Ofek - vigilantism survey
Data preparation
Item
Q6_4was reversed.Item
Q12_3was reversed.Following the recommendations of … we removed subjects who hurried through the questionnaire, completing it in less than a third of the average completion time of the entire sample (that is, less than 53 seconds).
We remain with \(N = 468\) observations.
Survey data
Confirmatory Factor Analysis (CFA)
First, we assessed the reliability of the measurement model for the latent variables using confirmatory factor analysis.
The table below details the reliability measures for the latent variables.
pj, trust, and efficacy show good \(\alpha\) values.
vig is acceptable, just over the common threshold of 0.75.
| measure | pj | trust | eff | vig |
|---|---|---|---|---|
| alpha | 0.9445 | 0.9277 | 0.8562 | 0.7734 |
| omega | 0.9446 | 0.9276 | 0.8559 | 0.7898 |
| omega2 | 0.9446 | 0.9276 | 0.8559 | 0.7898 |
| omega3 | 0.9446 | 0.9275 | 0.8555 | 0.7908 |
| avevar | 0.7089 | 0.8103 | 0.6645 | 0.5656 |
Measurement invariance
Measurement invariance of past victims and non-victims was assessed by fitting a multigroup SEM model.
There were 303 non-victims and 121 victims.
The model fitted the data well, CFI = .967, TLI = .956, RMSEA = .059.
All item loadings were significant in both groups.
We conclude that measurement invariance holds; the grouping variable will be treated as a manifest variable (directly observed, without error).
Surprisingly in both groups, higher police efficacy was associated with MORE self-vigilantism.
The output below details all the estimates for this model.
| 0 | 1 | |||||||
| Estimate | Std. Err. | z | p | Estimate | Std. Err. | z | p | |
| Factor Loadings | ||||||||
| pj | ||||||||
| q5.1 | 1.00+ | 1.00+ | ||||||
| q5.2 | 0.94 | 0.04 | 25.48 | .000 | 1.01 | 0.09 | 11.34 | .000 |
| q5.3 | 0.98 | 0.04 | 24.23 | .000 | 1.03 | 0.11 | 9.78 | .000 |
| q5.4 | 0.98 | 0.05 | 21.37 | .000 | 1.06 | 0.10 | 10.98 | .000 |
| q5.5 | 0.91 | 0.04 | 21.65 | .000 | 1.04 | 0.10 | 10.02 | .000 |
| q5.6 | 1.00 | 0.03 | 29.85 | .000 | 1.03 | 0.09 | 11.17 | .000 |
| q5.7 | 0.95 | 0.04 | 25.47 | .000 | 1.12 | 0.10 | 11.49 | .000 |
| dj | ||||||||
| q8.1 | 1.00+ | 1.00+ | ||||||
| trust | ||||||||
| q7.1 | 1.00+ | 1.00+ | ||||||
| q7.2 | 1.00 | 0.03 | 30.45 | .000 | 1.01 | 0.06 | 16.26 | .000 |
| q7.3 | 0.98 | 0.04 | 22.63 | .000 | 1.02 | 0.05 | 18.94 | .000 |
| eff | ||||||||
| q6.1 | 1.00+ | 1.00+ | ||||||
| q6.2 | 1.06 | 0.06 | 18.15 | .000 | 1.01 | 0.09 | 10.84 | .000 |
| q6.3 | 1.07 | 0.06 | 17.77 | .000 | 0.89 | 0.10 | 9.15 | .000 |
| vig | ||||||||
| q9.1 | 1.00+ | 1.00+ | ||||||
| q9.2 | 1.32 | 0.11 | 11.72 | .000 | 1.52 | 0.49 | 3.12 | .002 |
| q9.3 | 0.85 | 0.10 | 8.53 | .000 | 0.79 | 0.14 | 5.54 | .000 |
| Regression Slopes | ||||||||
| vig | ||||||||
| pj | -0.32 | 0.14 | -2.33 | .020 | -0.32 | 0.14 | -2.33 | .020 |
| dj | 0.01 | 0.08 | 0.17 | .868 | 0.01 | 0.08 | 0.17 | .868 |
| trust | -0.22 | 0.12 | -1.88 | .060 | -0.22 | 0.12 | -1.88 | .060 |
| eff | 0.52 | 0.18 | 2.89 | .004 | 0.52 | 0.18 | 2.89 | .004 |
| Intercepts | ||||||||
| q5.1 | 2.24 | 0.06 | 36.73 | .000 | 2.02 | 0.10 | 19.72 | .000 |
| q5.2 | 2.37 | 0.06 | 41.84 | .000 | 1.94 | 0.09 | 21.77 | .000 |
| q5.3 | 2.48 | 0.06 | 43.27 | .000 | 2.13 | 0.09 | 23.53 | .000 |
| q5.4 | 2.84 | 0.06 | 47.38 | .000 | 2.58 | 0.10 | 26.65 | .000 |
| q5.5 | 2.43 | 0.06 | 42.27 | .000 | 2.07 | 0.10 | 20.87 | .000 |
| q5.6 | 2.34 | 0.06 | 40.19 | .000 | 2.06 | 0.10 | 21.41 | .000 |
| q5.7 | 2.40 | 0.06 | 43.32 | .000 | 2.13 | 0.09 | 22.65 | .000 |
| q8.1 | 2.54 | 0.06 | 46.00 | .000 | 2.19 | 0.10 | 22.28 | .000 |
| q7.1 | 2.72 | 0.06 | 44.69 | .000 | 2.28 | 0.09 | 23.98 | .000 |
| q7.2 | 2.75 | 0.06 | 44.60 | .000 | 2.40 | 0.10 | 23.49 | .000 |
| q7.3 | 2.61 | 0.06 | 42.22 | .000 | 2.19 | 0.10 | 23.05 | .000 |
| q6.1 | 2.45 | 0.06 | 44.38 | .000 | 2.06 | 0.10 | 21.41 | .000 |
| q6.2 | 2.40 | 0.06 | 41.18 | .000 | 2.23 | 0.10 | 22.47 | .000 |
| q6.3 | 2.55 | 0.06 | 44.04 | .000 | 2.25 | 0.09 | 23.84 | .000 |
| q9.1 | 2.24 | 0.07 | 33.09 | .000 | 2.25 | 0.11 | 20.83 | .000 |
| q9.2 | 1.98 | 0.06 | 31.16 | .000 | 2.02 | 0.10 | 20.10 | .000 |
| q9.3 | 1.62 | 0.06 | 29.48 | .000 | 1.67 | 0.09 | 18.53 | .000 |
| Residual Variances | ||||||||
| q5.1 | 0.38 | 0.04 | 10.39 | .000 | 0.59 | 0.12 | 5.08 | .000 |
| q5.2 | 0.31 | 0.04 | 7.34 | .000 | 0.27 | 0.06 | 4.80 | .000 |
| q5.3 | 0.28 | 0.03 | 8.41 | .000 | 0.27 | 0.04 | 6.23 | .000 |
| q5.4 | 0.37 | 0.04 | 9.61 | .000 | 0.38 | 0.06 | 6.77 | .000 |
| q5.5 | 0.39 | 0.04 | 9.65 | .000 | 0.48 | 0.09 | 5.11 | .000 |
| q5.6 | 0.27 | 0.03 | 8.83 | .000 | 0.41 | 0.07 | 6.26 | .000 |
| q5.7 | 0.24 | 0.03 | 8.89 | .000 | 0.21 | 0.04 | 5.22 | .000 |
| q8.1 | 0.00+ | 0.00+ | ||||||
| q7.1 | 0.20 | 0.04 | 4.64 | .000 | 0.20 | 0.04 | 4.80 | .000 |
| q7.2 | 0.23 | 0.04 | 6.30 | .000 | 0.37 | 0.12 | 3.03 | .002 |
| q7.3 | 0.29 | 0.05 | 5.49 | .000 | 0.17 | 0.04 | 3.85 | .000 |
| q6.1 | 0.35 | 0.04 | 9.91 | .000 | 0.35 | 0.10 | 3.57 | .000 |
| q6.2 | 0.39 | 0.05 | 8.44 | .000 | 0.39 | 0.12 | 3.16 | .002 |
| q6.3 | 0.37 | 0.05 | 7.39 | .000 | 0.46 | 0.09 | 5.35 | .000 |
| q9.1 | 0.89 | 0.11 | 8.21 | .000 | 0.93 | 0.21 | 4.42 | .000 |
| q9.2 | 0.21 | 0.09 | 2.43 | .015 | 0.11 | 0.29 | 0.37 | .711 |
| q9.3 | 0.54 | 0.07 | 7.52 | .000 | 0.72 | 0.14 | 5.08 | .000 |
| Latent Intercepts | ||||||||
| pj | 0.00+ | 0.00+ | ||||||
| dj | 0.00+ | 0.00+ | ||||||
| trust | 0.00+ | 0.00+ | ||||||
| eff | 0.00+ | 0.00+ | ||||||
| vig | 0.00+ | 0.00+ | ||||||
| Latent Variances | ||||||||
| pj | 0.87 | 0.07 | 11.74 | .000 | 0.71 | 0.14 | 5.03 | .000 |
| dj | 1.02 | 0.07 | 15.02 | .000 | 1.22 | 0.13 | 9.53 | .000 |
| trust | 1.04 | 0.08 | 12.65 | .000 | 0.94 | 0.11 | 8.61 | .000 |
| eff | 0.66 | 0.07 | 9.41 | .000 | 0.84 | 0.13 | 6.57 | .000 |
| vig | 0.61 | 0.10 | 6.14 | .000 | 0.45 | 0.24 | 1.87 | .061 |
| Latent Covariances | ||||||||
| pj w/dj | 0.79 | 0.06 | 12.14 | .000 | 0.67 | 0.11 | 6.31 | .000 |
| pj w/trust | 0.80 | 0.06 | 12.34 | .000 | 0.68 | 0.10 | 7.10 | .000 |
| pj w/eff | 0.68 | 0.06 | 10.92 | .000 | 0.68 | 0.10 | 6.52 | .000 |
| dj w/trust | 0.87 | 0.07 | 12.89 | .000 | 0.87 | 0.11 | 8.16 | .000 |
| dj w/eff | 0.69 | 0.06 | 10.97 | .000 | 0.79 | 0.12 | 6.81 | .000 |
| trust w/eff | 0.67 | 0.06 | 10.44 | .000 | 0.77 | 0.10 | 7.33 | .000 |
| Fit Indices | ||||||||
| χ2 | 454.45 | |||||||
| CFI | 0.96 | |||||||
| TLI | 0.96 | |||||||
| RMSEA | 0.07 | |||||||
| Scaled χ2 | 376.35(224) | .000 | ||||||
| +Fixed parameter | ||||||||
SEM fitting
- As the measurement invariance assumption is plausible, we proceed to fitting a SEM model to the entire dataset.
lavaan 0.6.15 ended normally after 66 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Used Total
Number of observations 461 538
Model Test User Model:
Standard Scaled
Test Statistic 283.767 224.155
Degrees of freedom 126 126
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.266
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 6678.620 4901.647
Degrees of freedom 153 153
P-value 0.000 0.000
Scaling correction factor 1.363
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.976 0.979
Tucker-Lewis Index (TLI) 0.971 0.975
Robust Comparative Fit Index (CFI) 0.981
Robust Tucker-Lewis Index (TLI) 0.977
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -8597.650 -8597.650
Scaling correction factor 1.378
for the MLR correction
Loglikelihood unrestricted model (H1) -8455.767 -8455.767
Scaling correction factor 1.295
for the MLR correction
Akaike (AIC) 17283.301 17283.301
Bayesian (BIC) 17465.170 17465.170
Sample-size adjusted Bayesian (SABIC) 17325.527 17325.527
Root Mean Square Error of Approximation:
RMSEA 0.052 0.041
90 Percent confidence interval - lower 0.044 0.033
90 Percent confidence interval - upper 0.060 0.049
P-value H_0: RMSEA <= 0.050 0.323 0.972
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.046
90 Percent confidence interval - lower 0.036
90 Percent confidence interval - upper 0.056
P-value H_0: Robust RMSEA <= 0.050 0.726
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.047 0.047
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
pj =~
q5_1 1.000
q5_2 0.974 0.036 27.073 0.000
q5_3 1.001 0.041 24.562 0.000
q5_4 1.002 0.043 23.269 0.000
q5_5 0.951 0.040 23.527 0.000
q5_6 1.014 0.035 29.385 0.000
q5_7 0.996 0.037 27.075 0.000
dj =~
q8_1 1.000
trust =~
q7_1 1.000
q7_2 0.996 0.029 34.655 0.000
q7_3 0.985 0.034 28.973 0.000
eff =~
q6_1 1.000
q6_2 1.023 0.048 21.286 0.000
q6_3 1.009 0.050 20.300 0.000
vig =~
q9_1 1.000
q9_2 1.342 0.104 12.924 0.000
q9_3 0.837 0.082 10.163 0.000
Regressions:
Estimate Std.Err z-value P(>|z|)
vig ~
pj (h2) -0.306 0.147 -2.076 0.038
dj (h4) -0.004 0.076 -0.049 0.961
trust (h1) -0.207 0.107 -1.943 0.052
eff (h3) 0.501 0.178 2.817 0.005
q1 (h5) 0.001 0.087 0.012 0.990
Covariances:
Estimate Std.Err z-value P(>|z|)
pj ~~
dj 0.772 0.056 13.726 0.000
trust 0.788 0.054 14.587 0.000
eff 0.700 0.053 13.103 0.000
dj ~~
trust 0.899 0.057 15.762 0.000
eff 0.742 0.056 13.318 0.000
trust ~~
eff 0.730 0.055 13.277 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.q5_1 0.442 0.044 10.142 0.000
.q5_2 0.302 0.035 8.689 0.000
.q5_3 0.276 0.027 10.260 0.000
.q5_4 0.372 0.032 11.804 0.000
.q5_5 0.413 0.039 10.640 0.000
.q5_6 0.307 0.029 10.746 0.000
.q5_7 0.239 0.023 10.492 0.000
.q8_1 0.000
.q7_1 0.202 0.033 6.037 0.000
.q7_2 0.266 0.042 6.286 0.000
.q7_3 0.260 0.040 6.446 0.000
.q6_1 0.356 0.038 9.331 0.000
.q6_2 0.396 0.049 8.155 0.000
.q6_3 0.391 0.044 8.981 0.000
.q9_1 0.890 0.093 9.576 0.000
.q9_2 0.207 0.078 2.652 0.008
.q9_3 0.583 0.062 9.410 0.000
pj 0.832 0.069 12.141 0.000
dj 1.102 0.060 18.406 0.000
trust 1.049 0.067 15.657 0.000
eff 0.738 0.063 11.724 0.000
.vig 0.582 0.085 6.866 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
TRUST -0.207 0.107 -1.943 0.052
PJ -0.306 0.147 -2.076 0.038
EFFICACY 0.501 0.178 2.817 0.005
DJ -0.004 0.076 -0.049 0.961
Victim 0.001 0.087 0.012 0.990| Model | ||||
| Estimate | Std. Err. | z | p | |
| Regression Slopes | ||||
| vig | ||||
| pj | -0.17 | 0.10 | -1.81 | .071 |
| dj | 0.02 | 0.07 | 0.29 | .770 |
| trust | -0.15 | 0.08 | -2.04 | .041 |
| eff | 0.26 | 0.07 | 3.55 | .000 |
| q1 | 0.00 | 0.10 | 0.04 | .967 |
| Residual Variances | ||||
| vig | 0.85 | 0.06 | 14.91 | .000 |
| pj | 0.86+ | |||
| dj | 1.10+ | |||
| trust | 1.11+ | |||
| eff | 0.88+ | |||
| q1 | 0.20+ | |||
| Residual Covariances | ||||
| pj w/dj | 0.76+ | |||
| pj w/trust | 0.77+ | |||
| pj w/eff | 0.70+ | |||
| pj w/q1 | -0.06+ | |||
| dj w/trust | 0.89+ | |||
| dj w/eff | 0.75+ | |||
| dj w/q1 | -0.07+ | |||
| trust w/eff | 0.73+ | |||
| trust w/q1 | -0.08+ | |||
| eff w/q1 | -0.06+ | |||
| Fit Indices | ||||
| χ2 | 0.00(0) | |||
| CFI | 1.00 | |||
| TLI | 1.00 | |||
| RMSEA | 0.00 | |||
| +Fixed parameter | ||||
npar fmin chisq
6.000 0.000 0.000
df pvalue baseline.chisq
0.000 NA 15.419
baseline.df baseline.pvalue cfi
5.000 0.009 1.000
tli nnfi rfi
1.000 1.000 1.000
nfi pnfi ifi
1.000 0.000 1.000
rni logl unrestricted.logl
1.000 -623.190 -623.190
aic bic ntotal
1258.381 1283.233 465.000
bic2 rmsea rmsea.ci.lower
1264.190 0.000 0.000
rmsea.ci.upper rmsea.ci.level rmsea.pvalue
0.000 0.900 NA
rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
0.050 NA 0.080
rmr rmr_nomean srmr
0.000 0.000 0.000
srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean
0.000 0.000 0.000
cn_05 cn_01 gfi
NA NA 1.000
agfi pgfi mfi
1.000 0.000 1.000
ecvi
0.026 | Model | ||||
| Estimate | Std. Err. | z | p | |
| Regression Slopes | ||||
| vig | ||||
| pj | -0.31 | 0.15 | -2.08 | .038 |
| dj | -0.00 | 0.08 | -0.05 | .961 |
| trust | -0.21 | 0.11 | -1.94 | .052 |
| eff | 0.50 | 0.18 | 2.82 | .005 |
| q1 | 0.00 | 0.09 | 0.01 | .990 |
| Fit Indices | ||||
| χ2 | 283.77 | |||
| CFI | 0.98 | |||
| TLI | 0.97 | |||
| RMSEA | 0.05 | |||
| Scaled χ2 | 224.16(126) | .000 | ||
| +Fixed parameter | ||||
[1] 2 2 4 2 4 4 4 3 4 4 4 2 3 3 4 3 3 1 2 4 3 4 2 1 2
[26] 3 1 1 1 4 2 2 1 3 1 2 1 5 1 3 1 2 3 3 1 1 2 4 2 5
[51] 3 3 1 3 3 4 3 4 3 4 3 4 1 4 3 1 4 2 2 4 1 4 4 NA 1
[76] 3 1 2 2 3 3 1 4 4 3 1 4 4 4 3 3 3 3 3 3 3 1 2 3 2
[101] 4 1 4 4 4 3 2 1 4 5 3 4 4 5 3 2 1 4 3 4 3 4 3 4 4
[126] 1 4 4 2 2 4 4 2 3 5 1 2 2 4 4 1 4 2 2 4 1 5 2 1 1
[151] 2 1 3 3 3 1 2 2 2 2 1 1 3 4 4 2 3 4 1 2 1 4 4 4 1
[176] 4 3 4 3 1 3 1 5 4 NA 1 1 4 1 4 1 2 1 1 4 2 5 4 1 3
[201] 4 2 1 1 1 4 4 1 2 4 2 4 2 1 4 4 5 4 3 3 3 3 1 5 4
[226] 1 1 2 1 4 3 2 3 4 5 2 4 3 4 2 3 4 1 3 1 1 4 2 4 5
[251] 4 1 2 4 1 4 4 2 1 4 3 2 3 4 1 2 2 2 1 1 4 2 2 5 1
[276] 1 3 1 1 2 3 1 1 1 2 3 4 4 2 1 1 4 4 2 3 3 5 1 2 3
[301] 2 1 2 3 3 3 3 1 1 1 3 1 2 3 4 4 3 4 2 4 1 3 1 5 3
[326] 4 3 1 3 2 NA 4 2 5 3 2 NA 4 2 4 2 3 4 2 3 3 4 4 1 3
[351] 4 2 4 4 3 1 1 5 4 2 1 5 3 1 3 4 4 4 1 1 3 3 4 1 1
[376] 1 3 4 4 4 3 3 4 3 1 3 1 3 3 4 4 2 4 3 2 4 3 3 5 1
[401] 3 1 NA 3 1 4 4 5 NA 2 4 2 NA 1 1 2 3 1 1 3 3 5 4 1 3
[426] 3 2 4 4 3 2 3 3 4 3 4 1 3 4 1 4 1 1 1 4 2 1 NA 1 5
[451] 1 1 3 4 1 1 2 2 4 2 4 2 2 1 4 3 2 2 5 3 4 1 5 NA 3
[476] 2 2 3 1 3 NA NA NA NA NA NA 3 2 4 2 3 NA 2 2 4 1 2 4 4 3
[501] 3 2 2 NA NA 4 NA NA NA NA 4 4 4 4 NA 4 NA NA 4 3 3 4 4 NA NA
[526] NA NA NA 5 NA NA NA NA 3 NA NA 3 NA NA NA NA 3 2 NA 1 4 5 NA 4 NA
[551] NAExtra models
lavaan 0.6.15 ended normally after 47 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 31
Used Total
Number of observations 439 538
Model Test User Model:
Standard Scaled
Test Statistic 523.683 403.307
Degrees of freedom 74 74
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.298
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 4468.699 3164.864
Degrees of freedom 91 91
P-value 0.000 0.000
Scaling correction factor 1.412
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.897 0.893
Tucker-Lewis Index (TLI) 0.874 0.868
Robust Comparative Fit Index (CFI) 0.901
Robust Tucker-Lewis Index (TLI) 0.879
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -8096.075 -8096.075
Scaling correction factor 1.261
for the MLR correction
Loglikelihood unrestricted model (H1) -7834.234 -7834.234
Scaling correction factor 1.287
for the MLR correction
Akaike (AIC) 16254.150 16254.150
Bayesian (BIC) 16380.769 16380.769
Sample-size adjusted Bayesian (SABIC) 16282.391 16282.391
Root Mean Square Error of Approximation:
RMSEA 0.118 0.101
90 Percent confidence interval - lower 0.108 0.092
90 Percent confidence interval - upper 0.127 0.109
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.115
90 Percent confidence interval - lower 0.104
90 Percent confidence interval - upper 0.126
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.062 0.062
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
pj =~
q11_1 1.000
q11_2 1.034 0.065 15.904 0.000
q11_3 1.284 0.077 16.624 0.000
q11_4 1.287 0.077 16.720 0.000
q11_5 1.209 0.080 15.086 0.000
q11_6 1.425 0.091 15.685 0.000
q11_7 1.447 0.092 15.750 0.000
sa =~
q12_1 1.000
q12_2 1.073 0.046 23.433 0.000
q12_3 0.152 0.078 1.941 0.052
vig =~
q15_1 1.000
q15_2 1.043 0.064 16.248 0.000
q15_3 0.979 0.081 12.104 0.000
q15_4 1.365 0.091 14.937 0.000
Regressions:
Estimate Std.Err z-value P(>|z|)
vig ~
pj -0.293 0.184 -1.590 0.112
sa 0.175 0.159 1.098 0.272
Covariances:
Estimate Std.Err z-value P(>|z|)
pj ~~
sa 0.721 0.064 11.281 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.q11_1 0.874 0.076 11.523 0.000
.q11_2 0.498 0.046 10.919 0.000
.q11_3 0.391 0.051 7.715 0.000
.q11_4 0.309 0.041 7.505 0.000
.q11_5 0.475 0.050 9.533 0.000
.q11_6 0.438 0.060 7.345 0.000
.q11_7 0.482 0.060 7.993 0.000
.q12_1 0.423 0.048 8.754 0.000
.q12_2 0.337 0.071 4.752 0.000
.q12_3 1.777 0.081 22.056 0.000
.q15_1 0.735 0.077 9.524 0.000
.q15_2 0.910 0.088 10.320 0.000
.q15_3 0.658 0.076 8.685 0.000
.q15_4 0.214 0.058 3.722 0.000
pj 0.674 0.083 8.144 0.000
sa 0.967 0.080 12.054 0.000
.vig 0.770 0.098 7.884 0.000 pj sa
pj 1.0000000 0.6744761
sa 0.6744761 1.0000000