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. 2017 Nov 24;6(4):579–592. doi: 10.1556/2006.6.2017.065

Table 4.

Parameter estimates for HLM analysis examining Compulsive Internet Use as function of time (treatment effect) moderated by compliance

Compulsive Internet Use
Parameter Model 0 Model 1 Model 2
Fixed effects
Initial status Intercept (γ00) 28.24 35.45 30.80
(1.39) (2.35) (2.88)
p < .001 p < .001 p < .001
Compliance (γ01) 11.07
(4.41)
p = .016
Rate of change Slope (γ10) −3.94 −1.84
(1.10) (1.33)
p = .001
Compliance (γ11) −5.09
(2.06)
p = .018
Variance components
Level 1 Within-person (Inline graphic) 86.49 66.59 64.58
(14.97) (16.86) (15.86)
p < .001 p < .001 p < .001
Level 2 Initial status (Inline graphic) 49.66 61.31 35.67
(18.69) (64.90) (57.58)
p = .008
Rate of change (Inline graphic) 5.74 0.58
(13.74) (12.13)
Covariance (σ01) −6.97 4.54
(27.43) (24.08)
Model fit parameters
graphic file with name jba-06-04-065_ig006.jpg .23 .25
graphic file with name jba-06-04-065_ig007.jpg .42
graphic file with name jba-06-04-065_ig008.jpg .90
−2 log-likelihood 834.10 820.56 814.21
AIC 840.10 832.56 830.21

Note. Standard errors are displayed in parentheses. The rate of change displays the amount of change per observation. Inline graphic: estimates the proportion of explained within-person variation (level 1); Inline graphic: estimates the proportion of explained between-person variation in the intercept (level 2); Inline graphic: estimates the proportion of explained between-person variation in the slope (level 2); HLM: hierarchical linear models; AIC: Akaike’s information criterion.