Preliminary Analyses. The measure utilized in this study contained three vignettes that were diagnosed by participants. Each vignette was assigned a point value dependent on the diagnosis provided by the participant. The point values for each vignette ranged from 0 to 2 points. Two points were given for a diagnosis that matched the correct diagnosis for the vignette. One point was given to diagnoses that were in the same DSM-5 category as the correct diagnosis. Zero points were given to all other diagnoses. This scoring system yielded a cumulative score with a range of 0 to 6 points. The overall average cumulative score was 3.17 points while the standard deviation was 1.38 points. Table 4 Mean Cumulative Scores by Discipline Discipline Mean Standard Deviation Overall 3.17 1.38 Social Workers 3.13 1.31 Counselors 3.23 1.36 Hypothesis Testing To test the hypothesis that a significant difference existed between psychologists, counselors, and social workers in regards to diagnosing, an One-Way Analysis of Variance
Preliminary Analyses. Because it was hypothesized that changes in perception of health status (as assessed with the SAS, HA subscale of the IAS and RQ) are correlated, the changes on these variables were transformed in one principal component accounting for as much of the variability in the data as possible. To this end a principal component analysis (PCA) was conducted on the 12 week follow-up residualised change scores on these measures (obtained by statistically correcting the follow-up scores for any baseline differences on these measures). Next, using the regression method a composite fac- tor score for change in perception of current health problems was calculated. The PCA on the residualised gain scores on the SAS, HA subscale and RQ at fol- low-up clearly yielded a one-factor solution (eigenvalue 1.75) accounting for 58.4% of the variance. Factor loadings were high (respectively 0.70, 0.82 and 0.77). Since there were no significant differences between the major non-western groups in change score in perception of current health problems (data not shown), we decided to operationalise ethnicity as western versus non-western. The ethnic difference in 113 changes in perception of health status was significant (t(295): -3.53, p<.001) and had a moderate effect size (d = 0.38). In Table 1 an overview is presented of characteristics of participants with a west- ern or non-western ethnicity. Except for gender, all demographic variables show a significant difference between residents of a western or non-western ethnicity. In addition, non-western residents reported more symptoms of fatigue, psychopatholo- gy, and post-traumatic stress and less health-related quality of life compared to west- ern participants at baseline.
Preliminary Analyses. At pretreatment the mean CES-D score for the samples was 24.71 (SD = 9.82). Mental health characteristics are summarized in Table 1. In both IC and WLC approximately two-thirds of the participants met the criteria for at least one axis I disorder. The most prevalent disorders were MDD (39%) and anxiety disorders (45%). Previous MDD was reported by 69% of the participants. There were no significant differences (all p
Preliminary Analyses. A path analysis was conducted using Mplus 7.4 (Xxxxxx and Xxxxxx, 1998-2015). MLM estimator was chosen for its robustness to non-normality in data that does not contain missing values (Muthén and Muthén, 1998-2015). Prior to hypothesis testing, an analysis of goodness of fit was conducted. Based on the criteria of goodness of fit by Xx and Xxxxxxx (1999), RMSEA values lower than .08 and CFI and TLI values above .90 are indicators of good fits to the data. The hypothesized model did not fit the data well (RMSEA = .163, 90% CI = [0.130, 0.198], CFI = .95, TLI =.94). Given that previous research suggests that motives of entrepreneurship are associated with aspirations to grow a business (e.g., Verheul & xxx Xxx, 2011) and that motives of entrepreneurship are related to optimistic beliefs about the future (xxx xxx Xxxx et al., 2016), the hypothesized model was revised by adding direct paths from motives of entrepreneurship to future time perspective and general growth intentions. The analysis of goodness of fit revealed that the revised model fitted the data well (RMSEA = .060, 90% CI = [0.000, 0.0114], CFI = .99, TLI =.97;
Preliminary Analyses. The analysis of goodness of fit of the revised hypothesized model that included variables representing experimental conditions (i.e., simplified version and extended version) and a control condition revealed that the model fitted the data well (RMSEA = .045, 90% CI = [0.000, 0.089], CFI = .98, TLI =.96; see Figure 4.2). Figure 4.2. Hypothesized model in Study 4.2 Hypothesis testing Hypotheses 1 and 2. In support of Hypothesis 1, opportunity-based entrepreneurship was positively associated with entrepreneurial self-esteem (β = .25, SE =.06, p < .01), whereas necessity-based entrepreneurship was negatively associated with entrepreneurial self-esteem (β =
Preliminary Analyses. The analysis of goodness of fit of the revised hypothesized model revealed that the model fitted the data well (RMSEA = .079, 90% CI = [0.037, 0.122], CFI = .97, TLI =.93; see Figure 4.3). Thus, this model was used for hypothesis testing in this study.
Preliminary Analyses. To assess whether multilevel analysis would be appropriate to analyze the effects of stressful events and worry episodes on somatic complaints, we first estimated the intra-class correlation in a baseline model with a random intercept and with SHC as the dependent variable, but without any predictors. The results showed that the intraclass correlation was .59, showing that 59% of the variance was due to individual differences between participants, thereby providing evidence for a 2-level hierarchical structure of the data. In addition, since the somatic complaints were measured repeatedly within subjects, we tested whether the error terms of the model would be correlated. Residual error covariance was modeled using the first-order auto-regressive covariance matrix, which showed that the estimated auto-correlation (ρ) was .22 (p = .017). With respect to the model predicting daily negative affect, a baseline random intercept model without predictors showed that the intra-class correlation was .46, that 46% of the variance was due to individual differences between participants. Because residual error covariance using the first-order auto-regressive covariance matrix did not yield stable models, the diagonal covariance matrix was used. Effects of stressful events and worry episodes on daily somatic complaints First we examined whether stressful events were associated with the number of SHC, while controlling for SHC the previous day. The effect of stressful events on SHC was significant (B = .191, p < .0001, 95% CI: .087 - .294). When stressful work and private events were entered separately into the model, the results showed that work related stressors had a larger effect on somatic complaints (B = .366, p < .01, 95% CI: .114 - .617) than private stressors (B = .290, p < .05, 95% CI: .052 - .527). Next, we examined the effects of the worry variables, (frequency, duration and intensity) on the number of SHC. The correlations between these variables were high (rs > .87). In a first step, SHC was regressed on worry frequency and worry duration (the variables used in the study by Brosschot & van der Doef [2006]), while controlling for the number of somatic complaints during the previous day. Worry frequency significantly predicted the number of somatic complaints (B = .451, p < .01, 95% CI: .152 - .749), and worry duration did this marginally (B = .008, p = .082, 95% CI: -.001 - .019). When worry intensity was entered into the model, only worry intensity predicte...
Preliminary Analyses. Chi squared analyses comparing CHR and HC groups revealed significant differences in sex ratio (larger proportion of males in CHR versus control group), but no group differences in race or ethnicity. T-tests revealed significant differences in age between groups (CHR mean = 18.5 years, healthy controls mean = 19.7 years). See Table 2 for a summary of sample characteristics including demographics and mean symptom ratings for the CHR and control groups. Remaining analyses comparing CHR and control groups included age and sex as covariates. In order to determine if medication status at baseline was related to the sleep variable (in the CHR participants), multiple independent samples T-tests were conducted. Results from the T-tests showed that there was no significant association between sleep disturbance and medication use of any kind, including sleep medications (see Table 3. for breakdown by medication status). Medication status was therefore not included as a covariate in remaining analyses. The lack of association between sleep disturbance and use of sleep medication likely indicates that the use of sleep medication is effective at reducing sleep disturbance.
Preliminary Analyses. See Table 1 for descriptive statistics of all measures. See Table 2 for intercorrelations between externalizing scores at pre- and post-treatment, delinquency scores at pre- and post- treatment, and internalizing scores at pre- and post-treatment. As seen in this table, all of the youth behavioral problem measures are significantly and positively correlated with the exception of self-reported delinquency at pre-treatment, which is only correlated with CBCL externalizing behaviors at pretreatment, and self reported delinquency post-treatment. A paired sample t-test found significant reductions in externalizing scores between pre- and post-treatment, t(167)=9.35, p<.001, and in delinquency scores between pre- and post-treatment, t(165)=4.66, p<.001. See Table 3 for intercorrelations between all stress measures. Once again, it can be seen that almost all of these measures are significantly and positively correlated, as would be expected. Potential Confounds In preliminary analyses, SES was not found to be significantly correlated with externalizing scores at pre- or post-treatment, internalizing scores at pre- and post- treatment, or gender. Due to the lack of associations between the primary dependent variables and SES, SES was not controlled for in the analyses.
Preliminary Analyses. Correlations between each predictor variable and infant weight at each time point are shown in Table 2. To examine whether the assumption of normality was met, infant weight at each time point was examined. The Xxxxxxx-Xxxx test of normality indicated that raw infant weight scores at all time points were normally distributed (ps > 0.05). Thus for all subsequent analyses in HLM, raw weight at each time point was used. First, an unconditional growth model was run using weight as the outcome and time as the predictor at level 1 in order to examine whether weight changed significantly over the course of the study. The estimated mean slope for the weight variable was 0.026 (SE=0.0007), indicating that infant weight increased at an average rate of 0.023 kilograms per day from birth to six-months-of-age. This was significant at p < 0.001, and indicated significant increases in weight over time within the sample. In addition, there was significant variation among slopes of weight gain in this sample (χ2=163.55; p < 0.001). Before examining the indirect effect of overfeeding on the relationship between maternal stress and RWG, associations between maternal age, gestational age, preterm status, infant sex, socioeconomic status, and overfeeding were examined using bivariate analyses. Results indicated that only preterm status was significantly associated with overfeeding (p = 0.046). None of the other potential control variables was related to overfeeding (ps > 0.05). When preparing to examine any main effects of maternal stress and overfeeding on weight gain, all potential covariates were each entered separately as predictors of infant weight (intercept and slope) in HLM. Results indicated that preterm status and gestational age were both significantly associated with birthweight (p < 0.001). Entering both variables as covariates would have been redundant, as preterm status is a dichotomized variable created from gestational age. Thus, only gestational age was entered as a covariate at the intercept in all following HLM analyses. No other variables were associated with either birthweight (i.e., intercept) or the slope of weight gain. Hypothesis Testing Prior to examining any associations in HLM, a binary logistic regression analysis was conducted to examine whether maternal stress was associated with the overfeeding composite variable. A significant association would support a portion of the mediation hypothesis. Results indicated that maternal stress significantly ...
