80, and standardized loadings of secondary dependence motives range between 0.25 and 0.95. The selleckchem correlation between the two second-order factors is 0.73. The inspection of modification indices reveals large correlations between social/environmental goads and cues and between tolerance and automaticity. Freeing these error covariances increased the model fit (��2 = 1,672.0, df = 610, CFI = 0.924, TLI = 0.920, RMSEA = 0.049, Cfit of RMSEA = 0.658, SRMR = 0.057), but it is still significantly less adequate than for Model 3. Multigroup CFA: Gender Differences The measurement invariance (equal latent form, equal factor loadings, equal indicator intercepts, equal factor variances, and equal factor correlations) of the Brief WISDM was examined in men and women by use of multiple group CFA.

We estimated the model fit in both genders separately, which yielded an adequate degree of fit in both groups (males: ��2 = 1,001, df = 570, CFI = 0.923, TLI = 0.910, RMSEA = 0.050, Cfit of RMSEA = 0.498, SRMR = 0.055; females: ��2 = 1153, df = 570, CFI = 0.923, TLI = 0.910, RMSEA = 0.051, Cfit of RMSEA = 0.315, SRMR = 0.051). Four nested models with increasing constraints were estimated. The fit indices are reported in Table 4. First, the measurement model was estimated freely in men and women together. This unconstrained solution fitted the data satisfactorily. In the second model, the factor loadings and intercepts were set as equal between the genders. The degree of fit (��2) decreased significantly (Satorra�CBentler scaled ��2difference test =71.8, df = 52, p < .

04), but the other indices still remained in the acceptable range. In the third model, the factor variances were set as equal. The degree of fit (��2) decreased further significantly (Satorra�CBentler scaled ��2difference test = 20.41, df = 11, p < .04). In the fourth, the correlations between the factors were set as equal in both groups. The degree of fit (��2) did not change significantly (Satorra�CBentler scaled �� 2difference test = 43.9, df = 56, p > .05); therefore, the correlations between factors are equal in men and women. Table 4. Multigroup Analysis of Brief Wisconsin Inventory of Smoking Dependence Motives With Four Nested Models Concurrent Validity: CFA With Covariates Before the estimation of the CFA with covariates model, we also examined the correlations between two smoking dependence motives and the number of nicotine dependence symptoms Anacetrapib measured by TDS and heaviness of smoking measured by HSI. Table 3 presents the correlations. All 11 smoking dependence motives correlate significantly with both measures of nicotine dependence, and only the correlation between social/environmental goads and TDS was not significant.