactilejr 0.644 0.108 5.974 0.000 0.467
## 62 musclejointbone ~~ internaljr 0.267 0.139 1.920 0.055 0.038
## 63 musclejointbone ~~ emo 0.447 0.112 3.980 0.000 0.262
## 64 lookjr ~~ soundjr 0.643 0.081 7.938 0.000 0.510
## 65 lookjr ~~ tactilejr 0.524 0.068 7.735 0.000 0.413
## 66 lookjr ~~ internaljr 0.428 0.073 5.850 0.000 0.307
## 67 lookjr ~~ emo 0.539 0.076 7.088 0.000 0.414
## 68 soundjr ~~ tactilejr 0.761 0.097 7.871 0.000 0.602
## 69 soundjr ~~ internaljr 0.634 0.113 5.608 0.000 0.448
## 70 soundjr ~~ emo 0.759 0.110 6.931 0.000 0.579
## 71 tactilejr ~~ internaljr 0.456 0.104 4.387 0.000 0.285
## 72 tactilejr ~~ emo 0.718 0.095 7.538 0.000 0.562
## 73 internaljr ~~ emo 0.725 0.095 7.670 0.000 0.570
## 74 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000
## 75 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000
## 76 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000
## 77 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000
## 78 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000
## 79 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000
## 80 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000
## 81 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000
## 82 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000
## 83 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000
## 84 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000
## 85 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000
## 86 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000
## 87 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000
## 88 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000
## 89 A1Q1 ~1 0.000 0.000 NA NA 0.000
## 90 A1Q2 ~1 0.000 0.000 NA NA 0.000
## 91 A1Q3 ~1 0.000 0.000 NA NA 0.000
## 92 A2Q1 ~1 0.000 0.000 NA NA 0.000
## 93 A2Q2 ~1 0.000 0.000 NA NA 0.000
## 94 B1Q1 ~1 0.000 0.000 NA NA 0.000
## 95 B1Q2 ~1 0.000 0.000 NA NA 0.000
## 96 B1Q3 ~1 0.000 0.000 NA NA 0.000
## 97 B2Q1 ~1 0.000 0.000 NA NA 0.000
## 98 B2Q2 ~1 0.000 0.000 NA NA 0.000
## 99 B3Q1 ~1 0.000 0.000 NA NA 0.000
## 100 B3Q2 ~1 0.000 0.000 NA NA 0.000
## 101 CQ1 ~1 0.000 0.000 NA NA 0.000
## 102 DQ1 ~1 0.000 0.000 NA NA 0.000
## 103 EQ1 ~1 0.000 0.000 NA NA 0.000
## 104 tacphys ~1 0.000 0.000 NA NA 0.000
## 105 musclejointbone ~1 0.000 0.000 NA NA 0.000
## 106 lookjr ~1 0.000 0.000 NA NA 0.000
## 107 soundjr ~1 0.000 0.000 NA NA 0.000
## 108 tactilejr ~1 0.000 0.000 NA NA 0.000
## 109 internaljr ~1 0.000 0.000 NA NA 0.000
## 110 emo ~1 0.000 0.000 NA NA 0.000
## ci.upper
## 1 0.733
## 2 0.909
## 3 0.744
## 4 0.942
## 5 0.934
## 6 1.049
## 7 0.901
## 8 0.878
## 9 0.873
## 10 0.863
## 11 0.980
## 12 0.864
## 13 0.900
## 14 0.823
## 15 0.861
## 16 0.830
## 17 1.404
## 18 1.369
## 19 1.481
## 20 1.866
## 21 0.232
## 22 0.553
## 23 0.830
## 24 1.467
## 25 1.380
## 26 1.334
## 27 1.240
## 28 1.110
## 29 1.191
## 30 0.810
## 31 0.727
## 32 0.555
## 33 0.743
## 34 0.597
## 35 0.720
## 36 0.115
## 37 0.373
## 38 0.422
## 39 0.632
## 40 0.645
## 41 0.365
## 42 0.549
## 43 0.190
## 44 0.651
## 45 0.626
## 46 1.000
## 47 1.000
## 48 1.000
## 49 1.000
## 50 1.000
## 51 1.000
## 52 1.000
## 53 0.849
## 54 0.838
## 55 0.824
## 56 0.985
## 57 0.684
## 58 0.751
## 59 0.514
## 60 0.757
## 61 0.821
## 62 0.496
## 63 0.632
## 64 0.777
## 65 0.635
## 66 0.548
## 67 0.665
## 68 0.920
## 69 0.820
## 70 0.940
## 71 0.627
## 72 0.875
## 73 0.881
## 74 1.000
## 75 1.000
## 76 1.000
## 77 1.000
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 0.000
## 90 0.000
## 91 0.000
## 92 0.000
## 93 0.000
## 94 0.000
## 95 0.000
## 96 0.000
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
inspect(fit.sps.cfa.6.test, "cov.lv")
## tcphys mscljn lookjr sondjr tctljr intrnl emo
## tacphys 1.000
## musclejointbone 0.665 1.000
## lookjr 0.737 0.364 1.000
## soundjr 0.633 0.511 0.643 1.000
## tactilejr 0.845 0.644 0.524 0.761 1.000
## internaljr 0.514 0.267 0.428 0.634 0.456 1.000
## emo 0.572 0.447 0.539 0.759 0.718 0.725 1.000
5 Factor CFA - Cross-Validation
fit.sps.cfa.5.test<- cfa(sps.cfa.5, data=test.dat, std.lv=T, ordered = c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2","B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1"))
summary(fit.sps.cfa.5.test, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 27 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 497 501
##
## Model Test User Model:
## Standard Robust
## Test Statistic 89.524 115.506
## Degrees of freedom 80 80
## P-value (Chi-square) 0.219 0.006
## Scaling correction factor 0.926
## Shift parameter 18.846
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 3548.794 2322.326
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.553
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 0.984
## Tucker-Lewis Index (TLI) 0.996 0.979
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.015 0.030
## 90 Percent confidence interval - lower 0.000 0.017
## 90 Percent confidence interval - upper 0.030 0.041
## P-value RMSEA <= 0.05 1.000 0.999
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.083 0.083
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tactile =~
## A1Q1 0.620 0.060 10.314 0.000 0.620 0.620
## A1Q2 0.774 0.057 13.621 0.000 0.774 0.774
## A1Q3 0.619 0.066 9.399 0.000 0.619 0.619
## B3Q1 0.821 0.049 16.651 0.000 0.821 0.821
## B3Q2 0.720 0.055 13.069 0.000 0.720 0.720
## musclejointbone =~
## A2Q1 0.795 0.088 8.999 0.000 0.795 0.795
## A2Q2 0.749 0.113 6.654 0.000 0.749 0.749
## lookjr =~
## B1Q1 0.997 0.033 30.633 0.000 0.997 0.997
## B1Q2 0.847 0.033 25.748 0.000 0.847 0.847
## B1Q3 0.819 0.036 22.914 0.000 0.819 0.819
## soundjr =~
## B2Q1 0.746 0.078 9.620 0.000 0.746 0.746
## B2Q2 0.737 0.077 9.560 0.000 0.737 0.737
## emo =~
## CQ1 0.677 0.064 10.614 0.000 0.677 0.677
## DQ1 0.702 0.063 11.132 0.000 0.702 0.702
## EQ1 0.729 0.066 11.010 0.000 0.729 0.729
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tactile ~~
## musclejointbon 0.687 0.091 7.536 0.000 0.687 0.687
## lookjr 0.671 0.055 12.222 0.000 0.671 0.671
## soundjr 0.738 0.090 8.198 0.000 0.738 0.738
## emo 0.688 0.077 8.953 0.000 0.688 0.688
## musclejointbone ~~
## lookjr 0.364 0.091 3.988 0.000 0.364 0.364
## soundjr 0.511 0.150 3.418 0.001 0.511 0.511
## emo 0.427 0.107 4.006 0.000 0.427 0.427
## lookjr ~~
## soundjr 0.643 0.081 7.940 0.000 0.643 0.643
## emo 0.554 0.067 8.233 0.000 0.554 0.554
## soundjr ~~
## emo 0.793 0.103 7.712 0.000 0.793 0.793
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.000 0.000 0.000
## .A1Q2 0.000 0.000 0.000
## .A1Q3 0.000 0.000 0.000
## .B3Q1 0.000 0.000 0.000
## .B3Q2 0.000 0.000 0.000
## .A2Q1 0.000 0.000 0.000
## .A2Q2 0.000 0.000 0.000
## .B1Q1 0.000 0.000 0.000
## .B1Q2 0.000 0.000 0.000
## .B1Q3 0.000 0.000 0.000
## .B2Q1 0.000 0.000 0.000
## .B2Q2 0.000 0.000 0.000
## .CQ1 0.000 0.000 0.000
## .DQ1 0.000 0.000 0.000
## .EQ1 0.000 0.000 0.000
## tactile 0.000 0.000 0.000
## musclejointbon 0.000 0.000 0.000
## lookjr 0.000 0.000 0.000
## soundjr 0.000 0.000 0.000
## emo 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1|t1 0.728 0.062 11.729 0.000 0.728 0.728
## A1Q2|t1 1.278 0.077 16.681 0.000 1.278 1.278
## A1Q3|t1 1.245 0.075 16.514 0.000 1.245 1.245
## B3Q1|t1 1.212 0.074 16.336 0.000 1.212 1.212
## B3Q2|t1 1.123 0.071 15.753 0.000 1.123 1.123
## A2Q1|t1 1.350 0.080 16.977 0.000 1.350 1.350
## A2Q2|t1 1.703 0.099 17.250 0.000 1.703 1.703
## B1Q1|t1 0.139 0.056 2.464 0.014 0.139 0.139
## B1Q2|t1 0.457 0.058 7.812 0.000 0.457 0.457
## B1Q3|t1 0.728 0.062 11.729 0.000 0.728 0.728
## B2Q1|t1 1.337 0.079 16.931 0.000 1.337 1.337
## B2Q2|t1 1.256 0.076 16.570 0.000 1.256 1.256
## CQ1|t1 0.999 0.068 14.739 0.000 0.999 0.999
## DQ1|t1 1.077 0.070 15.403 0.000 1.077 1.077
## EQ1|t1 0.708 0.062 11.474 0.000 0.708 0.708
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A1Q1 0.616 0.616 0.616
## .A1Q2 0.400 0.400 0.400
## .A1Q3 0.617 0.617 0.617
## .B3Q1 0.325 0.325 0.325
## .B3Q2 0.482 0.482 0.482
## .A2Q1 0.367 0.367 0.367
## .A2Q2 0.440 0.440 0.440
## .B1Q1 0.007 0.007 0.007
## .B1Q2 0.282 0.282 0.282
## .B1Q3 0.329 0.329 0.329
## .B2Q1 0.444 0.444 0.444
## .B2Q2 0.457 0.457 0.457
## .CQ1 0.541 0.541 0.541
## .DQ1 0.508 0.508 0.508
## .EQ1 0.468 0.468 0.468
## tactile 1.000 1.000 1.000
## musclejointbon 1.000 1.000 1.000
## lookjr 1.000 1.000 1.000
## soundjr 1.000 1.000 1.000
## emo 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A1Q1 1.000 1.000 1.000
## A1Q2 1.000 1.000 1.000
## A1Q3 1.000 1.000 1.000
## B3Q1 1.000 1.000 1.000
## B3Q2 1.000 1.000 1.000
## A2Q1 1.000 1.000 1.000
## A2Q2 1.000 1.000 1.000
## B1Q1 1.000 1.000 1.000
## B1Q2 1.000 1.000 1.000
## B1Q3 1.000 1.000 1.000
## B2Q1 1.000 1.000 1.000
## B2Q2 1.000 1.000 1.000
## CQ1 1.000 1.000 1.000
## DQ1 1.000 1.000 1.000
## EQ1 1.000 1.000 1.000
standardizedSolution(fit.sps.cfa.5.test, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tactile =~ A1Q1 0.620 0.060 10.314 0.000 0.521
## 2 tactile =~ A1Q2 0.774 0.057 13.621 0.000 0.681
## 3 tactile =~ A1Q3 0.619 0.066 9.399 0.000 0.511
## 4 tactile =~ B3Q1 0.821 0.049 16.651 0.000 0.740
## 5 tactile =~ B3Q2 0.720 0.055 13.069 0.000 0.629
## 6 musclejointbone =~ A2Q1 0.795 0.088 8.999 0.000 0.650
## 7 musclejointbone =~ A2Q2 0.749 0.113 6.654 0.000 0.564
## 8 lookjr =~ B1Q1 0.997 0.033 30.633 0.000 0.943
## 9 lookjr =~ B1Q2 0.847 0.033 25.748 0.000 0.793
## 10 lookjr =~ B1Q3 0.819 0.036 22.914 0.000 0.760
## 11 soundjr =~ B2Q1 0.746 0.078 9.620 0.000 0.618
## 12 soundjr =~ B2Q2 0.737 0.077 9.560 0.000 0.610
## 13 emo =~ CQ1 0.677 0.064 10.614 0.000 0.572
## 14 emo =~ DQ1 0.702 0.063 11.132 0.000 0.598
## 15 emo =~ EQ1 0.729 0.066 11.010 0.000 0.620
## 16 A1Q1 | t1 0.728 0.062 11.729 0.000 0.626
## 17 A1Q2 | t1 1.278 0.077 16.681 0.000 1.152
## 18 A1Q3 | t1 1.245 0.075 16.514 0.000 1.121
## 19 B3Q1 | t1 1.212 0.074 16.336 0.000 1.090
## 20 B3Q2 | t1 1.123 0.071 15.753 0.000 1.005
## 21 A2Q1 | t1 1.350 0.080 16.977 0.000 1.219
## 22 A2Q2 | t1 1.703 0.099 17.250 0.000 1.541
## 23 B1Q1 | t1 0.139 0.056 2.464 0.014 0.046
## 24 B1Q2 | t1 0.457 0.058 7.812 0.000 0.361
## 25 B1Q3 | t1 0.728 0.062 11.729 0.000 0.626
## 26 B2Q1 | t1 1.337 0.079 16.931 0.000 1.207
## 27 B2Q2 | t1 1.256 0.076 16.570 0.000 1.131
## 28 CQ1 | t1 0.999 0.068 14.739 0.000 0.887
## 29 DQ1 | t1 1.077 0.070 15.403 0.000 0.962
## 30 EQ1 | t1 0.708 0.062 11.474 0.000 0.607
## 31 A1Q1 ~~ A1Q1 0.616 0.074 8.276 0.000 0.494
## 32 A1Q2 ~~ A1Q2 0.400 0.088 4.546 0.000 0.255
## 33 A1Q3 ~~ A1Q3 0.617 0.082 7.557 0.000 0.482
## 34 B3Q1 ~~ B3Q1 0.325 0.081 4.013 0.000 0.192
## 35 B3Q2 ~~ B3Q2 0.482 0.079 6.080 0.000 0.352
## 36 A2Q1 ~~ A2Q1 0.367 0.141 2.611 0.009 0.136
## 37 A2Q2 ~~ A2Q2 0.440 0.168 2.609 0.009 0.162
## 38 B1Q1 ~~ B1Q1 0.007 0.065 0.108 0.914 -0.100
## 39 B1Q2 ~~ B1Q2 0.282 0.056 5.062 0.000 0.191
## 40 B1Q3 ~~ B1Q3 0.329 0.059 5.625 0.000 0.233
## 41 B2Q1 ~~ B2Q1 0.444 0.116 3.838 0.000 0.254
## 42 B2Q2 ~~ B2Q2 0.457 0.114 4.028 0.000 0.271
## 43 CQ1 ~~ CQ1 0.541 0.086 6.260 0.000 0.399
## 44 DQ1 ~~ DQ1 0.508 0.088 5.744 0.000 0.362
## 45 EQ1 ~~ EQ1 0.468 0.097 4.849 0.000 0.309
## 46 tactile ~~ tactile 1.000 0.000 NA NA 1.000
## 47 musclejointbone ~~ musclejointbone 1.000 0.000 NA NA 1.000
## 48 lookjr ~~ lookjr 1.000 0.000 NA NA 1.000
## 49 soundjr ~~ soundjr 1.000 0.000 NA NA 1.000
## 50 emo ~~ emo 1.000 0.000 NA NA 1.000
## 51 tactile ~~ musclejointbone 0.687 0.091 7.536 0.000 0.537
## 52 tactile ~~ lookjr 0.671 0.055 12.222 0.000 0.581
## 53 tactile ~~ soundjr 0.738 0.090 8.198 0.000 0.590
## 54 tactile ~~ emo 0.688 0.077 8.953 0.000 0.561
## 55 musclejointbone ~~ lookjr 0.364 0.091 3.988 0.000 0.214
## 56 musclejointbone ~~ soundjr 0.511 0.150 3.418 0.001 0.265
## 57 musclejointbone ~~ emo 0.427 0.107 4.006 0.000 0.251
## 58 lookjr ~~ soundjr 0.643 0.081 7.940 0.000 0.510
## 59 lookjr ~~ emo 0.554 0.067 8.233 0.000 0.443
## 60 soundjr ~~ emo 0.793 0.103 7.712 0.000 0.624
## 61 A1Q1 ~*~ A1Q1 1.000 0.000 NA NA 1.000
## 62 A1Q2 ~*~ A1Q2 1.000 0.000 NA NA 1.000
## 63 A1Q3 ~*~ A1Q3 1.000 0.000 NA NA 1.000
## 64 B3Q1 ~*~ B3Q1 1.000 0.000 NA NA 1.000
## 65 B3Q2 ~*~ B3Q2 1.000 0.000 NA NA 1.000
## 66 A2Q1 ~*~ A2Q1 1.000 0.000 NA NA 1.000
## 67 A2Q2 ~*~ A2Q2 1.000 0.000 NA NA 1.000
## 68 B1Q1 ~*~ B1Q1 1.000 0.000 NA NA 1.000
## 69 B1Q2 ~*~ B1Q2 1.000 0.000 NA NA 1.000
## 70 B1Q3 ~*~ B1Q3 1.000 0.000 NA NA 1.000
## 71 B2Q1 ~*~ B2Q1 1.000 0.000 NA NA 1.000
## 72 B2Q2 ~*~ B2Q2 1.000 0.000 NA NA 1.000
## 73 CQ1 ~*~ CQ1 1.000 0.000 NA NA 1.000
## 74 DQ1 ~*~ DQ1 1.000 0.000 NA NA 1.000
## 75 EQ1 ~*~ EQ1 1.000 0.000 NA NA 1.000
## 76 A1Q1 ~1 0.000 0.000 NA NA 0.000
## 77 A1Q2 ~1 0.000 0.000 NA NA 0.000
## 78 A1Q3 ~1 0.000 0.000 NA NA 0.000
## 79 B3Q1 ~1 0.000 0.000 NA NA 0.000
## 80 B3Q2 ~1 0.000 0.000 NA NA 0.000
## 81 A2Q1 ~1 0.000 0.000 NA NA 0.000
## 82 A2Q2 ~1 0.000 0.000 NA NA 0.000
## 83 B1Q1 ~1 0.000 0.000 NA NA 0.000
## 84 B1Q2 ~1 0.000 0.000 NA NA 0.000
## 85 B1Q3 ~1 0.000 0.000 NA NA 0.000
## 86 B2Q1 ~1 0.000 0.000 NA NA 0.000
## 87 B2Q2 ~1 0.000 0.000 NA NA 0.000
## 88 CQ1 ~1 0.000 0.000 NA NA 0.000
## 89 DQ1 ~1 0.000 0.000 NA NA 0.000
## 90 EQ1 ~1 0.000 0.000 NA NA 0.000
## 91 tactile ~1 0.000 0.000 NA NA 0.000
## 92 musclejointbone ~1 0.000 0.000 NA NA 0.000
## 93 lookjr ~1 0.000 0.000 NA NA 0.000
## 94 soundjr ~1 0.000 0.000 NA NA 0.000
## 95 emo ~1 0.000 0.000 NA NA 0.000
## ci.upper
## 1 0.718
## 2 0.868
## 3 0.728
## 4 0.903
## 5 0.810
## 6 0.941
## 7 0.934
## 8 1.050
## 9 0.901
## 10 0.878
## 11 0.873
## 12 0.863
## 13 0.782
## 14 0.805
## 15 0.838
## 16 0.830
## 17 1.404
## 18 1.369
## 19 1.334
## 20 1.240
## 21 1.481
## 22 1.866
## 23 0.232
## 24 0.553
## 25 0.830
## 26 1.467
## 27 1.380
## 28 1.110
## 29 1.191
## 30 0.810
## 31 0.739
## 32 0.545
## 33 0.751
## 34 0.459
## 35 0.612
## 36 0.599
## 37 0.717
## 38 0.114
## 39 0.374
## 40 0.426
## 41 0.634
## 42 0.644
## 43 0.683
## 44 0.653
## 45 0.627
## 46 1.000
## 47 1.000
## 48 1.000
## 49 1.000
## 50 1.000
## 51 0.837
## 52 0.762
## 53 0.886
## 54 0.814
## 55 0.514
## 56 0.757
## 57 0.602
## 58 0.777
## 59 0.665
## 60 0.962
## 61 1.000
## 62 1.000
## 63 1.000
## 64 1.000
## 65 1.000
## 66 1.000
## 67 1.000
## 68 1.000
## 69 1.000
## 70 1.000
## 71 1.000
## 72 1.000
## 73 1.000
## 74 1.000
## 75 1.000
## 76 0.000
## 77 0.000
## 78 0.000
## 79 0.000
## 80 0.000
## 81 0.000
## 82 0.000
## 83 0.000
## 84 0.000
## 85 0.000
## 86 0.000
## 87 0.000
## 88 0.000
## 89 0.000
## 90 0.000
## 91 0.000
## 92 0.000
## 93 0.000
## 94 0.000
## 95 0.000
Compare Fits for Models that Converged
for (mod in modnames.test) {
fits.test[count,]$chisq <- fitMeasures(mod, c("chisq"))
fits.test[count,]$df <- fitMeasures(mod, c("df"))
fits.test[count,]$pvalue <- fitMeasures(mod, c("pvalue"))
fits.test[count,]$cfi <- fitMeasures(mod, c("cfi"))
fits.test[count,]$rmsea <- fitMeasures(mod, c("rmsea"))
fits.test[count,]$srmr <- fitMeasures(mod, c("srmr"))
count <- count + 1
}
fits.test %>%
kable(digits=3, bootstrap_options = "striped", font_size = 10) %>%
kable_styling()
|
Model
|
chisq
|
df
|
pvalue
|
cfi
|
rmsea
|
srmr
|
|
6 Factor Model
|
71.728
|
70
|
0.420
|
0.999
|
0.007
|
0.076
|
|
5 Factor Model
|
89.524
|
80
|
0.219
|
0.997
|
0.015
|
0.083
|
LRT Comparing 6-Factor and 5-Factor Models
anova(fit.sps.cfa.6.test, fit.sps.cfa.5.test)
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.sps.cfa.6.test 70 71.728
## fit.sps.cfa.5.test 80 89.524 18.029 10 0.05448 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Create Composite Scores of USP-SPS Data
spsnames <- c("A1Q1", "A1Q2", "A1Q3", "A2Q1", "A2Q2", "B1Q1", "B1Q2", "B1Q3", "B2Q1", "B2Q2", "B3Q1", "B3Q2", "CQ1", "DQ1", "EQ1")
ctoccsv.yn[,spsnames] = data.frame(sapply(ctoccsv.yn[,spsnames], as.integer))
ctoccsv.yn$tacphys=rowMeans(ctoccsv.yn[,c("A1Q1", "A1Q2", "A1Q3")], na.rm=TRUE)
ctoccsv.yn$musclejointbone=rowMeans(ctoccsv.yn[,c("A2Q1", "A2Q2")], na.rm=TRUE)
ctoccsv.yn$lookjr=rowMeans(ctoccsv.yn[,c("B1Q1", "B1Q2", "B1Q3")], na.rm=TRUE)
ctoccsv.yn$soundjr=rowMeans(ctoccsv.yn[,c("B2Q1", "B2Q2")], na.rm=TRUE)
ctoccsv.yn$tactilejr=rowMeans(ctoccsv.yn[,c("B3Q1", "B3Q2")], na.rm=TRUE)
ctoccsv.yn$emo=rowMeans(ctoccsv.yn[,c("CQ1", "DQ1", "EQ1")], na.rm=TRUE)
Multivariate Regression Model
Composites from 6-Factor CFA of USP-SPS + DYBOCS
sps.cfa.6.dy <- '
tacphys ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
musclejointbone ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
lookjr ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
soundjr ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
tactilejr ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
emo ~ TOTALAGR + TOTALSEX + TOTALCON + TOTALSIM + TOTALCOL + TOTALSIN
'
fit.sps.cfa.6.dy<- cfa(sps.cfa.6.dy, data=ctoccsv.yn, std.ov=T)
summary(fit.sps.cfa.6.dy, fit.measures=T, standardized=T)
## lavaan 0.6-9 ended normally after 102 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Used Total
## Number of observations 991 1001
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 1975.265
## Degrees of freedom 51
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 94438.789
## Loglikelihood unrestricted model (H1) -17046.710
##
## Akaike (AIC) -188763.579
## Bayesian (BIC) -188484.352
## Sample-size adjusted Bayesian (BIC) -188665.386
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tacphys ~
## TOTALAGR 0.010 0.007 1.374 0.170 0.010 0.048
## TOTALSEX 0.009 0.007 1.359 0.174 0.009 0.045
## TOTALCON 0.031 0.006 4.811 0.000 0.031 0.160
## TOTALSIM 0.019 0.008 2.499 0.012 0.019 0.088
## TOTALCOL 0.020 0.008 2.480 0.013 0.020 0.081
## TOTALSIN 0.008 0.007 1.088 0.276 0.008 0.038
## musclejointbone ~
## TOTALAGR 0.003 0.007 0.368 0.713 0.003 0.013
## TOTALSEX 0.015 0.007 2.122 0.034 0.015 0.073
## TOTALCON -0.013 0.007 -1.925 0.054 -0.013 -0.066
## TOTALSIM 0.010 0.008 1.320 0.187 0.010 0.048
## TOTALCOL 0.017 0.008 2.094 0.036 0.017 0.071
## TOTALSIN 0.014 0.008 1.781 0.075 0.014 0.064
## lookjr ~
## TOTALAGR -0.009 0.007 -1.376 0.169 -0.009 -0.046
## TOTALSEX 0.006 0.007 0.879 0.379 0.006 0.028
## TOTALCON 0.020 0.006 3.197 0.001 0.020 0.103
## TOTALSIM 0.072 0.007 9.830 0.000 0.072 0.334
## TOTALCOL 0.016 0.008 2.115 0.034 0.016 0.067
## TOTALSIN -0.006 0.007 -0.821 0.412 -0.006 -0.027
## soundjr ~
## TOTALAGR -0.005 0.007 -0.685 0.493 -0.005 -0.024
## TOTALSEX 0.014 0.007 2.008 0.045 0.014 0.068
## TOTALCON -0.005 0.007 -0.733 0.463 -0.005 -0.025
## TOTALSIM 0.037 0.008 4.819 0.000 0.037 0.173
## TOTALCOL 0.018 0.008 2.177 0.030 0.018 0.073
## TOTALSIN 0.008 0.008 1.128 0.259 0.008 0.040
## tactilejr ~
## TOTALAGR 0.000 0.007 0.024 0.981 0.000 0.001
## TOTALSEX 0.007 0.007 1.050 0.294 0.007 0.036
## TOTALCON -0.007 0.007 -1.042 0.297 -0.007 -0.036
## TOTALSIM 0.027 0.008 3.507 0.000 0.027 0.127
## TOTALCOL 0.022 0.008 2.627 0.009 0.022 0.088
## TOTALSIN 0.013 0.008 1.679 0.093 0.013 0.060
## emo ~
## TOTALAGR -0.008 0.007 -1.098 0.272 -0.008 -0.039
## TOTALSEX -0.002 0.007 -0.222 0.824 -0.002 -0.008
## TOTALCON -0.005 0.007 -0.742 0.458 -0.005 -0.025
## TOTALSIM 0.036 0.008 4.543 0.000 0.036 0.165
## TOTALCOL 0.009 0.008 1.115 0.265 0.009 0.038
## TOTALSIN 0.011 0.008 1.426 0.154 0.011 0.051
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .tacphys ~~
## .musclejointbon 0.278 0.031 8.918 0.000 0.278 0.295
## .lookjr 0.310 0.030 10.496 0.000 0.310 0.354
## .soundjr 0.243 0.030 7.996 0.000 0.243 0.263
## .tactilejr 0.321 0.031 10.259 0.000 0.321 0.345
## .emo 0.236 0.031 7.685 0.000 0.236 0.252
## .musclejointbone ~~
## .lookjr 0.165 0.029 5.649 0.000 0.165 0.182
## .soundjr 0.231 0.031 7.385 0.000 0.231 0.241
## .tactilejr 0.255 0.032 8.058 0.000 0.255 0.265
## .emo 0.202 0.031 6.435 0.000 0.202 0.209
## .lookjr ~~
## .soundjr 0.254 0.029 8.616 0.000 0.254 0.285
## .tactilejr 0.239 0.030 8.085 0.000 0.239 0.266
## .emo 0.271 0.030 9.059 0.000 0.271 0.300
## .soundjr ~~
## .tactilejr 0.348 0.032 10.818 0.000 0.348 0.366
## .emo 0.308 0.032 9.659 0.000 0.308 0.322
## .tactilejr ~~
## .emo 0.290 0.032 9.091 0.000 0.290 0.302
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .tacphys 0.909 0.041 22.260 0.000 0.909 0.910
## .musclejointbon 0.972 0.044 22.260 0.000 0.972 0.973
## .lookjr 0.844 0.038 22.260 0.000 0.844 0.845
## .soundjr 0.944 0.042 22.260 0.000 0.944 0.945
## .tactilejr 0.956 0.043 22.260 0.000 0.956 0.957
## .emo 0.965 0.043 22.260 0.000 0.965 0.966
standardizedSolution(fit.sps.cfa.6.dy, ci=T, level = .90)
## lhs op rhs est.std se z pvalue ci.lower
## 1 tacphys ~ TOTALAGR 0.048 0.035 1.375 0.169 -0.009
## 2 tacphys ~ TOTALSEX 0.045 0.033 1.361 0.174 -0.009
## 3 tacphys ~ TOTALCON 0.160 0.033 4.887 0.000 0.106
## 4 tacphys ~ TOTALSIM 0.088 0.035 2.509 0.012 0.030
## 5 tacphys ~ TOTALCOL 0.081 0.033 2.490 0.013 0.028
## 6 tacphys ~ TOTALSIN 0.038 0.035 1.089 0.276 -0.019
## 7 musclejointbone ~ TOTALAGR 0.013 0.036 0.368 0.713 -0.046
## 8 musclejointbone ~ TOTALSEX 0.073 0.034 2.129 0.033 0.017
## 9 musclejointbone ~ TOTALCON -0.066 0.034 -1.930 0.054 -0.123
## 10 musclejointbone ~ TOTALSIM 0.048 0.036 1.322 0.186 -0.012
## 11 musclejointbone ~ TOTALCOL 0.071 0.034 2.100 0.036 0.015
## 12 musclejointbone ~ TOTALSIN 0.064 0.036 1.785 0.074 0.005
## 13 lookjr ~ TOTALAGR -0.046 0.033 -1.377 0.168 -0.101
## 14 lookjr ~ TOTALSEX 0.028 0.032 0.880 0.379 -0.025
## 15 lookjr ~ TOTALCON 0.103 0.032 3.217 0.001 0.050
## 16 lookjr ~ TOTALSIM 0.334 0.032 10.462 0.000 0.281
## 17 lookjr ~ TOTALCOL 0.067 0.031 2.121 0.034 0.015
## 18 lookjr ~ TOTALSIN -0.027 0.033 -0.821 0.412 -0.082
## 19 soundjr ~ TOTALAGR -0.024 0.035 -0.685 0.493 -0.082
## 20 soundjr ~ TOTALSEX 0.068 0.034 2.013 0.044 0.012
## 21 soundjr ~ TOTALCON -0.025 0.034 -0.733 0.463 -0.081
## 22 soundjr ~ TOTALSIM 0.173 0.035 4.899 0.000 0.115
## 23 soundjr ~ TOTALCOL 0.073 0.033 2.184 0.029 0.018
## 24 soundjr ~ TOTALSIN 0.040 0.035 1.129 0.259 -0.018
## 25 tactilejr ~ TOTALAGR 0.001 0.036 0.024 0.981 -0.058
## 26 tactilejr ~ TOTALSEX 0.036 0.034 1.051 0.293 -0.020
## 27 tactilejr ~ TOTALCON -0.036 0.034 -1.043 0.297 -0.092
## 28 tactilejr ~ TOTALSIM 0.127 0.036 3.538 0.000 0.068
## 29 tactilejr ~ TOTALCOL 0.088 0.033 2.640 0.008 0.033
## 30 tactilejr ~ TOTALSIN 0.060 0.035 1.683 0.092 0.001
## 31 emo ~ TOTALAGR -0.039 0.036 -1.099 0.272 -0.098
## 32 emo ~ TOTALSEX -0.008 0.034 -0.222 0.824 -0.064
## 33 emo ~ TOTALCON -0.025 0.034 -0.742 0.458 -0.082
## 34 emo ~ TOTALSIM 0.165 0.036 4.612 0.000 0.106
## 35 emo ~ TOTALCOL 0.038 0.034 1.116 0.264 -0.018
## 36 emo ~ TOTALSIN 0.051 0.036 1.428 0.153 -0.008
## 37 tacphys ~~ tacphys 0.910 0.017 53.703 0.000 0.882
## 38 musclejointbone ~~ musclejointbone 0.973 0.010 96.669 0.000 0.957
## 39 lookjr ~~ lookjr 0.845 0.020 41.568 0.000 0.811
## 40 soundjr ~~ soundjr 0.945 0.014 68.132 0.000 0.922
## 41 tactilejr ~~ tactilejr 0.957 0.012 76.893 0.000 0.937
## 42 emo ~~ emo 0.966 0.011 85.651 0.000 0.947
## 43 tacphys ~~ musclejointbone 0.295 0.029 10.188 0.000 0.248
## 44 tacphys ~~ lookjr 0.354 0.028 12.724 0.000 0.308
## 45 tacphys ~~ soundjr 0.263 0.030 8.879 0.000 0.214
## 46 tacphys ~~ tactilejr 0.345 0.028 12.314 0.000 0.299
## 47 tacphys ~~ emo 0.252 0.030 8.461 0.000 0.203
## 48 musclejointbone ~~ lookjr 0.182 0.031 5.940 0.000 0.132
## 49 musclejointbone ~~ soundjr 0.241 0.030 8.067 0.000 0.192
## 50 musclejointbone ~~ tactilejr 0.265 0.030 8.964 0.000 0.216
## 51 musclejointbone ~~ emo 0.209 0.030 6.874 0.000 0.159
## 52 lookjr ~~ soundjr 0.285 0.029 9.747 0.000 0.237
## 53 lookjr ~~ tactilejr 0.266 0.030 9.001 0.000 0.217
## 54 lookjr ~~ emo 0.300 0.029 10.398 0.000 0.253
## 55 soundjr ~~ tactilejr 0.366 0.028 13.301 0.000 0.321
## 56 soundjr ~~ emo 0.322 0.028 11.325 0.000 0.276
## 57 tactilejr ~~ emo 0.302 0.029 10.446 0.000 0.254
## 58 TOTALAGR ~~ TOTALAGR 1.000 0.000 NA NA 1.000
## 59 TOTALAGR ~~ TOTALSEX 0.386 0.000 NA NA 0.386
## 60 TOTALAGR ~~ TOTALCON 0.185 0.000 NA NA 0.185
## 61 TOTALAGR ~~ TOTALSIM 0.287 0.000 NA NA 0.287
## 62 TOTALAGR ~~ TOTALCOL 0.234 0.000 NA NA 0.234
## 63 TOTALAGR ~~ TOTALSIN 0.343 0.000 NA NA 0.343
## 64 TOTALSEX ~~ TOTALSEX 1.000 0.000 NA NA 1.000
## 65 TOTALSEX ~~ TOTALCON 0.147 0.000 NA NA 0.147
## 66 TOTALSEX ~~ TOTALSIM 0.201 0.000 NA NA 0.201
## 67 TOTALSEX ~~ TOTALCOL 0.171 0.000 NA NA 0.171
## 68 TOTALSEX ~~ TOTALSIN 0.265 0.000 NA NA 0.265
## 69 TOTALCON ~~ TOTALCON 1.000 0.000 NA NA 1.000
## 70 TOTALCON ~~ TOTALSIM 0.362 0.000 NA NA 0.362
## 71 TOTALCON ~~ TOTALCOL 0.244 0.000 NA NA 0.244
## 72 TOTALCON ~~ TOTALSIN 0.282 0.000 NA NA 0.282
## 73 TOTALSIM ~~ TOTALSIM 1.000 0.000 NA NA 1.000
## 74 TOTALSIM ~~ TOTALCOL 0.310 0.000 NA NA 0.310
## 75 TOTALSIM ~~ TOTALSIN 0.378 0.000 NA NA 0.378
## 76 TOTALCOL ~~ TOTALCOL 1.000 0.000 NA NA 1.000
## 77 TOTALCOL ~~ TOTALSIN 0.227 0.000 NA NA 0.227
## 78 TOTALSIN ~~ TOTALSIN 1.000 0.000 NA NA 1.000
## ci.upper
## 1 0.105
## 2 0.100
## 3 0.214
## 4 0.146
## 5 0.135
## 6 0.095
## 7 0.072
## 8 0.130
## 9 -0.010
## 10 0.108
## 11 0.126
## 12 0.123
## 13 0.009
## 14 0.081
## 15 0.155
## 16 0.386
## 17 0.118
## 18 0.027
## 19 0.034
## 20 0.124
## 21 0.031
## 22 0.231
## 23 0.127
## 24 0.098
## 25 0.059
## 26 0.092
## 27 0.021
## 28 0.186
## 29 0.143
## 30 0.118
## 31 0.020
## 32 0.049
## 33 0.031
## 34 0.224
## 35 0.093
## 36 0.110
## 37 0.938
## 38 0.990
## 39 0.878
## 40 0.968
## 41 0.978
## 42 0.984
## 43 0.343
## 44 0.399
## 45 0.311
## 46 0.391
## 47 0.301
## 48 0.233
## 49 0.291
## 50 0.313
## 51 0.259
## 52 0.333
## 53 0.314
## 54 0.348
## 55 0.411
## 56 0.369
## 57 0.349
## 58 1.000
## 59 0.386
## 60 0.185
## 61 0.287
## 62 0.234
## 63 0.343
## 64 1.000
## 65 0.147
## 66 0.201
## 67 0.171
## 68 0.265
## 69 1.000
## 70 0.362
## 71 0.244
## 72 0.282
## 73 1.000
## 74 0.310
## 75 0.378
## 76 1.000
## 77 0.227
## 78 1.000