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 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. 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. Discipline Mean Standard Deviation Overall 3.17 1.38 Social Workers 3.13 1.31 Counselors 3.23 1.36 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. Internalizing and externalizing scores were correlated at pretreatment (r=.61, p<.001) and at post treatment (r=.77, p<.001). Table 2 presents the inter-correlations between stress measures. As can be seen, lifetime stress did not correlate with either of the other stress measures. Negative recent life events and daily hassles were positively correlated. Table 2 also depicts the significant correlation between daily hassles and basal cortisol levels. Cortisol sampled at pretreatment was not significantly associated with recent negative life events or lifetime stress. Preliminary analyses revealed that the time of day that cortisol was collected was significantly associated with cortisol levels at awakening (r=.20, p<.05) and this variable was controlled for in all further analyses examining HPA axis dysregulation. Number of hours slept the night before collection, as well as the use of cigarettes, chewing tobacco, prescription medication, antihistamines, marijuana, steroids, sleep medications, allergy medications, cold/flu medication, and caffeine were not significantly related to cortisol levels at awakening. Youth age was not significantly correlated with externalizing behaviors, cortisol, or any of the stress measures. This nonsignificant correlation runs counter to some findings in the literature (Xxxxx et al., 2008). However, our sample was restricted to adolescence, as we sampled only from youth aged 12 to 18 years. In this study, SES was positively correlated with externalizing behaviors at pretreatment (r=.24, p<.01). SES was also significantly correlated with ethnicity such that Latino youth had significantly lower SES (r=-.35, p<.001) and white youth had significantly higher SES (r=.35, p<.001). SES was controlled for in all analyses. An ANCOVA controlling for SES revealed ethnic group differences for externalizing behaviors, such that youth who were White had higher externalizing behaviors at pretreatment. No significant differences between ethnicities were found for externalizing behaviors at post treatment. All further analyses exploring externalizing behavior at pretreatment statistically controlled for ethnicity (using a dummy coded variable). Zero-order correlations between stress, basal cortisol, and pretreatment externalizing behavior are presented in Table 5. Another consideration is that self-report measures are susceptible to response sets and styles and can lead to spurious results. Researchers in this study attempted to control for t...
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. 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). 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. We tested for differences between the three age groups regarding demographic variables in the Time 1 mother and father samples and in the longitudinal sample. The only differences between age groups were found for presence of siblings and parental educational level (Table 2.2). In the Time 1 mother sample and the Time 1 father sample, older children had siblings more often than younger children. In addition, 12-month-olds in the Time 1 mother sample had parents with a higher educational level than 36-month-olds. No differences between age groups on demographic variables existed in the longitudinal sample. Because age groups in the Time 1 mother sample differed on presence of siblings and parental educational level, analyses concerning age differences in this sample were controlled for the effects of these two variables. To facilitate comparison across mother and father reports of aggression, the same control variables were used in analyses concerning age effects in the Time 1 father sample. Some univariate outliers were found, but additional analyses showed that the outliers had no effects on the results. No multivariate outliers were identified.
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. 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 predicted the number of SHC (B = .094, p < .01, 95% CI: .028 - .160), whereas the e...
