Principles and practice of Structural Equation Modeling Ch.7
今年4版が出て、Stataのコードもついてくる模様。
今のところraw dataはないので、共分散行列を入力してそれを分析することになる。
共分散行列は、サポートページでしめされている標準偏差と相関行列から計算することができる。
Rの場合はcor2cov()で相関行列と標準偏差を共分散行列に書き換えてくれる。
基本的にxとyの相関＝（xとyの共分散）/（xの標準偏差）(yの標準偏差)なので、
相関にxとyの標準誤差をかければ、共分散は求められるので、エクセルでも計算できる。
clear all ssd init coercive burnout support tpi experien somatic ssd set obs 109 ssd set cov 69.009572 \ 28.868204 95.447038 \ -22.427279 -49.071453 110.695649 \ -16.805466 1.011164 9.805758 25 \ -4.987848 3.406984 2.808509 12.160924 13.822037 \ -15.269773 -0.68495 8.707448 19.179989 9.728453 27.787658 ssd list sem (burnout<-coercive support) (tpi<-coercive support burnout) (experien<-tpi) (somatic<-tpi), cov(coercive*support) estat gof, stats(all)
Endogenous variables
Observed: burnout tpi experien somatic
Exogenous variables
Observed: coercive support
Fitting target model:
Iteration 0: log likelihood = -2052.8451
Iteration 1: log likelihood = -2052.8451
Structural equation model Number of obs = 109
Estimation method = ml
Log likelihood = -2052.8451
--------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
burnout <- |
coercive | .2935851 .0984724 2.98 0.003 .1005828 .4865873
support | -.3838194 .0777506 -4.94 0.000 -.5362078 -.231431
-------------+----------------------------------------------------------------
tpi <- |
burnout | .1424866 .0510318 2.79 0.005 .0424661 .2425072
coercive | -.2717027 .0545622 -4.98 0.000 -.3786426 -.1647629
support | .0966997 .0458219 2.11 0.035 .0068904 .1865089
-------------+----------------------------------------------------------------
experien <- |
tpi | .486437 .0538653 9.03 0.000 .3808629 .5920111
-------------+----------------------------------------------------------------
somatic <- |
tpi | .7671996 .0692629 11.08 0.000 .6314468 .9029524
---------------+----------------------------------------------------------------
var(e.burnout)| 67.51208 9.14499 51.77024 88.04055
var(e.tpi)| 19.16417 2.595923 14.69565 24.99144
var(e.experien)| 7.833977 1.061168 6.007324 10.21606
var(e.somatic)| 12.95285 1.754555 9.932622 16.89143
var(coercive)| 68.37646 9.262076 52.43307 89.16776
var(support)| 109.6801 14.85695 84.10591 143.0306
---------------+----------------------------------------------------------------
cov(coercive,|
support)| -22.22152 8.563488 -2.59 0.009 -39.00565 -5.437396
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(7) = 3.93, Prob > chi2 = 0.7877
. estat gof, stats(all)
----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Likelihood ratio |
chi2_ms(7) | 3.931 model vs. saturated
p > chi2 | 0.788
chi2_bs(14) | 211.718 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.000 Root mean squared error of approximation
90% CI, lower bound | 0.000
upper bound | 0.078
pclose | 0.882 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Information criteria |
AIC | 4133.690 Akaike's information criterion
BIC | 4171.369 Bayesian information criterion
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 1.000 Comparative fit index
TLI | 1.031 Tucker-Lewis index
---------------------+------------------------------------------------------
Size of residuals |
SRMR | 0.034 Standardized root mean squared residual
CD | 0.447 Coefficient of determination
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