This model has two parts: a measurement and a structural model. The measurement model includes the smoking dependence motives, and the structural part contains the covariates, including gender, HSI, TDS, presence of smoking partner, and selleck Enzalutamide household smoking rule. The degree of model fit was adequate (��2 = 1784.8, df = 700, CFI = 0.923, TLI = 0.906, RMSEA = 0.046, Cfit of RMSEA = 0.987, SRMR = 0.046). The standardized regression coefficients are presented in Table 5. Table 5. Confirmatory Factor Analysis With Covariates Model: Predictors of Smoking Dependence Motives Gender predicted only weight control motive, whereas TDS significantly predicted all motives, HSI predicted significantly only affiliative attachment, automaticity, loss of control, cognitive enhancement, craving, and tolerance while TDS was controlled for.

Discussion Our analysis confirmed the measurement model of the brief version of WISDM (WISDM-37), but the full version of WISDM (WISDM-68) was not confirmed in our Internet-based treatment-seeking Hungarian sample. Although WISDM-68 was validated in several studies with adult smokers (Piper et al., 2004; Shenassa et al., 2009), the misspecification of the WISDM-68 was observed by our research group in the present sample and in an independent sample of university students (Tombor & Urb��n, 2010) as well. The source of the misfit was rooted in the large modification indices, which indicated several correlated errors and cross-loadings. We could confirm the original measurement model of WISDM-37 (Smith et al., 2010), which contains 11 correlating factors and some correlated errors.

Besides the shorter length, the other advantage of WISDM-37 over WISDM-68 is that it contains fewer factors that overlap in content and therefore decreases the chance of cross-loadings and model misspecification. Internal consistencies of the WISDM-37 subscales are also satisfactory and comparable with those reported earlier (Smith et al., 2010), and similarly, cue exposure/associative processes have the lowest consistency among them. Therefore, the current study provides important data about smokers in another culture and in another language and also implies that this shorter inventory will be useful with smokers using Internet Web sites. The previous research (Piper et al., 2008; Smith et al., 2010) suggested two higher order factors, namely primary and secondary dependence motives.

Comparing two competing models, we found that 11 freely correlated factors model fitted the data significantly better than the alternative model, which implies two second-order factors. Anacetrapib Even so, more refinement is needed in the second-order factor structure since the present analysis also documented that this model still has adequate fit indices indicating only minor misspecifications.