> library(sem) > cor.mat<-read.moments(names=c("ten1", "ten2", "ten3", "wor1", "wor2", + "wor3", "irthk1", "irthk2", "irthk3", "body1", "body2",

Transcription

1 > library(sem) > cor.mat<-read.moments(names=c("ten1", "ten2", "ten3", "wor1", "wor2", + "wor3", "irthk1", "irthk2", "irthk3", "body1", "body2", "body3")) 1: : : : : : : : : : : : : Read 78 items > cor.mat ten1 ten2 ten3 wor1 wor2 wor3 irthk1 ten ten ten wor wor wor irthk irthk irthk body body body irthk2 irthk3 body1 body2 body3 ten ten ten wor wor wor irthk irthk irthk body body body > model.paths<-specify.model() 1: tension -> ten1, lambda11, NA 2: tension -> ten2, lambda12, NA 3: tension -> ten3, lambda13, 1 4: worry -> wor1, lambda21, NA 5: worry -> wor2, lambda22, NA 6: worry -> wor3, lambda23, 1 7: testirt -> irthk1, lambda31, NA 8: testirt -> irthk2, lambda32, NA 9: testirt -> irthk3, lambda33, 1 10: bodysymp -> body1, lambda41, NA 11: bodysymp -> body2, lambda42, NA 12: bodysymp -> body3, lambda43, 1 13: tension <-> worry, phi12, NA

2 14: tension <-> testirt, phi13, NA 15: tension <-> bodysymp, phi14, NA 16: worry <-> testirt, phi23, NA 17: worry <-> bodysymp, phi24, NA 18: testirt <-> bodysymp, phi34, NA 19: ten1 <-> ten1, ten1.var, NA 20: ten2 <-> ten2, ten2.var, NA 21: ten3 <-> ten3, ten3.var, NA 22: wor1 <-> wor1, wor1.var, NA 23: wor2 <-> wor2, wor2.var, NA 24: wor3 <-> wor3, wor3.var, NA 25: irthk1 <-> irthk1, irthk1.var, NA 26: irthk2 <-> irthk2, irthk2.var, NA 27: irthk3 <-> irthk3, irthk3.var, NA 28: body1 <-> body1, body1.var, NA 29: body2 <-> body2, body2.var, NA 30: body3 <-> body3, body3.var, NA 31: tension <-> tension, NA, 1 32: worry <-> worry, NA, 1 33: testirt <-> testirt, NA, 1 34: bodysymp <-> bodysymp, NA, 1 35: Read 34 records > sem.rtts<-sem(model.paths, cor.mat, 318) > summary(sem.rtts) Model Chisquare = Df = 48 Pr(>Chisq) = Chisquare (null model) = Df = 66 Goodness-of-fit index = Adjusted goodness-of-fit index = RMSEA index = % CI: ( , ) Bentler-Bonnett NFI = Tucker-Lewis NNFI = Bentler CFI = SRMR = BIC = Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max e e e e e e+00 Parameter Estimates Estimate Std Error z value Pr(> z ) lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 phi e+00 phi e-02 phi e+00

3 phi e+00 phi e+00 phi e-05 ten1.var e+00 ten2.var e+00 ten3.var e-12 wor1.var e-16 wor2.var e+00 wor3.var e-15 irthk1.var e+00 irthk2.var e+00 irthk3.var e-11 body1.var e+00 body2.var e+00 body3.var e+00 lambda11 ten1 <--- tension lambda12 ten2 <--- tension lambda13 ten3 <--- tension lambda21 wor1 <--- worry lambda22 wor2 <--- worry lambda23 wor3 <--- worry lambda31 irthk1 <--- testirt lambda32 irthk2 <--- testirt lambda33 irthk3 <--- testirt lambda41 body1 <--- bodysymp lambda42 body2 <--- bodysymp lambda43 body3 <--- bodysymp phi12 worry <--> tension phi13 testirt <--> tension phi14 bodysymp <--> tension phi23 testirt <--> worry phi24 bodysymp <--> worry phi34 bodysymp <--> testirt ten1.var ten1 <--> ten1 ten2.var ten2 <--> ten2 ten3.var ten3 <--> ten3 wor1.var wor1 <--> wor1 wor2.var wor2 <--> wor2 wor3.var wor3 <--> wor3 irthk1.var irthk1 <--> irthk1 irthk2.var irthk2 <--> irthk2 irthk3.var irthk3 <--> irthk3 body1.var body1 <--> body1 body2.var body2 <--> body2 body3.var body3 <--> body3 Iterations = 36 > mod.indices(sem.rtts) 5 largest modification indices, A matrix: wor3:ten2 ten3:body1 wor1:ten3 wor3:tension ten3:body largest modification indices, P matrix: bodysymp:ten3 body3:irthk3 body1:ten3 bodysymp:ten2 wor1:ten

4 > model.paths2<-specify.model() 1: tension -> ten1, lambda11, NA 2: tension -> ten2, lambda12, NA 3: tension -> ten3, lambda13, 1 4: worry -> wor1, lambda21, NA 5: worry -> wor2, lambda22, NA 6: worry -> wor3, lambda23, 1 7: testirt -> irthk1, lambda31, NA 8: testirt -> irthk2, lambda32, NA 9: testirt -> irthk3, lambda33, 1 10: tension <-> worry, phi12, NA 11: tension <-> testirt, phi13, NA 12: worry <-> testirt, phi23, NA 13: ten1 <-> ten1, ten1.var, NA 14: ten2 <-> ten2, ten2.var, NA 15: ten3 <-> ten3, ten3.var, NA 16: wor1 <-> wor1, wor1.var, NA 17: wor2 <-> wor2, wor2.var, NA 18: wor3 <-> wor3, wor3.var, NA 19: irthk1 <-> irthk1, irthk1.var, NA 20: irthk2 <-> irthk2, irthk2.var, NA 21: irthk3 <-> irthk3, irthk3.var, NA 22: tension <-> tension, NA, 1 23: worry <-> worry, NA, 1 24: testirt <-> testirt, NA, 1 25: Read 24 records > #### Run new CFA > sem.rtts2<-sem(model.paths2, cor.mat, 318) Warning message: In sem.mod(model.paths2, cor.mat, 318) : The following observed variables are in the input covariance or raw-moment matrix but do not appear in the model: body1, body2, body3 > summary(sem.rtts2) Model Chisquare = Df = 24 Pr(>Chisq) = Chisquare (null model) = Df = 36 Goodness-of-fit index = Adjusted goodness-of-fit index = RMSEA index = % CI: ( , ) Bentler-Bonnett NFI = Tucker-Lewis NNFI = Bentler CFI = SRMR = BIC = Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max Parameter Estimates Estimate Std Error z value Pr(> z ) lambda e+00 lambda e+00 lambda e+00 lambda e+00

5 lambda e+00 lambda e+00 lambda e+00 lambda e+00 lambda e+00 phi e+00 phi e-02 phi e+00 ten1.var e+00 ten2.var e-14 ten3.var e-16 wor1.var e-15 wor2.var e+00 wor3.var e-15 irthk1.var e+00 irthk2.var e+00 irthk3.var e-11 lambda11 ten1 <--- tension lambda12 ten2 <--- tension lambda13 ten3 <--- tension lambda21 wor1 <--- worry lambda22 wor2 <--- worry lambda23 wor3 <--- worry lambda31 irthk1 <--- testirt lambda32 irthk2 <--- testirt lambda33 irthk3 <--- testirt phi12 worry <--> tension phi13 testirt <--> tension phi23 testirt <--> worry ten1.var ten1 <--> ten1 ten2.var ten2 <--> ten2 ten3.var ten3 <--> ten3 wor1.var wor1 <--> wor1 wor2.var wor2 <--> wor2 wor3.var wor3 <--> wor3 irthk1.var irthk1 <--> irthk1 irthk2.var irthk2 <--> irthk2 irthk3.var irthk3 <--> irthk3 Iterations = 34