1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.

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1 1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.

2 2 3 2 Conservative Depress SEM. 1. x SEM. Depress. x11 x12 x13,, x11-x13. 2., Conservative.,. x1 x3-x7 x9.

3 , SEM /.. ( )Depress Conservative SEM / /.,. ( ) / /..

4 /., Stata,.. estat gof,stats (all) Fit statistic Value Description Likelihood ratio chi2_ms(33) model vs. saturated p > chi chi2_bs(45) baseline vs. saturated p > chi Population error RMSEA Root mean squared error of approximation 90% CI, lower bound upper bound pclose Probability RMSEA <= 0.05 Information criteria AIC Akaike s information criterion BIC Bayesian information criterion Baseline comparison CFI Comparative fit index TLI Tucker-Lewis index Size of residuals SRMR Standardized root mean squared residual CD Coefficient of determination.. Chi-square(33)= {it:p}<0.01 RMSEA=0.41 CFI=0.98 SRMA=0.03 {it:n}=1466 {it:p} p. SEM, 2 Depress Conservative.

5 SEM. 1.,. McClelland et al. (2013) path.dta 4. 7., SEM. SEM., mlmv( ).,. 1 Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 2 A substantive example of a path model.

6 sem (attention4 -> math7, ) (attention4 -> read7, ) (attention4 -> math21, ) (math7 -> math21, ) (read > 7 -> math21, ), method(mlmv) standardized Endogenous variables Observed: Exogenous variables Observed: math7 read7 math21 attention4 Fitting saturated model: ( ) Structural equation model Number of obs = 430 Estimation method = mlmv Log likelihood = OIM Standardized Coef. Std. Err. z P> z [95% Conf. Interval] Structural math7 <- attention _cons read7 <- attention _cons math21 <- math read attention _cons var(e.math7) var(e.read7) var(e.math21) LR test of model vs. saturated: chi2(1) = 27.56, Prob > chi2 =

7 7 3 2 math7 attention4 ( ). read7 ( ). math21 math7 read7, math7 math21 attention4, math7 read7... estat eqgof Equation-level goodness of fit Variance depvars fitted predicted residual R-squared mc mc2 observed math read math overall mc = correlation between depvar and its prediction mc2 = mc^2 is the Bentler-Raykov squared multiple correlation coefficient math7 read7, 2%., math21 19%. R 2 = mc2 (Bentler-Raykov R 2 ).. estat gof,stats(all)

8 Fit statistic Value Description Likelihood ratio chi2_ms(1) model vs. saturated p > chi chi2_bs(6) baseline vs. saturated p > chi Population error RMSEA Root mean squared error of approximation 90% CI, lower bound upper bound pclose Probability RMSEA <= 0.05 Information criteria AIC Akaike s information criterion BIC Bayesian information criterion Baseline comparison CFI Comparative fit index TLI Tucker-Lewis index Size of residuals CD Coefficient of determination Note: SRMR is not reported because of missing values. χ 2 (1) = 27.56, p < 0.001,. RMSEA 0.25, 0.05,. CFI 0.9,.. estat mindices

9 9 3 2 Modification indices Standard MI df P>MI EPC EPC Structural math7 read7 read math math math cov(e.math7,e.read7) EPC = expected parameter change,, math21 read7 math7,., read7 math7.,.,. 3.3 math7 read7. 1. perfect fit,,,.

10 , mlmv. Structural equation model Number of obs = 430 Estimation method = mlmv Log likelihood = OIM Standardized Coef. Std. Err. z P> z [95% Conf. Interval] Structural math7 <- attention _cons read7 <- attention _cons math21 <- math read attention _cons var(e.math7) var(e.read7) var(e.math21) cov(e.math7,e.read7) LR test of model vs. saturated: chi2(0) = 0.00, Prob > chi2 =... estat eqgof Equation-level goodness of fit

11 Variance depvars fitted predicted residual R-squared mc mc2 observed math read math overall mc = correlation between depvar and its prediction mc2 = mc^2 is the Bentler-Raykov squared multiple correlation coefficient math7 read7 math , estat gof,stats(all) estats mindices 3.4 math21 3 attention4 math7 read7 2. estat teffects,standardize

12 Direct effects OIM Coef. Std. Err. z P> z Std. Coef. Structural math7 <- attention read7 <- attention math21 <- math read attention Indirect effects OIM Coef. Std. Err. z P> z Std. Coef. Structural math7 <- attention4 0 (no path) 0 read7 <- attention4 0 (no path) 0 math21 <- math7 0 (no path) 0 read7 0 (no path) 0 attention Total effects OIM Coef. Std. Err. z P> z Std. Coef. Structural math7 <- attention read7 <- attention math21 <- math read attention Std. Coef. 1,, 2, attention4 math7 read

13 = ,. Math7 attention4 math Read7 attention4 read Math21 attention4 math math7 math read7 math p < 0.05, p < 0.01, p < , ( ),

1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.

1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,