Modifikationsindizes
Strukturgleichungsmodellierung mit lavaan in R
Erin Buchanan
Professor
Schwache Modelle verbessern
User-Modell versus Basismodell:
Comparative Fit Index (CFI) 0.879
Tucker-Lewis Index (TLI) 0.774
Root Mean Square Error of Approximation:
RMSEA 0.128
90-Prozent-Konfidenzintervall 0.094 0.164
p-Wert RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.079
Schwache Modelle verbessern (2)
Latente Variablen:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
visual =~
x1 1.000 0.777 0.667
x2 0.690 0.124 5.585 0.000 0.536 0.456
x3 0.985 0.160 6.157 0.000 0.766 0.678
speed =~
x7 1.000 0.622 0.572
x8 1.204 0.170 7.090 0.000 0.749 0.741
x9 1.052 0.147 7.142 0.000 0.654 0.649
Schwache Modelle verbessern (3)
Varianzen:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.x1 0.754 0.110 6.838 0.000 0.754 0.555
.x2 1.094 0.103 10.661 0.000 1.094 0.792
.x3 0.688 0.105 6.557 0.000 0.688 0.540
.x7 0.796 0.082 9.756 0.000 0.796 0.673
.x8 0.461 0.077 6.002 0.000 0.461 0.451
.x9 0.587 0.071 8.273 0.000 0.587 0.578
var(HolzingerSwineford1939$x1)
1.362898
Modifikationsindizes
modificationindices(twofactor.fit, sort = TRUE)
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
34 x7 ~~ x8 35.521 0.624 0.624 0.568 0.568
18 visual =~ x9 35.521 0.659 0.512 0.508 0.508
36 x8 ~~ x9 19.041 -0.527 -0.527 -0.517 -0.517
16 visual =~ x7 19.041 -0.503 -0.391 -0.359 -0.359
26 x1 ~~ x9 11.428 0.177 0.177 0.151 0.151
- Nacheinander hinzufügen
- Nur sinnvolle Parameter hinzufügen
Modell aktualisieren
34 x7 ~~ x8 35.521 0.624 0.624 0.568 0.568
twofactor.model <- 'visual =~ x1 + x2 + x3
speed =~ x7 + x8 + x9
x7 ~~ x8'
twofactor.fit <- cfa(model = twofactor.model,
data = HolzingerSwineford1939)
summary(twofactor.fit, standardized = TRUE,
fit.measures = TRUE)
User-Modell versus Basismodell:
Comparative Fit Index (CFI) 0.976
Tucker-Lewis Index (TLI) 0.949
Lass uns üben!
Strukturgleichungsmodellierung mit lavaan in R
