Preliminary Analyses Clause Examples

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. 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. Table 2 shows means and standard deviations of baseline variables. We found no significant differences on baseline measures by infant sex. Mothers completed an average of 16.64 (SD = 8.79) surveys, or 59.4% of all possible surveys, with a range from 3.6% to 100% completion. Response rate was significantly correlated with only one of the baseline variables; lower response rate was associated with higher baseline maternal symptoms of anxiety (see last line of Table 3). Table 3 also shows correlations among all of the baseline variables; we return to Table 3 for the description of findings relevant to Aim 1. See Table 4 for means, standard deviations and number of surveys completed for all EMA variables by time point. Table 5 displays correlations among EMA variables across all survey timepoints. All hypothesized predictor variables (symptoms of depression and anxiety, positive mood, and negative mood) were significantly intercorrelated. As such, we ran separate models for each predictor. Preliminary analyses regarding potential covariates revealed that maternal education level exerted a significant influence on PSE and was therefore included as a covariate in all HLM analyses. See Table 6 for a complete list of variables we examined as potential covariates. and infant characteristics, were mixed (see column 1 in Table 3). In partial support of Hypothesis 1A, PSE (labeled as PSOC in Table 3) was significantly, negatively associated with the DASS depression scale, r = -0.24, p =.003, but was not significantly associated with the DASS anxiety scale. In partial support of Hypothesis 1B, that baseline PSE would be associated with a set of maternal and infant characteristics, baseline PSE was significantly, negatively associated with the DASS stress scale, r = - 0.27, p = 0.001, but was not significantly associated with perceived social support or sleep quality. Further, PSE was significantly associated with two of the three domains of infant temperament, in the predicted directions: positively associated with effortful control and negatively associated with negative affect, r = 0.23, p = 0.004, and r = -0.25, p = 0.002, respectively, but not significantly associated with the surgency domain of infant temperament Next, we describe results from the HLM analyses of the EMA data to test Hypothesis 2, that PSE would be variable over the course of the day and over the course of the study. We ran an intercept-only model which revealed that the mean level of PSE across a...

Preliminary Analyses. A path analysis was conducted using Mplus 7.4 (▇▇▇▇▇▇ and ▇▇▇▇▇▇, 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 ▇▇ and ▇▇▇▇▇▇▇ (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 & ▇▇▇ ▇▇▇, 2011) and that motives of entrepreneurship are related to optimistic beliefs about the future (▇▇▇ ▇▇▇ ▇▇▇▇ 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. 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...

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. 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. 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. 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 ▇▇▇▇▇▇▇-▇▇▇▇ 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. 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 predicted overfeedi...