Are Effective Managers also Family Supportive Supervisors?
Data Preparation
Package Loading
Data Summary
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country* | 1 | 287 | 2.2090592 | 0.6242659 | 2 | 2.2597403 | 0.0000 | 1 | 3 | 2 | -0.1793903 | -0.5979669 | 0.0368492 |
| AGECATE* | 2 | 287 | 2.1742160 | 1.0466216 | 3 | 2.1471861 | 1.4826 | 1 | 4 | 3 | -0.0585262 | -1.6349373 | 0.0617801 |
| GENDER* | 3 | 287 | 1.3414634 | 0.4750288 | 1 | 1.3030303 | 0.0000 | 1 | 2 | 1 | 0.6651562 | -1.5629767 | 0.0280401 |
| SEX | 4 | 287 | 0.3414634 | 0.4750288 | 0 | 0.3030303 | 0.0000 | 0 | 1 | 1 | 0.6651562 | -1.5629767 | 0.0280401 |
| POSITION* | 5 | 287 | 1.4738676 | 0.7231294 | 1 | 1.3463203 | 0.0000 | 1 | 3 | 2 | 1.1688984 | -0.1254913 | 0.0426850 |
| YEAR | 6 | 287 | 6.8710801 | 7.3065327 | 4 | 5.4415584 | 4.4478 | 0 | 38 | 38 | 1.8843478 | 3.5224802 | 0.4312910 |
| EF.CARE1 | 7 | 287 | 2.9721254 | 0.7928373 | 3 | 3.0086580 | 1.4826 | 1 | 4 | 3 | -0.3702672 | -0.4139917 | 0.0467997 |
| EF.LEARN | 8 | 287 | 2.7735192 | 0.8409925 | 3 | 2.8095238 | 1.4826 | 1 | 4 | 3 | -0.2942372 | -0.4890392 | 0.0496422 |
| EF.TRUST | 9 | 287 | 2.8745645 | 0.8309661 | 3 | 2.9177489 | 1.4826 | 1 | 4 | 3 | -0.3832902 | -0.4012388 | 0.0490504 |
| EF.INVOLVE | 10 | 287 | 2.6829268 | 0.8733553 | 3 | 2.7272727 | 1.4826 | 1 | 4 | 3 | -0.3784087 | -0.5040578 | 0.0515525 |
| EF.CARE2 | 11 | 287 | 2.7491289 | 0.8567651 | 3 | 2.8008658 | 1.4826 | 1 | 4 | 3 | -0.3627065 | -0.4528869 | 0.0505732 |
| EF.SUPP | 12 | 287 | 2.7247387 | 0.8388603 | 3 | 2.7532468 | 1.4826 | 1 | 4 | 3 | -0.2291517 | -0.5293616 | 0.0495164 |
| FSSB.IS | 13 | 287 | 2.6898955 | 0.8180667 | 3 | 2.7099567 | 1.4826 | 1 | 4 | 3 | -0.2187967 | -0.4539676 | 0.0482890 |
| FSSB.ES | 14 | 287 | 2.7630662 | 0.8525743 | 3 | 2.8008658 | 1.4826 | 1 | 4 | 3 | -0.2722095 | -0.5521557 | 0.0503259 |
| FSSB.CM | 15 | 287 | 2.7804878 | 0.8511012 | 3 | 2.8268398 | 1.4826 | 1 | 4 | 3 | -0.3477726 | -0.4606684 | 0.0502389 |
Analysis
Correlation
EF.CARE1 EF.CARE2 EF.LEARN EF.INVOLVE EF.TRUST EF.SUPP
EF.CARE1 1.0000000 0.6485369 0.5253808 0.5325505 0.7058410 0.6350672
EF.CARE2 0.6485369 1.0000000 0.5225996 0.5755576 0.6972385 0.6236011
EF.LEARN 0.5253808 0.5225996 1.0000000 0.5635946 0.5796184 0.5556341
EF.INVOLVE 0.5325505 0.5755576 0.5635946 1.0000000 0.5857888 0.5820222
EF.TRUST 0.7058410 0.6972385 0.5796184 0.5857888 1.0000000 0.6926680
EF.SUPP 0.6350672 0.6236011 0.5556341 0.5820222 0.6926680 1.0000000
FSSB.IS 0.6550975 0.6468912 0.5734935 0.5715110 0.6266693 0.6547336
FSSB.ES 0.6781650 0.6076326 0.5637218 0.5749508 0.6883359 0.6760495
FSSB.IS FSSB.ES FSSB.CM
EF.CARE1 0.6550975 0.6781650 0.5816095
EF.CARE2 0.6468912 0.6076326 0.5475686
EF.LEARN 0.5734935 0.5637218 0.5262647
EF.INVOLVE 0.5715110 0.5749508 0.5128443
EF.TRUST 0.6266693 0.6883359 0.5492550
EF.SUPP 0.6547336 0.6760495 0.5664230
FSSB.IS 1.0000000 0.6863685 0.5396655
FSSB.ES 0.6863685 1.0000000 0.5448540
[ reached getOption("max.print") -- omitted 1 row ]Call: mixed.cor(x = data_sel1_corr, method = "spearman")
EF.CARE1 EF.CARE2 EF.LE EF.IN EF.TR EF.SU FSSB.I FSSB.E FSSB.C
EF.CARE1 1.00
EF.CARE2 0.73 1.00
EF.LEARN -0.17 -0.37 1.00
EF.INVOLVE -0.03 0.30 0.17 1.00
EF.TRUST 0.80 0.88 -0.20 0.37 1.00
EF.SUPP 0.63 0.55 0.05 0.45 0.68 1.00
FSSB.IS 0.65 0.47 0.02 -0.05 0.45 0.62 1.00
FSSB.ES 0.82 0.55 0.23 0.28 0.67 0.80 0.78 1.00
[ reached getOption("max.print") -- omitted 1 row ] EF.CARE1 EF.CARE2 EF.LE EF.IN EF.TR EF.SU FSSB.I FSSB.E FSSB.C
EF.CARE1 1.00
EF.CARE2 0.73 1.00
EF.LEARN -0.17 -0.37 1.00
EF.INVOLVE -0.03 0.30 0.17 1.00
EF.TRUST 0.80 0.88 -0.20 0.37 1.00
EF.SUPP 0.63 0.55 0.05 0.45 0.68 1.00
FSSB.IS 0.65 0.47 0.02 -0.05 0.45 0.62 1.00
FSSB.ES 0.82 0.55 0.23 0.28 0.67 0.80 0.78 1.00
[ reached getOption("max.print") -- omitted 1 row ]Call:corr.test(x = data_sel1_corr, method = "spearman")
Correlation matrix
EF.CARE1 EF.CARE2 EF.LEARN EF.INVOLVE EF.TRUST EF.SUPP FSSB.IS
EF.CARE1 1.00 0.73 -0.17 -0.03 0.80 0.63 0.65
EF.CARE2 0.73 1.00 -0.37 0.30 0.88 0.55 0.47
EF.LEARN -0.17 -0.37 1.00 0.17 -0.20 0.05 0.02
EF.INVOLVE -0.03 0.30 0.17 1.00 0.37 0.45 -0.05
EF.TRUST 0.80 0.88 -0.20 0.37 1.00 0.68 0.45
EF.SUPP 0.63 0.55 0.05 0.45 0.68 1.00 0.62
FSSB.IS 0.65 0.47 0.02 -0.05 0.45 0.62 1.00
FSSB.ES 0.82 0.55 0.23 0.28 0.67 0.80 0.78
FSSB.ES FSSB.CM
EF.CARE1 0.82 0.42
EF.CARE2 0.55 0.27
EF.LEARN 0.23 -0.55
EF.INVOLVE 0.28 -0.43
EF.TRUST 0.67 0.30
EF.SUPP 0.80 0.25
FSSB.IS 0.78 -0.03
FSSB.ES 1.00 -0.02
[ reached getOption("max.print") -- omitted 1 row ]
Sample Size
[1] 9
Probability values (Entries above the diagonal are adjusted for multiple tests.)
EF.CARE1 EF.CARE2 EF.LEARN EF.INVOLVE EF.TRUST EF.SUPP FSSB.IS
EF.CARE1 0.00 0.76 1.00 1.00 0.33 1.00 1.00
EF.CARE2 0.02 0.00 1.00 1.00 0.06 1.00 1.00
EF.LEARN 0.67 0.33 0.00 1.00 1.00 1.00 1.00
EF.INVOLVE 0.93 0.43 0.67 0.00 1.00 1.00 1.00
EF.TRUST 0.01 0.00 0.61 0.33 0.00 1.00 1.00
EF.SUPP 0.07 0.12 0.90 0.22 0.04 0.00 1.00
FSSB.IS 0.06 0.21 0.97 0.90 0.22 0.08 0.00
FSSB.ES 0.01 0.12 0.55 0.46 0.05 0.01 0.01
FSSB.ES FSSB.CM
EF.CARE1 0.25 1
EF.CARE2 1.00 1
EF.LEARN 1.00 1
EF.INVOLVE 1.00 1
EF.TRUST 1.00 1
EF.SUPP 0.33 1
FSSB.IS 0.40 1
FSSB.ES 0.00 1
[ reached getOption("max.print") -- omitted 1 row ]
To see confidence intervals of the correlations, print with the short=FALSE optionCovariance
EF.CARE1 EF.CARE2 EF.LEARN EF.INVOLVE EF.TRUST EF.SUPP
EF.CARE1 0.6285909 0.4405351 0.3503082 0.3687532 0.4650228 0.4223703
EF.CARE2 0.4405351 0.7340464 0.3765503 0.4306669 0.4963939 0.4481860
EF.LEARN 0.3503082 0.3765503 0.7072683 0.4139519 0.4050584 0.3919861
EF.INVOLVE 0.3687532 0.4306669 0.4139519 0.7627494 0.4251237 0.4264029
EF.TRUST 0.4650228 0.4963939 0.4050584 0.4251237 0.6905046 0.4828342
EF.SUPP 0.4223703 0.4481860 0.3919861 0.4264029 0.4828342 0.7036866
FSSB.IS 0.4248922 0.4534003 0.3945567 0.4083234 0.4260008 0.4493068
FSSB.ES 0.4584074 0.4438488 0.4041934 0.4281085 0.4876587 0.4835043
FSSB.IS FSSB.ES FSSB.CM
EF.CARE1 0.4248922 0.4584074 0.3924612
EF.CARE2 0.4534003 0.4438488 0.3992836
EF.LEARN 0.3945567 0.4041934 0.3766843
EF.INVOLVE 0.4083234 0.4281085 0.3812042
EF.TRUST 0.4260008 0.4876587 0.3884530
EF.SUPP 0.4493068 0.4835043 0.4044005
FSSB.IS 0.6692332 0.4787164 0.3757462
FSSB.ES 0.4787164 0.7268829 0.3953607
[ reached getOption("max.print") -- omitted 1 row ]Descriptive
Normality Test
Beta-hat kappa p-val
Skewness 7.093847 339.322351 3.874678e-14
Kurtosis 112.111374 7.892718 2.886580e-15
Bartlett test of homogeneity of variances
data: EF.CARE1 by EF.CARE2
Bartlett's K-squared = 9.3219, df = 3, p-value = 0.0253
Bartlett test of homogeneity of variances
data: EF.CARE1 by EF.LEARN
Bartlett's K-squared = 7.4673, df = 3, p-value = 0.0584
Fligner-Killeen test of homogeneity of variances
data: EF.CARE1 by EF.CARE2
Fligner-Killeen:med chi-squared = 10.291, df = 3, p-value =
0.01625
Fligner-Killeen test of homogeneity of variances
data: EF.CARE1 by EF.LEARN
Fligner-Killeen:med chi-squared = 9.4009, df = 3, p-value =
0.02441PCA
Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4
Standard deviation 2.4111744 0.75768556 0.71428935 0.66982715
Proportion of Variance 0.6459735 0.06378749 0.05668992 0.04985205
Cumulative Proportion 0.6459735 0.70976103 0.76645095 0.81630299
Comp.5 Comp.6 Comp.7 Comp.8
Standard deviation 0.63509403 0.6123425 0.59082449 0.53596926
Proportion of Variance 0.04481605 0.0416626 0.03878595 0.03191812
Cumulative Proportion 0.86111904 0.9027816 0.94156759 0.97348571
Comp.9
Standard deviation 0.48849630
Proportion of Variance 0.02651429
Cumulative Proportion 1.00000000
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
EF.CARE1 -0.344 0.346 -0.176 0.113 -0.577 -0.542 0.289
EF.CARE2 -0.338 0.229 0.384 0.553 0.316 0.292 0.270 0.348
EF.LEARN -0.308 -0.641 0.162 -0.544 0.391 0.131
EF.INVOLVE -0.314 -0.458 0.371 0.637 -0.265 -0.261
EF.TRUST -0.353 0.229 0.331 -0.440 0.102 -0.703
EF.SUPP -0.345 0.123 -0.317 -0.442 0.636 -0.317 0.245
FSSB.IS -0.343 0.125 0.146 -0.220 -0.273 0.707 0.176 -0.188 -0.395
FSSB.ES -0.347 0.197 0.130 -0.274 -0.394 -0.268 0.678 0.232
[ reached getOption("max.print") -- omitted 1 row ]
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Proportion Var 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
Cumulative Var 0.111 0.222 0.333 0.444 0.556 0.667 0.778 0.889
Comp.9
SS loadings 1.000
Proportion Var 0.111
Cumulative Var 1.000
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
[1,] -0.319737592 -1.04638486 -1.0921219775 -2.910773e-01 -1.328182367
[2,] -2.376975205 0.25312096 0.4757499354 8.073743e-01 -0.103913386
[3,] -0.780687170 -0.15850426 0.0825359200 5.952987e-02 -0.126273726
[4,] 0.024072839 -0.25221455 -0.5407824788 5.915276e-01 1.192722984
[5,] -3.222116466 0.86181836 -0.6778455516 6.754802e-03 0.109244939
[6,] -0.780687170 -0.15850426 0.0825359200 5.952987e-02 -0.126273726
[7,] -0.421053675 0.18647818 1.1042760769 -8.925572e-02 0.029153571
[8,] 2.012368421 -0.82785974 1.9741057154 -2.530500e-01 -0.162877968
Comp.6 Comp.7 Comp.8 Comp.9
[1,] 0.313341500 -0.0398415026 -0.207614694 0.305798308
[2,] 0.342734688 -0.8265258234 0.391269657 1.038271970
[3,] 0.117343951 0.1765640221 0.076414191 0.003094703
[4,] -0.131587117 -1.2133403753 -0.795752930 0.286470236
[5,] 0.733214806 -0.3327270253 0.551200926 -0.421124003
[6,] 0.117343951 0.1765640221 0.076414191 0.003094703
[7,] 0.082937092 0.1250896831 -0.078038617 0.141097727
[8,] 0.861397576 0.0905285255 -0.404766337 -0.211085117
[ reached getOption("max.print") -- omitted 279 rows ]
Latent Class Model
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0348 0.223 0.4774 0.2648
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0906 0.2509 0.4774 0.1812
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0732 0.2718 0.4634 0.1916
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.115 0.2439 0.4843 0.1568
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0592 0.2369 0.4739 0.23
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0767 0.2962 0.453 0.1742
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0767 0.3066 0.4669 0.1498
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0767 0.2787 0.4495 0.1951
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0801 0.2544 0.4704 0.1951
Estimated class population shares
1
Predicted class memberships (by modal posterior prob.)
1
=========================================================
Fit for 1 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 27
residual degrees of freedom: 260
maximum log-likelihood: -3131.752
AIC(1): 6317.504
BIC(1): 6416.31
G^2(1): 3239.88 (Likelihood ratio/deviance statistic)
X^2(1): 123398324 (Chi-square goodness of fit)
Model 1: llik = -2562.537 ... best llik = -2562.537
Model 2: llik = -2562.537 ... best llik = -2562.537
Model 3: llik = -2562.537 ... best llik = -2562.537
Model 4: llik = -2562.537 ... best llik = -2562.537
Model 5: llik = -2562.537 ... best llik = -2562.537
Model 6: llik = -2562.537 ... best llik = -2562.537
Model 7: llik = -2562.537 ... best llik = -2562.537
Model 8: llik = -2562.537 ... best llik = -2562.537
Model 9: llik = -2562.537 ... best llik = -2562.537
Model 10: llik = -2562.537 ... best llik = -2562.537
Model 11: llik = -2562.537 ... best llik = -2562.537
Model 12: llik = -2562.537 ... best llik = -2562.537
Model 13: llik = -2562.537 ... best llik = -2562.537
Model 14: llik = -2568.329 ... best llik = -2562.537
Model 15: llik = -2562.537 ... best llik = -2562.537
Model 16: llik = -2562.537 ... best llik = -2562.537
Model 17: llik = -2562.537 ... best llik = -2562.537
Model 18: llik = -2568.329 ... best llik = -2562.537
Model 19: llik = -2562.537 ... best llik = -2562.537
Model 20: llik = -2562.537 ... best llik = -2562.537
Model 21: llik = -2562.537 ... best llik = -2562.537
Model 22: llik = -2562.537 ... best llik = -2562.537
Model 23: llik = -2562.537 ... best llik = -2562.537
Model 24: llik = -2568.329 ... best llik = -2562.537
Model 25: llik = -2562.537 ... best llik = -2562.537
Model 26: llik = -2568.329 ... best llik = -2562.537
Model 27: llik = -2562.537 ... best llik = -2562.537
Model 28: llik = -2562.537 ... best llik = -2562.537
Model 29: llik = -2562.537 ... best llik = -2562.537
Model 30: llik = -2562.537 ... best llik = -2562.537
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0082 0.5204 0.4715
class 2: 0.0733 0.4603 0.4298 0.0365
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0063 0.0382 0.6296 0.3259
class 2: 0.1837 0.4858 0.3092 0.0213
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.000 0.0972 0.5445 0.3584
class 2: 0.154 0.4647 0.3739 0.0074
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0115 0.0899 0.5999 0.2987
class 2: 0.2293 0.4141 0.3566 0.0000
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0223 0.5473 0.4304
class 2: 0.1247 0.4740 0.3927 0.0086
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0385 0.6398 0.3217
class 2: 0.1613 0.5809 0.2466 0.0112
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0349 0.7079 0.2572
class 2: 0.1613 0.6068 0.2006 0.0312
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0240 0.6042 0.3717
class 2: 0.1613 0.5602 0.2785 0.0000
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0066 0.0722 0.5494 0.3717
class 2: 0.1613 0.4555 0.3831 0.0000
Estimated class population shares
0.5249 0.4751
Predicted class memberships (by modal posterior prob.)
0.5226 0.4774
=========================================================
Fit for 2 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 55
residual degrees of freedom: 232
maximum log-likelihood: -2562.537
AIC(2): 5235.075
BIC(2): 5436.346
G^2(2): 2101.451 (Likelihood ratio/deviance statistic)
X^2(2): 613805.7 (Chi-square goodness of fit)
Model 1: llik = -2377.346 ... best llik = -2377.346
Model 2: llik = -2377.346 ... best llik = -2377.346
Model 3: llik = -2377.346 ... best llik = -2377.346
Model 4: llik = -2377.346 ... best llik = -2377.346
Model 5: llik = -2377.346 ... best llik = -2377.346
Model 6: llik = -2377.346 ... best llik = -2377.346
Model 7: llik = -2377.346 ... best llik = -2377.346
Model 8: llik = -2377.346 ... best llik = -2377.346
Model 9: llik = -2377.346 ... best llik = -2377.346
Model 10: llik = -2377.346 ... best llik = -2377.346
Model 11: llik = -2377.346 ... best llik = -2377.346
Model 12: llik = -2377.346 ... best llik = -2377.346
Model 13: llik = -2377.346 ... best llik = -2377.346
Model 14: llik = -2377.346 ... best llik = -2377.346
Model 15: llik = -2377.346 ... best llik = -2377.346
Model 16: llik = -2377.346 ... best llik = -2377.346
Model 17: llik = -2377.346 ... best llik = -2377.346
Model 18: llik = -2377.346 ... best llik = -2377.346
Model 19: llik = -2377.346 ... best llik = -2377.346
Model 20: llik = -2377.346 ... best llik = -2377.346
Model 21: llik = -2377.346 ... best llik = -2377.346
Model 22: llik = -2377.346 ... best llik = -2377.346
Model 23: llik = -2377.346 ... best llik = -2377.346
Model 24: llik = -2377.346 ... best llik = -2377.346
Model 25: llik = -2377.346 ... best llik = -2377.346
Model 26: llik = -2377.346 ... best llik = -2377.346
Model 27: llik = -2377.346 ... best llik = -2377.346
Model 28: llik = -2377.346 ... best llik = -2377.346
Model 29: llik = -2377.346 ... best llik = -2377.346
Model 30: llik = -2377.346 ... best llik = -2377.346
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0311 0.8081 0.1608
class 2: 0.0916 0.5521 0.3280 0.0283
class 3: 0.0000 0.0000 0.0723 0.9277
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0249 0.0987 0.7741 0.1023
class 2: 0.2108 0.5510 0.2281 0.0101
class 3: 0.0000 0.0000 0.3316 0.6684
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0085 0.1880 0.6442 0.1593
class 2: 0.1831 0.4896 0.3182 0.0092
class 3: 0.0000 0.0343 0.3621 0.6035
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0199 0.1837 0.6912 0.1052
class 2: 0.2805 0.4296 0.2899 0.0000
class 3: 0.0000 0.0181 0.4216 0.5602
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0654 0.7972 0.1375
class 2: 0.1557 0.5511 0.2932 0.0000
class 3: 0.0000 0.0000 0.1433 0.8567
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.1141 0.7983 0.0877
class 2: 0.2015 0.6532 0.1452 0.0000
class 3: 0.0000 0.0000 0.3168 0.6832
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.1825 0.7834 0.0341
class 2: 0.2015 0.6054 0.1614 0.0316
class 3: 0.0000 0.0000 0.3862 0.6138
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.1370 0.7548 0.1082
class 2: 0.2015 0.5822 0.2163 0.0000
class 3: 0.0000 0.0000 0.2557 0.7443
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.1542 0.6903 0.1555
class 2: 0.2015 0.4868 0.3116 0.0000
class 3: 0.0173 0.0233 0.3132 0.6462
Estimated class population shares
0.4184 0.3803 0.2013
Predicted class memberships (by modal posterior prob.)
0.4146 0.3868 0.1986
=========================================================
Fit for 3 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 83
residual degrees of freedom: 204
maximum log-likelihood: -2377.346
AIC(3): 4920.691
BIC(3): 5224.428
G^2(3): 1731.067 (Likelihood ratio/deviance statistic)
X^2(3): 241884.2 (Chi-square goodness of fit)
Model 1: llik = -2294.161 ... best llik = -2294.161
Model 2: llik = -2295.032 ... best llik = -2294.161
Model 3: llik = -2307.302 ... best llik = -2294.161
Model 4: llik = -2295.032 ... best llik = -2294.161
Model 5: llik = -2295.692 ... best llik = -2294.161
Model 6: llik = -2298.128 ... best llik = -2294.161
Model 7: llik = -2297.101 ... best llik = -2294.161
Model 8: llik = -2294.461 ... best llik = -2294.161
Model 9: llik = -2294.161 ... best llik = -2294.161
Model 10: llik = -2295.032 ... best llik = -2294.161
Model 11: llik = -2294.485 ... best llik = -2294.161
Model 12: llik = -2297.101 ... best llik = -2294.161
Model 13: llik = -2294.485 ... best llik = -2294.161
Model 14: llik = -2295.032 ... best llik = -2294.161
Model 15: llik = -2295.032 ... best llik = -2294.161
Model 16: llik = -2295.032 ... best llik = -2294.161
Model 17: llik = -2307.302 ... best llik = -2294.161
Model 18: llik = -2295.763 ... best llik = -2294.161
Model 19: llik = -2370.156 ... best llik = -2294.161
Model 20: llik = -2295.032 ... best llik = -2294.161
Model 21: llik = -2295.032 ... best llik = -2294.161
Model 22: llik = -2318.301 ... best llik = -2294.161
Model 23: llik = -2294.161 ... best llik = -2294.161
Model 24: llik = -2295.032 ... best llik = -2294.161
Model 25: llik = -2295.067 ... best llik = -2294.161
Model 26: llik = -2295.032 ... best llik = -2294.161
Model 27: llik = -2295.032 ... best llik = -2294.161
Model 28: llik = -2294.161 ... best llik = -2294.161
Model 29: llik = -2295.853 ... best llik = -2294.161
Model 30: llik = -2295.032 ... best llik = -2294.161
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.5031 0.4535 0.0434
class 2: 0.3529 0.4625 0.1846 0.0000
class 3: 0.0000 0.0189 0.7830 0.1981
class 4: 0.0000 0.0000 0.0632 0.9368
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0798 0.5339 0.3601 0.0262
class 2: 0.5655 0.4047 0.0298 0.0000
class 3: 0.0207 0.0806 0.7825 0.1162
class 4: 0.0000 0.0000 0.3140 0.6860
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0920 0.4859 0.4221 0.0000
class 2: 0.3898 0.4454 0.1295 0.0353
class 3: 0.0095 0.1521 0.6456 0.1928
class 4: 0.0000 0.0337 0.3491 0.6172
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1242 0.4777 0.3980 0.0000
class 2: 0.6736 0.2495 0.0769 0.0000
class 3: 0.0172 0.1444 0.7105 0.1280
class 4: 0.0000 0.0184 0.4020 0.5797
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0154 0.5598 0.4240 0.0008
class 2: 0.5472 0.3031 0.1497 0.0000
class 3: 0.0000 0.0469 0.7816 0.1716
class 4: 0.0000 0.0000 0.1191 0.8809
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0812 0.6646 0.2542 0.0000
class 2: 0.4982 0.4509 0.0509 0.0000
class 3: 0.0000 0.0713 0.8212 0.1075
class 4: 0.0000 0.0000 0.2865 0.7135
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0512 0.6942 0.2115 0.0431
class 2: 0.6010 0.3990 0.0000 0.0000
class 3: 0.0000 0.0862 0.8801 0.0337
class 4: 0.0000 0.0000 0.3466 0.6534
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0514 0.6517 0.2969 0.0000
class 2: 0.6003 0.3304 0.0692 0.0000
class 3: 0.0000 0.0683 0.7977 0.1341
class 4: 0.0000 0.0000 0.2283 0.7717
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1314 0.4387 0.4299 0.0000
class 2: 0.3262 0.5547 0.1190 0.0000
class 3: 0.0000 0.1254 0.6833 0.1913
class 4: 0.0186 0.0217 0.3024 0.6573
Estimated class population shares
0.3383 0.0987 0.3753 0.1876
Predicted class memberships (by modal posterior prob.)
0.338 0.0976 0.3728 0.1916
=========================================================
Fit for 4 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 111
residual degrees of freedom: 176
maximum log-likelihood: -2294.161
AIC(4): 4810.322
BIC(4): 5216.525
G^2(4): 1564.698 (Likelihood ratio/deviance statistic)
X^2(4): 118814 (Chi-square goodness of fit)
Model 1: llik = -2252.947 ... best llik = -2252.947
Model 2: llik = -2269.295 ... best llik = -2252.947
Model 3: llik = -2255.204 ... best llik = -2252.947
Model 4: llik = -2262.421 ... best llik = -2252.947
Model 5: llik = -2253.062 ... best llik = -2252.947
Model 6: llik = -2281.306 ... best llik = -2252.947
Model 7: llik = -2276.786 ... best llik = -2252.947
Model 8: llik = -2273.137 ... best llik = -2252.947
Model 9: llik = -2252.947 ... best llik = -2252.947
Model 10: llik = -2272.954 ... best llik = -2252.947
Model 11: llik = -2256.082 ... best llik = -2252.947
Model 12: llik = -2252.947 ... best llik = -2252.947
Model 13: llik = -2268.529 ... best llik = -2252.947
Model 14: llik = -2268.641 ... best llik = -2252.947
Model 15: llik = -2257.654 ... best llik = -2252.947
Model 16: llik = -2269.295 ... best llik = -2252.947
Model 17: llik = -2276.805 ... best llik = -2252.947
Model 18: llik = -2253.773 ... best llik = -2252.947
Model 19: llik = -2272.407 ... best llik = -2252.947
Model 20: llik = -2252.947 ... best llik = -2252.947
Model 21: llik = -2267.069 ... best llik = -2252.947
Model 22: llik = -2269.295 ... best llik = -2252.947
Model 23: llik = -2268.641 ... best llik = -2252.947
Model 24: llik = -2256.621 ... best llik = -2252.947
Model 25: llik = -2254.118 ... best llik = -2252.947
Model 26: llik = -2281.672 ... best llik = -2252.947
Model 27: llik = -2271.993 ... best llik = -2252.947
Model 28: llik = -2257.042 ... best llik = -2252.947
Model 29: llik = -2277.993 ... best llik = -2252.947
Model 30: llik = -2269.295 ... best llik = -2252.947
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.5799 0.3485 0.0717 0.0000
class 2: 0.0000 0.0116 0.7673 0.2211
class 3: 0.0000 0.0000 0.0602 0.9398
class 4: 0.0000 0.2107 0.7031 0.0862
class 5: 0.0324 0.7246 0.2258 0.0172
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.8669 0.0606 0.0000 0.0725
class 2: 0.0210 0.0585 0.7827 0.1378
class 3: 0.0000 0.0000 0.3027 0.6973
class 4: 0.0421 0.2945 0.6317 0.0316
class 5: 0.1513 0.7588 0.0899 0.0000
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.5798 0.3477 0.0000 0.0725
class 2: 0.0105 0.1086 0.6572 0.2238
class 3: 0.0000 0.0350 0.3331 0.6319
class 4: 0.0000 0.4623 0.5377 0.0000
class 5: 0.1942 0.5119 0.2939 0.0000
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.6400 0.2890 0.0710 0.0000
class 2: 0.0125 0.1079 0.7293 0.1503
class 3: 0.0000 0.0187 0.3861 0.5953
class 4: 0.0563 0.4305 0.5133 0.0000
class 5: 0.3143 0.4427 0.2431 0.0000
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.8531 0.0000 0.1469 0.0000
class 2: 0.0000 0.0339 0.7794 0.1868
class 3: 0.0000 0.0000 0.0971 0.9029
class 4: 0.0000 0.3536 0.6188 0.0275
class 5: 0.0848 0.6855 0.2297 0.0000
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.6355 0.2920 0.0725 0.0000
class 2: 0.0000 0.0544 0.8269 0.1187
class 3: 0.0000 0.0000 0.2651 0.7349
class 4: 0.0636 0.3960 0.5258 0.0146
class 5: 0.1492 0.8200 0.0308 0.0000
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.7823 0.1452 0.0000 0.0725
class 2: 0.0000 0.0420 0.9264 0.0315
class 3: 0.0000 0.0000 0.3135 0.6865
class 4: 0.0000 0.6660 0.2730 0.0610
class 5: 0.1816 0.6435 0.1749 0.0000
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.7097 0.2903 0.0000 0.0000
class 2: 0.0000 0.0320 0.8039 0.1640
class 3: 0.0000 0.0000 0.2155 0.7845
class 4: 0.0316 0.5687 0.3996 0.0000
class 5: 0.1654 0.5971 0.2375 0.0000
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.5800 0.2749 0.1451 0.0000
class 2: 0.0000 0.1097 0.6604 0.2299
class 3: 0.0196 0.0224 0.2986 0.6594
class 4: 0.0642 0.3002 0.6356 0.0000
class 5: 0.1610 0.6221 0.2169 0.0000
Estimated class population shares
0.0481 0.3381 0.178 0.2208 0.215
Predicted class memberships (by modal posterior prob.)
0.0488 0.3415 0.1742 0.2195 0.216
=========================================================
Fit for 5 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 139
residual degrees of freedom: 148
maximum log-likelihood: -2252.947
AIC(5): 4783.895
BIC(5): 5292.563
G^2(5): 1482.271 (Likelihood ratio/deviance statistic)
X^2(5): 65604.04 (Chi-square goodness of fit)
Model 1: llik = -2231.675 ... best llik = -2231.675
Model 2: llik = -2229.089 ... best llik = -2229.089
Model 3: llik = -2238.935 ... best llik = -2229.089
Model 4: llik = -2237.868 ... best llik = -2229.089
Model 5: llik = -2252.464 ... best llik = -2229.089
Model 6: llik = -2240.661 ... best llik = -2229.089
Model 7: llik = -2230.174 ... best llik = -2229.089
Model 8: llik = -2238.196 ... best llik = -2229.089
Model 9: llik = -2234.28 ... best llik = -2229.089
Model 10: llik = -2241.441 ... best llik = -2229.089
Model 11: llik = -2235.046 ... best llik = -2229.089
Model 12: llik = -2238.296 ... best llik = -2229.089
Model 13: llik = -2235.612 ... best llik = -2229.089
Model 14: llik = -2235.014 ... best llik = -2229.089
Model 15: llik = -2257.342 ... best llik = -2229.089
Model 16: llik = -2231.22 ... best llik = -2229.089
Model 17: llik = -2246.802 ... best llik = -2229.089
Model 18: llik = -2253.331 ... best llik = -2229.089
Model 19: llik = -2239.615 ... best llik = -2229.089
Model 20: llik = -2242.104 ... best llik = -2229.089
Model 21: llik = -2237.103 ... best llik = -2229.089
Model 22: llik = -2235.625 ... best llik = -2229.089
Model 23: llik = -2262.894 ... best llik = -2229.089
Model 24: llik = -2236.235 ... best llik = -2229.089
Model 25: llik = -2235.879 ... best llik = -2229.089
Model 26: llik = -2247.161 ... best llik = -2229.089
Model 27: llik = -2237.268 ... best llik = -2229.089
Model 28: llik = -2231.771 ... best llik = -2229.089
Model 29: llik = -2228.957 ... best llik = -2228.957
Model 30: llik = -2247.513 ... best llik = -2228.957
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.2197 0.6863 0.0941
class 2: 0.0326 0.7260 0.2243 0.0171
class 3: 0.0000 0.0000 0.0516 0.9484
class 4: 0.0000 0.0000 0.2061 0.7939
class 5: 0.0000 0.0141 0.8649 0.1209
class 6: 0.5799 0.3484 0.0717 0.0000
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0420 0.2886 0.6363 0.0330
class 2: 0.1519 0.7624 0.0856 0.0000
class 3: 0.0000 0.0000 0.0994 0.9006
class 4: 0.0000 0.0000 0.4900 0.5100
class 5: 0.0268 0.0832 0.8345 0.0555
class 6: 0.8669 0.0606 0.0000 0.0725
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.4796 0.5204 0.0000
class 2: 0.1947 0.5072 0.2981 0.0000
class 3: 0.0000 0.0000 0.1232 0.8768
class 4: 0.0000 0.0747 0.5396 0.3857
class 5: 0.0132 0.1121 0.6727 0.2020
class 6: 0.5798 0.3477 0.0000 0.0725
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0579 0.4473 0.4948 0.0000
class 2: 0.3155 0.4382 0.2463 0.0000
class 3: 0.0000 0.0000 0.1008 0.8992
class 4: 0.0000 0.0395 0.6323 0.3282
class 5: 0.0157 0.1217 0.7444 0.1183
class 6: 0.6399 0.2890 0.0710 0.0000
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.3574 0.6122 0.0304
class 2: 0.0852 0.6865 0.2282 0.0000
class 3: 0.0000 0.0000 0.0602 0.9398
class 4: 0.0000 0.0000 0.2389 0.7611
class 5: 0.0000 0.0488 0.8709 0.0802
class 6: 0.8531 0.0000 0.1469 0.0000
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0692 0.4088 0.5078 0.0142
class 2: 0.1463 0.8214 0.0324 0.0000
class 3: 0.0000 0.0000 0.0441 0.9559
class 4: 0.0000 0.0000 0.5758 0.4242
class 5: 0.0000 0.0682 0.8364 0.0955
class 6: 0.6355 0.2920 0.0725 0.0000
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.6866 0.2510 0.0624
class 2: 0.1826 0.6405 0.1769 0.0000
class 3: 0.0000 0.0000 0.0000 1.0000
class 4: 0.0000 0.0204 0.6736 0.3060
class 5: 0.0000 0.0437 0.9376 0.0187
class 6: 0.7823 0.1452 0.0000 0.0725
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0326 0.5781 0.3893 0.0000
class 2: 0.1662 0.5963 0.2375 0.0000
class 3: 0.0000 0.0000 0.0585 0.9415
class 4: 0.0000 0.0000 0.4737 0.5263
class 5: 0.0000 0.0485 0.8289 0.1226
class 6: 0.7097 0.2903 0.0000 0.0000
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0721 0.2940 0.6339 0.0000
class 2: 0.1559 0.6270 0.2171 0.0000
class 3: 0.0462 0.0000 0.0712 0.8826
class 4: 0.0000 0.0625 0.5022 0.4353
class 5: 0.0000 0.1203 0.6848 0.1949
class 6: 0.5800 0.2749 0.1451 0.0000
Estimated class population shares
0.2142 0.2139 0.0754 0.1713 0.2772 0.0481
Predicted class memberships (by modal posterior prob.)
0.2056 0.216 0.0767 0.1812 0.2718 0.0488
=========================================================
Fit for 6 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 167
residual degrees of freedom: 120
maximum log-likelihood: -2228.957
AIC(6): 4791.915
BIC(6): 5403.048
G^2(6): 1434.291 (Likelihood ratio/deviance statistic)
X^2(6): 57772.07 (Chi-square goodness of fit)
NULL Model log-likelihood resid. df BIC aBIC cAIC
1 Modell 1 -3131.752 260 6416.310 5261.935 6443.310
2 Modell 2 -2562.537 232 5436.346 5261.935 5491.346
3 Modell 3 -2377.346 204 5224.428 4961.226 5307.428
4 Modell 4 -2294.161 176 5216.525 4864.532 5327.525
5 Modell 5 -2252.947 148 5292.563 4851.778 5431.563
6 Modell 6 -2228.957 120 5403.048 4873.473 5570.048
likelihood-ratio Entropy
1 3239.880 -
2 2101.451 0.928
3 1731.067 0.942
4 1564.698 NaN
5 1482.271 0.815
6 1434.291 NaN| Model | log-likelihood | resid. df | BIC | aBIC | cAIC | likelihood-ratio | Entropy |
|---|---|---|---|---|---|---|---|
| Modell 1 | -3131.752 | 260 | 6416.310 | 5261.935 | 6443.310 | 3239.880 |
|
| Modell 2 | -2562.537 | 232 | 5436.346 | 5261.935 | 5491.346 | 2101.451 | 0.928 |
| Modell 3 | -2377.346 | 204 | 5224.428 | 4961.226 | 5307.428 | 1731.067 | 0.942 |
| Modell 4 | -2294.161 | 176 | 5216.525 | 4864.532 | 5327.525 | 1564.698 | NaN |
| Modell 5 | -2252.947 | 148 | 5292.563 | 4851.778 | 5431.563 | 1482.271 | 0.815 |
| Modell 6 | -2228.957 | 120 | 5403.048 | 4873.473 | 5570.048 | 1434.291 | NaN |
Model log-likelihood resid. df Entropy Kriterium Guete
1 Modell 1 -3131.752 260 - BIC 6416.310
2 Modell 2 -2562.537 232 0.928 BIC 5436.346
3 Modell 3 -2377.346 204 0.942 BIC 5224.428
4 Modell 4 -2294.161 176 NaN BIC 5216.525
5 Modell 5 -2252.947 148 0.815 BIC 5292.563
6 Modell 6 -2228.957 120 NaN BIC 5403.048
7 Modell 1 -3131.752 260 - aBIC 5261.935
8 Modell 2 -2562.537 232 0.928 aBIC 5261.935
9 Modell 3 -2377.346 204 0.942 aBIC 4961.226
10 Modell 4 -2294.161 176 NaN aBIC 4864.532
11 Modell 5 -2252.947 148 0.815 aBIC 4851.778
12 Modell 6 -2228.957 120 NaN aBIC 4873.473
[ reached getOption("max.print") -- omitted 12 rows ]
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.5031 0.4535 0.0434
class 2: 0.3529 0.4625 0.1846 0.0000
class 3: 0.0000 0.0189 0.7830 0.1981
class 4: 0.0000 0.0000 0.0632 0.9368
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0798 0.5339 0.3601 0.0262
class 2: 0.5655 0.4047 0.0298 0.0000
class 3: 0.0207 0.0806 0.7825 0.1162
class 4: 0.0000 0.0000 0.3140 0.6860
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0920 0.4859 0.4221 0.0000
class 2: 0.3898 0.4454 0.1295 0.0353
class 3: 0.0095 0.1521 0.6456 0.1928
class 4: 0.0000 0.0337 0.3491 0.6172
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1242 0.4777 0.3980 0.0000
class 2: 0.6736 0.2495 0.0769 0.0000
class 3: 0.0172 0.1444 0.7105 0.1280
class 4: 0.0000 0.0184 0.4020 0.5797
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0154 0.5598 0.4240 0.0008
class 2: 0.5472 0.3031 0.1497 0.0000
class 3: 0.0000 0.0469 0.7816 0.1716
class 4: 0.0000 0.0000 0.1191 0.8809
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0812 0.6646 0.2542 0.0000
class 2: 0.4982 0.4509 0.0509 0.0000
class 3: 0.0000 0.0713 0.8212 0.1075
class 4: 0.0000 0.0000 0.2865 0.7135
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0512 0.6942 0.2115 0.0431
class 2: 0.6010 0.3990 0.0000 0.0000
class 3: 0.0000 0.0862 0.8801 0.0337
class 4: 0.0000 0.0000 0.3466 0.6534
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0514 0.6517 0.2969 0.0000
class 2: 0.6003 0.3304 0.0692 0.0000
class 3: 0.0000 0.0683 0.7977 0.1341
class 4: 0.0000 0.0000 0.2283 0.7717
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1314 0.4387 0.4299 0.0000
class 2: 0.3262 0.5547 0.1190 0.0000
class 3: 0.0000 0.1254 0.6833 0.1913
class 4: 0.0186 0.0217 0.3024 0.6573
Estimated class population shares
0.3383 0.0987 0.3753 0.1876
Predicted class memberships (by modal posterior prob.)
0.338 0.0976 0.3728 0.1916
=========================================================
Fit for 4 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 111
residual degrees of freedom: 176
maximum log-likelihood: -2294.161
AIC(4): 4810.322
BIC(4): 5216.525
G^2(4): 1564.698 (Likelihood ratio/deviance statistic)
X^2(4): 118814 (Chi-square goodness of fit)
[1] 33.83 9.87 37.53 18.76
1 2 3 4
97 28 107 55
1 2 3 4
33.80 9.76 37.28 19.16 Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.5031 0.4535 0.0434
class 2: 0.3529 0.4625 0.1846 0.0000
class 3: 0.0000 0.0189 0.7830 0.1981
class 4: 0.0000 0.0000 0.0632 0.9368
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0798 0.5339 0.3601 0.0262
class 2: 0.5655 0.4047 0.0298 0.0000
class 3: 0.0207 0.0806 0.7825 0.1162
class 4: 0.0000 0.0000 0.3140 0.6860
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0920 0.4859 0.4221 0.0000
class 2: 0.3898 0.4454 0.1295 0.0353
class 3: 0.0095 0.1521 0.6456 0.1928
class 4: 0.0000 0.0337 0.3491 0.6172
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1242 0.4777 0.3980 0.0000
class 2: 0.6736 0.2495 0.0769 0.0000
class 3: 0.0172 0.1444 0.7105 0.1280
class 4: 0.0000 0.0184 0.4020 0.5797
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0154 0.5598 0.4240 0.0008
class 2: 0.5472 0.3031 0.1497 0.0000
class 3: 0.0000 0.0469 0.7816 0.1716
class 4: 0.0000 0.0000 0.1191 0.8809
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0812 0.6646 0.2542 0.0000
class 2: 0.4982 0.4509 0.0509 0.0000
class 3: 0.0000 0.0713 0.8212 0.1075
class 4: 0.0000 0.0000 0.2865 0.7135
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0512 0.6942 0.2115 0.0431
class 2: 0.6010 0.3990 0.0000 0.0000
class 3: 0.0000 0.0862 0.8801 0.0337
class 4: 0.0000 0.0000 0.3466 0.6534
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0514 0.6517 0.2969 0.0000
class 2: 0.6003 0.3304 0.0692 0.0000
class 3: 0.0000 0.0683 0.7977 0.1341
class 4: 0.0000 0.0000 0.2283 0.7717
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.1314 0.4387 0.4299 0.0000
class 2: 0.3262 0.5547 0.1190 0.0000
class 3: 0.0000 0.1254 0.6833 0.1913
class 4: 0.0186 0.0217 0.3024 0.6573
Estimated class population shares
0.3383 0.0987 0.3753 0.1876
Predicted class memberships (by modal posterior prob.)
0.338 0.0976 0.3728 0.1916
=========================================================
Fit for 4 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 111
residual degrees of freedom: 176
maximum log-likelihood: -2294.161
AIC(4): 4810.322
BIC(4): 5216.525
G^2(4): 1564.698 (Likelihood ratio/deviance statistic)
X^2(4): 118814 (Chi-square goodness of fit)
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.0632 0.9368
class 2: 0.0000 0.0189 0.7831 0.1981
class 3: 0.0000 0.5031 0.4535 0.0435
class 4: 0.3528 0.4626 0.1846 0.0000
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.3140 0.6860
class 2: 0.0207 0.0806 0.7825 0.1162
class 3: 0.0797 0.5339 0.3602 0.0262
class 4: 0.5654 0.4048 0.0298 0.0000
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0337 0.3491 0.6172
class 2: 0.0095 0.1520 0.6456 0.1928
class 3: 0.0919 0.4859 0.4222 0.0000
class 4: 0.3898 0.4454 0.1295 0.0353
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0184 0.4020 0.5797
class 2: 0.0172 0.1443 0.7105 0.1280
class 3: 0.1242 0.4777 0.3981 0.0000
class 4: 0.6736 0.2495 0.0769 0.0000
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.1191 0.8809
class 2: 0.0000 0.0469 0.7816 0.1715
class 3: 0.0154 0.5597 0.4240 0.0010
class 4: 0.5471 0.3032 0.1497 0.0000
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.2865 0.7135
class 2: 0.0000 0.0713 0.8212 0.1075
class 3: 0.0812 0.6645 0.2543 0.0000
class 4: 0.4981 0.4510 0.0509 0.0000
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.3466 0.6534
class 2: 0.0000 0.0862 0.8801 0.0337
class 3: 0.0512 0.6941 0.2117 0.0431
class 4: 0.6009 0.3991 0.0000 0.0000
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.2283 0.7717
class 2: 0.0000 0.0682 0.7977 0.1341
class 3: 0.0513 0.6517 0.2970 0.0000
class 4: 0.6003 0.3305 0.0693 0.0000
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0186 0.0218 0.3024 0.6573
class 2: 0.0000 0.1254 0.6833 0.1913
class 3: 0.1313 0.4387 0.4300 0.0000
class 4: 0.3262 0.5548 0.1190 0.0000
Estimated class population shares
0.1876 0.3752 0.3384 0.0988
Predicted class memberships (by modal posterior prob.)
0.1916 0.3728 0.338 0.0976
=========================================================
Fit for 4 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 111
residual degrees of freedom: 176
maximum log-likelihood: -2294.161
AIC(4): 4810.322
BIC(4): 5216.525
G^2(4): 1564.698 (Likelihood ratio/deviance statistic)
X^2(4): 118851.1 (Chi-square goodness of fit)
ALERT: iterations finished, MAXIMUM LIKELIHOOD NOT FOUND
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.0632 0.9368
class 2: 0.0000 0.0189 0.7831 0.1981
class 3: 0.0000 0.5031 0.4535 0.0435
class 4: 0.3528 0.4626 0.1846 0.0000
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.3140 0.6860
class 2: 0.0207 0.0806 0.7825 0.1162
class 3: 0.0797 0.5339 0.3602 0.0262
class 4: 0.5654 0.4048 0.0298 0.0000
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0337 0.3491 0.6172
class 2: 0.0095 0.1520 0.6456 0.1928
class 3: 0.0919 0.4859 0.4222 0.0000
class 4: 0.3898 0.4454 0.1295 0.0353
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0184 0.4020 0.5797
class 2: 0.0172 0.1443 0.7105 0.1280
class 3: 0.1242 0.4777 0.3981 0.0000
class 4: 0.6736 0.2495 0.0769 0.0000
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.1191 0.8809
class 2: 0.0000 0.0469 0.7816 0.1715
class 3: 0.0154 0.5597 0.4240 0.0010
class 4: 0.5471 0.3032 0.1497 0.0000
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.2865 0.7135
class 2: 0.0000 0.0713 0.8212 0.1075
class 3: 0.0812 0.6645 0.2543 0.0000
class 4: 0.4981 0.4510 0.0509 0.0000
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.3466 0.6534
class 2: 0.0000 0.0862 0.8801 0.0337
class 3: 0.0512 0.6941 0.2117 0.0431
class 4: 0.6009 0.3991 0.0000 0.0000
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0000 0.0000 0.2283 0.7717
class 2: 0.0000 0.0682 0.7977 0.1341
class 3: 0.0513 0.6517 0.2970 0.0000
class 4: 0.6003 0.3305 0.0693 0.0000
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.0186 0.0218 0.3024 0.6573
class 2: 0.0000 0.1254 0.6833 0.1913
class 3: 0.1313 0.4387 0.4300 0.0000
class 4: 0.3262 0.5548 0.1190 0.0000
Estimated class population shares
0.1876 0.3752 0.3384 0.0988
Predicted class memberships (by modal posterior prob.)
0.1916 0.3728 0.338 0.0976
=========================================================
Fit for 4 latent classes:
=========================================================
number of observations: 287
number of estimated parameters: 111
residual degrees of freedom: 176
maximum log-likelihood: -2294.161
AIC(4): 4810.322
BIC(4): 5216.525
G^2(4): 1564.698 (Likelihood ratio/deviance statistic)
X^2(4): 118851.1 (Chi-square goodness of fit)
ALERT: iterations finished, MAXIMUM LIKELIHOOD NOT FOUND
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$EF.CARE1
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.2727 0.5323 0.1951 0.0000
class 2: 0.0000 0.4743 0.4757 0.0499
class 3: 0.0000 0.0181 0.7829 0.1989
class 4: 0.0000 0.0000 0.0594 0.9406
$EF.CARE2
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.5378 0.4059 0.0292 0.0271
class 2: 0.0424 0.5449 0.3988 0.0139
class 3: 0.0230 0.0769 0.7760 0.1241
class 4: 0.0000 0.0000 0.3168 0.6832
$EF.LEARN
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.3943 0.4282 0.1503 0.0273
class 2: 0.0613 0.4919 0.4468 0.0000
class 3: 0.0097 0.1532 0.6410 0.1960
class 4: 0.0000 0.0327 0.3493 0.6179
$EF.INVOLVE
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.6682 0.2723 0.0596 0.0000
class 2: 0.0734 0.4885 0.4381 0.0000
class 3: 0.0178 0.1418 0.7075 0.1329
class 4: 0.0000 0.0186 0.4051 0.5763
$EF.TRUST
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.4635 0.3487 0.1878 0.0000
class 2: 0.0000 0.5609 0.4362 0.0029
class 3: 0.0000 0.0458 0.7810 0.1732
class 4: 0.0000 0.0000 0.1160 0.8840
$EF.SUPP
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.3845 0.5641 0.0514 0.0000
class 2: 0.0881 0.6385 0.2735 0.0000
class 3: 0.0000 0.0658 0.8263 0.1079
class 4: 0.0000 0.0000 0.2798 0.7202
$FSSB.IS
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.5057 0.3965 0.0707 0.0271
class 2: 0.0385 0.7225 0.2040 0.0350
class 3: 0.0000 0.0808 0.8843 0.0349
class 4: 0.0000 0.0000 0.3416 0.6584
$FSSB.ES
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.4671 0.3968 0.1361 0.0000
class 2: 0.0543 0.6459 0.2998 0.0000
class 3: 0.0000 0.0702 0.7930 0.1368
class 4: 0.0000 0.0000 0.2254 0.7746
$FSSB.CM
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.2987 0.5587 0.1426 0.0000
class 2: 0.1232 0.4249 0.4519 0.0000
class 3: 0.0000 0.1241 0.6856 0.1903
class 4: 0.0187 0.0205 0.2938 0.6669
Estimated class population shares
0.1278 0.3124 0.3739 0.1859
Predicted class memberships (by modal posterior prob.)
0.1289 0.3136 0.3693 0.1882
=========================================================
Fit for 4 latent classes:
=========================================================
2 / 1
Coefficient Std. error t value Pr(>|t|)
(Intercept) 1.03947 0.73796 1.409 0.161
AGECATE36-55 9.13051 0.52540 17.378 0.000
AGECATELess than 35 -0.00285 0.79703 -0.004 0.997
AGECATEMore than 55 0.50723 1.59331 0.318 0.751
GENDERMale -0.33674 0.67100 -0.502 0.616
POSITIONMiddle Manager 0.65750 0.77389 0.850 0.397
POSITIONTop Manager 2.12669 1.75530 1.212 0.228
YEAR -0.06306 0.05120 -1.231 0.220
=========================================================
3 / 1
Coefficient Std. error t value Pr(>|t|)
(Intercept) 1.30151 0.69231 1.880 0.062
AGECATE36-55 9.04882 0.47806 18.928 0.000
AGECATELess than 35 -0.22209 0.76499 -0.290 0.772
AGECATEMore than 55 0.02120 1.48005 0.014 0.989
GENDERMale 0.21782 0.63160 0.345 0.731
POSITIONMiddle Manager 0.22887 0.74522 0.307 0.759
POSITIONTop Manager 1.59993 1.72538 0.927 0.355
YEAR -0.05769 0.04351 -1.326 0.187
=========================================================
4 / 1
Coefficient Std. error t value Pr(>|t|)
(Intercept) 0.49372 0.83049 0.594 0.553
AGECATE36-55 8.65946 0.63264 13.688 0.000
AGECATELess than 35 0.15311 0.87624 0.175 0.862
AGECATEMore than 55 0.28391 1.59166 0.178 0.859
GENDERMale -0.20026 0.81942 -0.244 0.807
POSITIONMiddle Manager 0.01824 0.91393 0.020 0.984
POSITIONTop Manager 1.58116 1.75692 0.900 0.370
YEAR -0.04066 0.04625 -0.879 0.381
=========================================================
number of observations: 287
number of estimated parameters: 132
residual degrees of freedom: 155
maximum log-likelihood: -2285.927
AIC(4): 4835.855
BIC(4): 5318.906
X^2(4): 72379.29 (Chi-square goodness of fit)
ALERT: estimation algorithm automatically restarted with new initial values
Plot
Logit
Call:
glm(formula = data_sel1$class_1 ~ GENDER + AGECATE + POSITION +
YEAR, family = binomial, data = data_sel1)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.7329 -0.6784 -0.6398 -0.5533 2.0982
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.433640 0.380057 -3.772 0.000162 ***
GENDERMale -0.239904 0.336100 -0.714 0.475357
AGECATE36-55 -0.256175 0.818680 -0.313 0.754347
AGECATELess than 35 0.193291 0.368811 0.524 0.600215
AGECATEMore than 55 0.001414 0.731322 0.002 0.998458
POSITIONMiddle Manager -0.248657 0.427189 -0.582 0.560515
POSITIONTop Manager -0.081514 0.469323 -0.174 0.862114
YEAR 0.006299 0.025256 0.249 0.803037
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 280.45 on 286 degrees of freedom
Residual deviance: 278.69 on 279 degrees of freedom
AIC: 294.69
Number of Fisher Scoring iterations: 4
Call:
glm(formula = data_sel1$class_2 ~ GENDER + AGECATE + POSITION +
YEAR, family = binomial, data = data_sel1)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2609 -0.9479 -0.8901 1.3180 1.6800
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52805 0.30610 -1.725 0.0845 .
GENDERMale 0.44513 0.26359 1.689 0.0913 .
AGECATE36-55 0.42432 0.60356 0.703 0.4820
AGECATELess than 35 -0.13453 0.29856 -0.451 0.6523
AGECATEMore than 55 -0.25022 0.60655 -0.413 0.6800
POSITIONMiddle Manager 0.02494 0.32925 0.076 0.9396
POSITIONTop Manager 0.02812 0.37559 0.075 0.9403
YEAR -0.01472 0.02108 -0.699 0.4848
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 379.09 on 286 degrees of freedom
Residual deviance: 374.92 on 279 degrees of freedom
AIC: 390.92
Number of Fisher Scoring iterations: 4
Call:
glm(formula = data_sel1$class_3 ~ GENDER + AGECATE + POSITION +
YEAR, family = binomial, data = data_sel1)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.1303 -0.8983 -0.8744 1.4053 1.6528
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.61656 0.31071 -1.984 0.0472 *
GENDERMale -0.19006 0.27450 -0.692 0.4887
AGECATE36-55 0.27936 0.61740 0.452 0.6509
AGECATELess than 35 -0.07113 0.30422 -0.234 0.8151
AGECATEMore than 55 0.28727 0.58950 0.487 0.6260
POSITIONMiddle Manager 0.17096 0.33749 0.507 0.6125
POSITIONTop Manager 0.39795 0.37370 1.065 0.2869
YEAR -0.01151 0.02114 -0.544 0.5862
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 367.18 on 286 degrees of freedom
Residual deviance: 364.98 on 279 degrees of freedom
AIC: 380.98
Number of Fisher Scoring iterations: 4
Call:
glm(formula = data_sel1$class_4 ~ GENDER + AGECATE + POSITION +
YEAR, family = binomial, data = data_sel1)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.8826 -0.4942 -0.4460 -0.2806 2.4623
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.34988 0.50306 -4.671 3e-06 ***
GENDERMale -0.29076 0.45904 -0.633 0.526
AGECATE36-55 -15.58123 1079.51893 -0.014 0.988
AGECATELess than 35 0.17088 0.49537 0.345 0.730
AGECATEMore than 55 -0.20691 0.93146 -0.222 0.824
POSITIONMiddle Manager -0.07759 0.52699 -0.147 0.883
POSITIONTop Manager -1.55089 1.05454 -1.471 0.141
YEAR 0.04594 0.02948 1.558 0.119
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 183.5 on 286 degrees of freedom
Residual deviance: 174.3 on 279 degrees of freedom
AIC: 190.3
Number of Fisher Scoring iterations: 16LCA Descriptive
Call: xtabs(formula = ~data_sel1$class + data_sel1$AGE + data_sel1$GENDER +
data_sel1$POSITION + data_sel1$YEAR)
Number of cases in table: 287
Number of factors: 5
Test for independence of all factors:
Chisq = 5047, df = 3126, p-value = 1.728e-94
Chi-squared approximation may be incorrect
35-55 36-55 Less than 35 More than 55
1 21 2 29 3
2 46 6 50 5
3 41 5 45 6
4 12 0 14 2
Female Male
1 39 16
2 64 43
3 66 31
4 20 8
Employee Middle Manager Top Manager
1 39 9 7
2 69 23 15
3 61 20 16
4 21 6 1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23
1 0 12 6 6 5 8 6 0 0 0 2 0 0 1 1 2 0 0 0 1 1 0 1
2 2 26 13 12 10 4 4 2 4 2 4 2 0 2 1 6 2 2 0 1 1 2 0
24 25 28 29 31 32 34 35 37 38
1 0 1 0 0 1 0 0 1 0 0
2 1 2 0 1 0 1 0 0 0 0
[ reached getOption("max.print") -- omitted 2 rows ]
SEM
Cronbach a
$sample.size
[1] 287
$number.of.items
[1] 9
$alpha
[1] 0.9306655Model (no group)
lavaan (0.5-20) converged normally after 33 iterations
Number of observations 287
Estimator ML
Minimum Function Test Statistic 41.509
Degrees of freedom 26
P-value (Chi-square) 0.028
Model test baseline model:
Minimum Function Test Statistic 1690.636
Degrees of freedom 36
P-value 0.000
User model versus baseline model:
Comparative Fit Index (CFI) 0.991
Tucker-Lewis Index (TLI) 0.987
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2383.240
Loglikelihood unrestricted model (H1) -2362.486
Number of free parameters 19
Akaike (AIC) 4804.480
Bayesian (BIC) 4874.011
Sample-size adjusted Bayesian (BIC) 4813.760
Root Mean Square Error of Approximation:
RMSEA 0.046
90 Percent Confidence Interval 0.015 0.071
P-value RMSEA <= 0.05 0.582
Standardized Root Mean Square Residual:
SRMR 0.022
Parameter Estimates:
Information Expected
Standard Errors Standard
Latent Variables:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE =~
EF.CARE1 1.000 0.642 0.811
EF.CARE2 1.054 0.069 15.299 0.000 0.676 0.790
EF.LEARN 0.910 0.071 12.907 0.000 0.584 0.696
EF.INVOLVE 0.968 0.073 13.310 0.000 0.621 0.713
EF.TRUST 1.087 0.065 16.694 0.000 0.697 0.841
EF.SUPP 1.058 0.067 15.842 0.000 0.679 0.810
FSSB =~
FSSB.IS 1.000 0.658 0.806
FSSB.ES 1.065 0.067 15.981 0.000 0.701 0.823
[ reached getOption("max.print") -- omitted 1 row ]
Regressions:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE ~
FSSB 0.968 0.065 14.783 0.000 0.992 0.992
Variances:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EF.CARE1 0.215 0.021 10.346 0.000 0.215 0.343
EF.CARE2 0.274 0.026 10.564 0.000 0.274 0.375
EF.LEARN 0.364 0.033 11.188 0.000 0.364 0.516
EF.INVOLVE 0.374 0.034 11.109 0.000 0.374 0.492
EF.TRUST 0.202 0.020 9.916 0.000 0.202 0.293
EF.SUPP 0.241 0.023 10.349 0.000 0.241 0.343
FSSB.IS 0.234 0.024 9.878 0.000 0.234 0.351
FSSB.ES 0.233 0.025 9.512 0.000 0.233 0.322
FSSB.CM 0.377 0.034 11.056 0.000 0.377 0.522
EFFECTIVE 0.006 0.012 0.504 0.614 0.015 0.015
[ reached getOption("max.print") -- omitted 1 row ] npar fmin chisq
19.000 0.072 41.509
df pvalue baseline.chisq
26.000 0.028 1690.636
baseline.df baseline.pvalue cfi
36.000 0.000 0.991
tli nnfi rfi
0.987 0.987 0.966
nfi pnfi ifi
0.975 0.704 0.991
rni logl unrestricted.logl
0.991 -2383.240 -2362.486
aic bic ntotal
4804.480 4874.011 287.000
bic2 rmsea rmsea.ci.lower
4813.760 0.046 0.015
rmsea.ci.upper rmsea.pvalue rmr
0.071 0.582 0.016
rmr_nomean srmr srmr_bentler
0.016 0.022 0.022
srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
0.022 0.022 0.022
srmr_mplus srmr_mplus_nomean cn_05
0.022 0.022 269.861
cn_01 gfi agfi
316.578 0.970 0.949
pgfi mfi ecvi
0.561 0.973 0.277 gammaHat adjGammaHat baseline.rmsea aic.smallN bic.priorN
0.9880933 0.9793923 0.4001834 4769.3268690 4874.0766762
hqc sic
4832.3470585 4893.6120376 gammaHat adjGammaHat baseline.rmsea aic.smallN bic.priorN
0.9880933 0.9793923 0.4001834 4769.3268690 4874.0766762
hqc sic
4832.3470585 4893.6120376 npar fmin chisq
19.000 0.072 41.509
df pvalue baseline.chisq
26.000 0.028 1690.636
baseline.df baseline.pvalue cfi
36.000 0.000 0.991
tli nnfi rfi
0.987 0.987 0.966
nfi pnfi ifi
0.975 0.704 0.991
rni logl unrestricted.logl
0.991 -2383.240 -2362.486
aic bic ntotal
4804.480 4874.011 287.000
bic2 rmsea rmsea.ci.lower
4813.760 0.046 0.015
rmsea.ci.upper rmsea.pvalue rmr
0.071 0.582 0.016
rmr_nomean srmr srmr_bentler
0.016 0.022 0.022
srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
0.022 0.022 0.022
srmr_mplus srmr_mplus_nomean cn_05
0.022 0.022 269.861
cn_01 gfi agfi
316.578 0.970 0.949
pgfi mfi ecvi
0.561 0.973 0.277 EFFECTIVE FSSB total
alpha 0.9001374 0.8115497 0.9306655
omega 0.9010469 0.8177238 0.9312515
omega2 0.9010469 0.8177238 0.9312515
omega3 0.9018590 0.8225605 0.9298857
avevar 0.6036494 0.6005063 0.6025994$lambda
EFFECT FSSB
EF.CARE1 0 0
EF.CARE2 1 0
EF.LEARN 2 0
EF.INVOLVE 3 0
EF.TRUST 4 0
EF.SUPP 5 0
FSSB.IS 0 0
FSSB.ES 0 6
FSSB.CM 0 7
$theta
EF.CARE1 EF.CARE2 EF.LEA EF.INV EF.TRU EF.SUP FSSB.I FSSB.E
EF.CARE1 9
EF.CARE2 0 10
EF.LEARN 0 0 11
EF.INVOLVE 0 0 0 12
EF.TRUST 0 0 0 0 13
EF.SUPP 0 0 0 0 0 14
FSSB.IS 0 0 0 0 0 0 15
FSSB.ES 0 0 0 0 0 0 0 16
FSSB.C
EF.CARE1
EF.CARE2
EF.LEARN
EF.INVOLVE
EF.TRUST
EF.SUPP
FSSB.IS
FSSB.ES
[ reached getOption("max.print") -- omitted 1 row ]
$psi
EFFECT FSSB
EFFECTIVE 18
FSSB 0 19
$beta
EFFECT FSSB
EFFECTIVE 0 8
FSSB 0 0EFFECTIVE FSSB
0.6062147 0.6016515 Measruement Invarance
Measurement invariance models:
Model 1 : fit.configural
Model 2 : fit.loadings
Model 3 : fit.intercepts
Model 4 : fit.means
Chi Square Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.configural 52 4446.6 4651.5 75.136
fit.loadings 59 4435.5 4614.8 78.077 2.941 7 0.8904
fit.intercepts 66 4430.2 4583.9 86.755 8.678 7 0.2766
fit.means 68 4695.5 4841.8 356.051 269.297 2 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Fit measures:
cfi rmsea cfi.delta rmsea.delta
fit.configural 0.956 0.056 NA NA
fit.loadings 0.964 0.047 0.008 0.008
fit.intercepts 0.961 0.047 0.003 0.001
fit.means 0.453 0.172 0.508 0.125
Measurement invariance models:
Model 1 : fit.configural
Model 2 : fit.loadings
Model 3 : fit.intercepts
Model 4 : fit.means
Chi Square Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.configural 78 4806.1 5113.5 133.26
fit.loadings 92 4795.6 5051.8 150.79 17.528 14 0.22913
fit.intercepts 106 4817.6 5022.6 200.79 50.000 14 6.106e-06 ***
fit.means 110 4822.4 5012.7 213.53 12.736 4 0.01264 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Fit measures:
cfi rmsea cfi.delta rmsea.delta
fit.configural 0.967 0.086 NA NA
fit.loadings 0.965 0.082 0.002 0.004
fit.intercepts 0.944 0.097 0.021 0.015
fit.means 0.939 0.099 0.005 0.003Multigroup SEM
lavaan (0.5-20) converged normally after 62 iterations
Number of observations per group
1 162
2 125
Estimator ML
Minimum Function Test Statistic 75.136
Degrees of freedom 52
P-value (Chi-square) 0.020
Chi-square for each group:
1 41.871
2 33.264
Model test baseline model:
Minimum Function Test Statistic 598.422
Degrees of freedom 72
P-value 0.000
User model versus baseline model:
Comparative Fit Index (CFI) 0.956
Tucker-Lewis Index (TLI) 0.939
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2167.275
Loglikelihood unrestricted model (H1) -2129.707
Number of free parameters 56
Akaike (AIC) 4446.550
Bayesian (BIC) 4651.481
Sample-size adjusted Bayesian (BIC) 4473.899
Root Mean Square Error of Approximation:
RMSEA 0.056
90 Percent Confidence Interval 0.023 0.082
P-value RMSEA <= 0.05 0.349
Standardized Root Mean Square Residual:
SRMR 0.047
Parameter Estimates:
Information Expected
Standard Errors Standard
Group 1 [1]:
Latent Variables:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE =~
EF.CARE1 1.000 0.342 0.659
EF.CARE2 1.082 0.161 6.715 0.000 0.370 0.627
EF.LEARN 0.908 0.172 5.294 0.000 0.311 0.477
EF.INVOLVE 0.962 0.170 5.661 0.000 0.329 0.514
EF.TRUST 1.080 0.151 7.166 0.000 0.370 0.679
EF.SUPP 0.936 0.146 6.423 0.000 0.321 0.595
FSSB =~
FSSB.IS 1.000 0.362 0.707
FSSB.ES 1.000 0.136 7.351 0.000 0.362 0.664
[ reached getOption("max.print") -- omitted 1 row ]
Regressions:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE ~
FSSB 0.927 0.145 6.406 0.000 0.979 0.979
Intercepts:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EF.CARE1 3.432 0.041 84.056 0.000 3.432 6.604
EF.CARE2 3.228 0.046 69.549 0.000 3.228 5.464
EF.LEARN 3.210 0.051 62.662 0.000 3.210 4.923
EF.INVOLVE 3.148 0.050 62.566 0.000 3.148 4.916
EF.TRUST 3.377 0.043 78.927 0.000 3.377 6.201
EF.SUPP 3.259 0.042 76.926 0.000 3.259 6.044
FSSB.IS 3.185 0.040 79.203 0.000 3.185 6.223
FSSB.ES 3.302 0.043 77.077 0.000 3.302 6.056
FSSB.CM 3.247 0.049 65.687 0.000 3.247 5.161
EFFECTIVE 0.000 0.000 0.000
[ reached getOption("max.print") -- omitted 1 row ]
Variances:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EF.CARE1 0.153 0.020 7.646 0.000 0.153 0.566
EF.CARE2 0.212 0.027 7.862 0.000 0.212 0.607
EF.LEARN 0.328 0.039 8.488 0.000 0.328 0.772
EF.INVOLVE 0.302 0.036 8.375 0.000 0.302 0.736
EF.TRUST 0.160 0.021 7.482 0.000 0.160 0.538
EF.SUPP 0.188 0.023 8.042 0.000 0.188 0.646
FSSB.IS 0.131 0.020 6.620 0.000 0.131 0.500
FSSB.ES 0.166 0.023 7.230 0.000 0.166 0.559
FSSB.CM 0.332 0.038 8.628 0.000 0.332 0.838
EFFECTIVE 0.005 0.012 0.393 0.695 0.041 0.041
[ reached getOption("max.print") -- omitted 1 row ]
Group 2 [2]:
Latent Variables:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE =~
EF.CARE1 1.000 0.412 0.608
EF.CARE2 1.091 0.216 5.042 0.000 0.449 0.609
EF.LEARN 0.709 0.190 3.735 0.000 0.292 0.412
EF.INVOLVE 0.732 0.201 3.633 0.000 0.301 0.399
EF.TRUST 1.127 0.207 5.451 0.000 0.464 0.695
EF.SUPP 0.712 0.170 4.184 0.000 0.293 0.473
FSSB =~
FSSB.IS 1.000 0.294 0.432
FSSB.ES 1.032 0.304 3.393 0.001 0.303 0.472
[ reached getOption("max.print") -- omitted 1 row ]
Regressions:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EFFECTIVE ~
FSSB 1.377 0.479 2.875 0.004 0.982 0.982
Intercepts:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EF.CARE1 2.376 0.061 39.226 0.000 2.376 3.508
EF.CARE2 2.128 0.066 32.269 0.000 2.128 2.886
EF.LEARN 2.208 0.063 34.886 0.000 2.208 3.120
EF.INVOLVE 2.080 0.068 30.813 0.000 2.080 2.756
EF.TRUST 2.224 0.060 37.240 0.000 2.224 3.331
EF.SUPP 2.032 0.055 36.711 0.000 2.032 3.284
FSSB.IS 2.048 0.061 33.698 0.000 2.048 3.014
FSSB.ES 2.064 0.057 35.956 0.000 2.064 3.216
FSSB.CM 2.176 0.063 34.508 0.000 2.176 3.087
EFFECTIVE 0.000 0.000 0.000
[ reached getOption("max.print") -- omitted 1 row ]
Variances:
Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
EF.CARE1 0.289 0.044 6.525 0.000 0.289 0.631
EF.CARE2 0.342 0.053 6.517 0.000 0.342 0.629
EF.LEARN 0.416 0.056 7.430 0.000 0.416 0.830
EF.INVOLVE 0.479 0.064 7.466 0.000 0.479 0.841
EF.TRUST 0.231 0.040 5.697 0.000 0.231 0.517
EF.SUPP 0.297 0.041 7.233 0.000 0.297 0.776
FSSB.IS 0.376 0.054 6.963 0.000 0.376 0.813
FSSB.ES 0.320 0.048 6.625 0.000 0.320 0.777
FSSB.CM 0.428 0.059 7.318 0.000 0.428 0.861
EFFECTIVE 0.006 0.045 0.134 0.894 0.036 0.036
[ reached getOption("max.print") -- omitted 1 row ] npar fmin chisq
56.000 0.131 75.136
df pvalue baseline.chisq
52.000 0.020 598.422
baseline.df baseline.pvalue cfi
72.000 0.000 0.956
tli nnfi rfi
0.939 0.939 0.826
nfi pnfi ifi
0.874 0.632 0.958
rni logl unrestricted.logl
0.956 -2167.275 -2129.707
aic bic ntotal
4446.550 4651.481 287.000
bic2 rmsea rmsea.ci.lower
4473.899 0.056 0.023
rmsea.ci.upper rmsea.pvalue rmr
0.082 0.349 0.019
rmr_nomean srmr srmr_bentler
0.021 0.047 0.047
srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
0.052 0.047 0.052
srmr_mplus srmr_mplus_nomean cn_05
0.047 0.052 267.741
cn_01 gfi agfi
301.292 0.995 0.991
pgfi mfi
0.479 0.960 gammaHat adjGammaHat baseline.rmsea aic.smallN bic.priorN
0.9823409 0.9694361 0.2257224 4362.3066442 4651.6759094
hqc sic
4528.6833520 4654.4508879 LCA - Excel
SEM - GRAPH
