描述 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
sex | gra | occ | dep | home | |||||||
N | 300 | 300 | 300 | 300 | 300 | ||||||
Missing | 0 | 0 | 0 | 0 | 0 | ||||||
Mean | 1.37 | 2.83 | 3.96 | 1.84 | 2.71 | ||||||
Median | 1.00 | 3.00 | 4.00 | 2.00 | 3.00 | ||||||
Standard deviation | 0.483 | 0.953 | 1.52 | 0.566 | 1.18 | ||||||
Minimum | 1 | 1 | 1 | 1 | 1 | ||||||
Maximum | 2 | 5 | 6 | 3 | 5 | ||||||
sex频数 | |||||||
---|---|---|---|---|---|---|---|
sex | 计数 | 合计% | 累计% | ||||
1 | 190 | 63.3 % | 63.3 % | ||||
2 | 110 | 36.7 % | 100.0 % | ||||
gra频数 | |||||||
---|---|---|---|---|---|---|---|
gra | 计数 | 合计% | 累计% | ||||
1 | 10 | 3.3 % | 3.3 % | ||||
2 | 118 | 39.3 % | 42.7 % | ||||
3 | 101 | 33.7 % | 76.3 % | ||||
4 | 54 | 18.0 % | 94.3 % | ||||
5 | 17 | 5.7 % | 100.0 % | ||||
occ频数 | |||||||
---|---|---|---|---|---|---|---|
occ | 计数 | 合计% | 累计% | ||||
1 | 30 | 10.0 % | 10.0 % | ||||
2 | 30 | 10.0 % | 20.0 % | ||||
3 | 34 | 11.3 % | 31.3 % | ||||
4 | 81 | 27.0 % | 58.3 % | ||||
5 | 77 | 25.7 % | 84.0 % | ||||
6 | 48 | 16.0 % | 100.0 % | ||||
dep频数 | |||||||
---|---|---|---|---|---|---|---|
dep | 计数 | 合计% | 累计% | ||||
1 | 75 | 25.0 % | 25.0 % | ||||
2 | 197 | 65.7 % | 90.7 % | ||||
3 | 28 | 9.3 % | 100.0 % | ||||
home频数 | |||||||
---|---|---|---|---|---|---|---|
home | 计数 | 合计% | 累计% | ||||
1 | 62 | 20.7 % | 20.7 % | ||||
2 | 64 | 21.3 % | 42.0 % | ||||
3 | 88 | 29.3 % | 71.3 % | ||||
4 | 71 | 23.7 % | 95.0 % | ||||
5 | 15 | 5.0 % | 100.0 % | ||||
Descriptives | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Skewness | Kurtosis | Shapiro-Wilk | |||||||||||||||
Mean | SD | Skewness | SE | Kurtosis | SE | W | p | ||||||||||
STD | 4.13 | 0.529 | -0.267 | 0.141 | -0.577 | 0.281 | 0.952 | < .001 | |||||||||
EVA | 3.99 | 0.593 | -0.658 | 0.141 | 1.853 | 0.281 | 0.950 | < .001 | |||||||||
TEA | 4.01 | 0.609 | -0.857 | 0.141 | 2.491 | 0.281 | 0.933 | < .001 | |||||||||
PRO | 3.94 | 0.520 | -0.487 | 0.141 | 1.095 | 0.281 | 0.948 | < .001 | |||||||||
ENV | 3.90 | 0.592 | -0.512 | 0.141 | 1.596 | 0.281 | 0.957 | < .001 | |||||||||
Models Info | |||||
---|---|---|---|---|---|
Estimation Method | ML | . | |||
Optimization Method | NLMINB | ||||
Number of observations | 300 | ||||
Free parameters | 14 | ||||
Standard errors | Standard | ||||
Scaled test | None | ||||
Converged | TRUE | ||||
Iterations | 2 | ||||
Model | SKL~EVA+PRO+ENV | ||||
PRO~STD | |||||
ENV~TEA | |||||
[3] [4] |
Model tests | |||||||
---|---|---|---|---|---|---|---|
Label | X² | df | p | ||||
User Model | 206 | 7 | < .001 | ||||
Baseline Model | 693 | 12 | < .001 | ||||
Fit indices | |||||||||
---|---|---|---|---|---|---|---|---|---|
95% Confidence Intervals | |||||||||
SRMR | RMSEA | Lower | Upper | RMSEA p | |||||
0.168 | 0.308 | 0.272 | 0.345 | < .001 | |||||
User model versus baseline model | |||
---|---|---|---|
Model | |||
Comparative Fit Index (CFI) | 0.708 | ||
Tucker-Lewis Index (TLI) | 0.499 | ||
Bentler-Bonett Non-normed Fit Index (NNFI) | 0.499 | ||
Relative Noncentrality Index (RNI) | 0.708 | ||
Bentler-Bonett Normed Fit Index (NFI) | 0.703 | ||
Bollen's Relative Fit Index (RFI) | 0.490 | ||
Bollen's Incremental Fit Index (IFI) | 0.710 | ||
Parsimony Normed Fit Index (PNFI) | 0.410 | ||
R² | |||
---|---|---|---|
Variable | R² | ||
SKL | 0.458 | ||
PRO | 0.297 | ||
ENV | 0.398 | ||
ENV | . | ||
STD | . | ||
TEA | . | ||
Parameters estimates | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dep | Pred | Estimate | SE | β | z | p | |||||||
SKL | EVA | 0.158 | 0.0425 | 0.183 | 3.72 | < .001 | |||||||
SKL | PRO | 0.239 | 0.0447 | 0.242 | 5.35 | < .001 | |||||||
SKL | ENV | 0.400 | 0.0410 | 0.462 | 9.75 | < .001 | |||||||
PRO | STD | 0.536 | 0.0476 | 0.545 | 11.26 | < .001 | |||||||
ENV | TEA | 0.613 | 0.0436 | 0.631 | 14.08 | < .001 | |||||||
Variances and Covariances | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variable 1 | Variable 2 | Estimate | SE | β | z | p | |||||||
SKL | SKL | 0.142 | 0.0116 | 0.542 | 12.25 | < .001 | |||||||
PRO | PRO | 0.189 | 0.0155 | 0.703 | 12.25 | < .001 | |||||||
ENV | ENV | 0.211 | 0.0172 | 0.602 | 12.25 | < .001 | |||||||
EVA | EVA | 0.351 | 0.0286 | 1.000 | 12.25 | < .001 | |||||||
EVA | STD | 0.194 | 0.0212 | 0.620 | 9.13 | < .001 | |||||||
EVA | TEA | 0.249 | 0.0253 | 0.690 | 9.84 | < .001 | |||||||
STD | STD | 0.279 | 0.0228 | 1.000 | 12.25 | < .001 | |||||||
STD | TEA | 0.202 | 0.0219 | 0.629 | 9.23 | < .001 | |||||||
TEA | TEA | 0.370 | 0.0302 | 1.000 | 12.25 | < .001 | |||||||
Defined parameters | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Label | Description | Parameter | Estimate | SE | β | z | p | ||||||||
IE1 | STD ⇒ PRO ⇒ SKL | p4*p2 | 0.128 | 0.026 | 0.132 | 4.829 | < .001 | ||||||||
IE2 | TEA ⇒ ENV ⇒ SKL | p5*p3 | 0.245 | 0.031 | 0.291 | 8.016 | < .001 | ||||||||
Mardia's coefficients | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Coefficient | z | χ² | df | p | |||||||
Skewness | 7.81 | 390 | 56 | < .001 | |||||||
Kurtosis | 71.27 | 20.6 | < .001 | ||||||||
[5] |
描述 | |
---|---|
个案数 | |
缺失 | |
均值 | |
中位数 | |
标准差 | |
最小值 | |
最大值 | |
描述 | |
---|---|
个案数 | |
缺失 | |
均值 | |
中位数 | |
标准差 | |
最小值 | |
最大值 | |
Models Info | |||||
---|---|---|---|---|---|
Estimation Method | ML | . | |||
Optimization Method | NLMINB | ||||
Number of observations | 300 | ||||
Free parameters | 22 | ||||
Standard errors | Standard | ||||
Scaled test | None | ||||
Converged | TRUE | ||||
Iterations | 67 | ||||
Model | SKL~STD+PRO+EVA+TEA+ENV | ||||
PRO~STD | |||||
ENV~TEA | |||||
[3] [4] |
Model tests | |||||||
---|---|---|---|---|---|---|---|
Label | X² | df | p | ||||
User Model | 205 | 5 | < .001 | ||||
Baseline Model | 693 | 12 | < .001 | ||||
Fit indices | |||||||||
---|---|---|---|---|---|---|---|---|---|
95% Confidence Intervals | |||||||||
SRMR | RMSEA | Lower | Upper | RMSEA p | |||||
0.147 | 0.366 | 0.324 | 0.409 | < .001 | |||||
User model versus baseline model | |||
---|---|---|---|
Model | |||
Comparative Fit Index (CFI) | 0.706 | ||
Tucker-Lewis Index (TLI) | 0.293 | ||
Bentler-Bonett Non-normed Fit Index (NNFI) | 0.293 | ||
Relative Noncentrality Index (RNI) | 0.706 | ||
Bentler-Bonett Normed Fit Index (NFI) | 0.703 | ||
Bollen's Relative Fit Index (RFI) | 0.288 | ||
Bollen's Incremental Fit Index (IFI) | 0.709 | ||
Parsimony Normed Fit Index (PNFI) | 0.293 | ||
Additional fit indices | |||
---|---|---|---|
Model | |||
Hoelter Critical N (CN), α=0.05 | 17.169 | ||
Hoelter Critical N (CN), α=0.01 | 23.034 | ||
Goodness of Fit Index (GFI) | 0.995 | ||
Adjusted Goodness of Fit Index (AGFI) | 0.973 | ||
Parsimony Goodness of Fit Index (PGFI) | 0.184 | ||
McDonald Fit Index (MFI) | 0.716 | ||
Expected Cross-Validation Index (ECVI) | 0.831 | ||
Loglikelihood user model (H0) | -1089.834 | ||
Loglikelihood unrestricted model (H1) | -987.130 | ||
Akaike (AIC) | 2223.668 | ||
Bayesian (BIC) | 2305.151 | ||
Sample-size adjusted Bayesian (SABIC) | 2235.380 | ||
R² | |||
---|---|---|---|
Variable | R² | ||
SKL | 0.463 | ||
PRO | 0.297 | ||
ENV | 0.398 | ||
EVA | . | ||
TEA | . | ||
ENV | . | ||
Parameters estimates | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dep | Pred | Estimate | SE | β | z | p | |||||||
SKL | STD | 0.0254 | 0.0621 | 0.0261 | 0.408 | 0.683 | |||||||
SKL | PRO | 0.2258 | 0.0499 | 0.2282 | 4.524 | < .001 | |||||||
SKL | EVA | 0.1383 | 0.0537 | 0.1593 | 2.573 | 0.010 | |||||||
SKL | TEA | 0.0219 | 0.0603 | 0.0259 | 0.363 | 0.716 | |||||||
SKL | ENV | 0.3942 | 0.0474 | 0.4537 | 8.323 | < .001 | |||||||
PRO | STD | 0.5357 | 0.0476 | 0.5450 | 11.258 | < .001 | |||||||
ENV | TEA | 0.6133 | 0.0436 | 0.6308 | 14.081 | < .001 | |||||||
Variances and Covariances | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variable 1 | Variable 2 | Estimate | SE | β | z | p | |||||||
SKL | SKL | 0.142 | 0.0116 | 0.537 | 12.25 | < .001 | |||||||
PRO | PRO | 0.189 | 0.0155 | 0.703 | 12.25 | < .001 | |||||||
ENV | ENV | 0.211 | 0.0172 | 0.602 | 12.25 | < .001 | |||||||
STD | STD | 0.279 | 0.0228 | 1.000 | 12.25 | < .001 | |||||||
STD | EVA | 0.194 | 0.0212 | 0.620 | 9.13 | < .001 | |||||||
STD | TEA | 0.202 | 0.0219 | 0.629 | 9.23 | < .001 | |||||||
EVA | EVA | 0.351 | 0.0286 | 1.000 | 12.25 | < .001 | |||||||
EVA | TEA | 0.249 | 0.0253 | 0.690 | 9.84 | < .001 | |||||||
TEA | TEA | 0.370 | 0.0302 | 1.000 | 12.25 | < .001 | |||||||
Intercepts | |||||||||
---|---|---|---|---|---|---|---|---|---|
Variable | Intercept | SE | z | p | |||||
SKL | 0.701 | 0.211 | 3.316 | < .001 | |||||
PRO | 1.725 | 0.198 | 8.718 | < .001 | |||||
ENV | 1.440 | 0.177 | 8.147 | < .001 | |||||
STD | 4.125 | 0.030 | 135.285 | < .001 | |||||
EVA | 3.990 | 0.034 | 116.699 | < .001 | |||||
TEA | 4.013 | 0.035 | 114.285 | < .001 | |||||
Defined parameters | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Label | Description | Parameter | Estimate | SE | β | z | p | ||||||||
IE1 | STD ⇒ PRO ⇒ SKL | p6*p2 | 0.121 | 0.029 | 0.124 | 4.198 | < .001 | ||||||||
IE2 | TEA ⇒ ENV ⇒ SKL | p7*p5 | 0.242 | 0.034 | 0.286 | 7.165 | < .001 | ||||||||
Heterotrait-monotrait (HTMT) ratio of correlations | |
---|---|
. | |
. | |
注释. HTMT indices not available for this model. | |
[5] |
Mardia's coefficients | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Coefficient | z | χ² | df | p | |||||||
Skewness | 7.81 | 390 | 56 | < .001 | |||||||
Kurtosis | 71.27 | 20.6 | < .001 | ||||||||
[5] |
[1] The jamovi project (2023). jamovi. (Version 2.4) [Computer Software]. Retrieved from https://www.jamovi.org.
[2] R Core Team (2022). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN snapshot 2023-04-07).
[3] Gallucci, M., Jentschke, S. (2021). SEMLj: jamovi SEM Analysis. [jamovi module]. For help please visit https://semlj.github.io/.
[4] Rosseel, Y. (2019). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. link.
[5] Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., Rosseel, Y., Miller, P., Quick, C., Garnier-Villarreal, M., Selig, J., Boulton, A., Preacher, K., Coffman, D., Rhemtulla, M., Robitzsch, A., Enders, C., Arslan, R., Clinton, B., Panko, P., Merkle, E., Chesnut, S., Byrnes, J., Rights, J. D., Longo, Y., Mansolf, M., Ben-Shachar, M. S., Rönkkö, M. (2019). semTools: Useful Tools for Structural Equation Modeling. [R Package]. Retrieved from https://CRAN.R-project.org/package=semTools.
[6] Epskamp S. , Stuber S., Nak J., Veenman M,, Jorgensen T.D. (2019). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output. [R Package]. Retrieved from https://CRAN.R-project.org/package=semPlot.