Table 2.
Twin correlations |
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Variable | rMZ | rDZ | |||||||||
SS | .45 (.32, .58) | .18 (.03, .33) | |||||||||
PD | .56 (.42, .71) | .33 (.19, .47) | |||||||||
DEL | .59 (.43, .74) | .37 (.19, .47) | |||||||||
Standardized parameter estimates from ACE models |
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Variable | A | C | E | ||||||||
SS | .43 (.31, .55) | .00 (.00, .00) | .56 (.45, .69) | ||||||||
PD | .30 (.01, .60) | .00 (.00, .00) | .70 (.40, .99) | ||||||||
DEL | .42 (.03, .81) | .14 (.00, .44) | .44 (.29, .59) | ||||||||
Standardized parameter estimates from ADE models |
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Variable | A | D | E | ||||||||
SS | .22 (.00, .82) | .24 (.00, .88) | .54 (.42, .66) | ||||||||
PD | .30 (.01, .60) | — | .70 (.40, .99) | ||||||||
DEL | .58 (.45, .70) | — | .42 (.30, .55) | ||||||||
Model comparison results | |||||||||||
Variable | Model | AIC | RMSEA | CFI | χ2 | df | c | Nested comparison | Δχ2 | p | Preferred model |
SS | ACE | 1,538.9 | .00 | 1.00 | 4.27 | 6 | 1.20 | ||||
ADE | 1,538.3 | .00 | 1.00 | 4.42 | 6 | 1.03 | |||||
AE | 1,536.9 | .00 | 1.00 | 4.99 | 7 | 1.03 | ADE vs. AE | .57 | .44 | AE | |
PD | ACE | 1,480.1 | .05 | .938 | 7.47 | 5 | 1.39 | ||||
AE | 1,478.1 | .05 | .926 | 8.96 | 6 | 1.16 | ACE vs. AE | .00 | .96 | AE | |
DEL | ACE | 1,457.2 | .00 | 1.00 | 4.21 | 6 | .88 | ||||
AE | 1,455.9 | .00 | 1.00 | 5.01 | 7 | .89 | ACE vs. AE | .78 | .37 | AE |
Note. Sibling contrast effects were modeled for peer deviance. 95% confidence intervals are reported in parentheses. SS = sensation seeking; PD = peer deviance; DEL = delinquency; rMZ = monozygotic twin-pair correlation; rDZ = dizygotic twin-pair correlation; A = additive genetics; C = shared environment; E = nonshared environment; D = dominance genetics; AIC = Akaike information criteria; df = degrees of freedom; c = scaling correction factor for model chi-squared. Differences between χ2 values obtained using maximum likelihood estimation with robust standard errors are not themselves χ2 distributed. Therefore, model χ2 values with scaling correction factors were used to compute Satorra-Bentler χ2 tests for nested model comparisons.