Exploratory Analyses Sample Clauses

Exploratory Analyses. In addition to providing demographic information and diagnoses, participants also provided information about if they had utilized the DSM-5 or consulted with a colleague while completing the questionnaire. This extra data provided extra information about participants’ environment and behaviors around diagnosing. While not the primary focus of this study, these exploratory analyses and the subsequent data gathered offer other possible answers or additional information to be considered when discussing this topic. Use of DSM-5. In addition to being divided into groups based on the discipline of their license, participants were also divided into groups based on use or non-use of the DSM-5 and if they consulted with a colleague or not. A t-test analysis was performed on this data due to comparing the means of two groups.

Exploratory Analyses. Exploratory efficacy analyses will be based on the ITT population. Details on exploratory efficacy analyses can be found in the SAP.

Exploratory Analyses. The results of our exploratory analyses—which demonstrate that college students may generally perceive faces of anger (Figure 15) and sadness (Figure 16) more intensely in the evening than in the morning, particularly at higher intensities— indicate the possibility that our nonsignificant results from hypothesis 2 are due to insufficient power. This possibility is apparent when we compare the temporal preference graphs to the time of testing graphs, as the visual trends are similar. The majority of our subjects were tested at night (52.2%) and our sample was slightly evening-oriented; this suggests overall that our subjects preferred the evening over the morning test time. Thus, social cognition and chronotype may in fact be related but in the opposite direction of our predictions: college students may show deficits in social cognition during the morning rather than during the evening; college students are more sensitive to highly expressive emotions during their preferred time of day (the evening) than during their non-preferred time of day despite the relationship between eveningness and mood or mood and social ineptitude. Upon summarizing studies investigating chronotype and temporal preference on cognitive functioning, Schmidt, Collette, Cajochen, & Peigneux (2007) found that time-of-day can affect performance on many cognitive tasks, regardless of physiological variables. We extend this literature in that we found a time-of-day effect for college students performing social cognitive tasks as well. These results must be interpreted cautiously because they may simply be demonstrating a true time-of-day effect—the tendency to perceive things differently at different times of day based on a multitude of variables, such as class schedule, social interaction times, food intake times, or stimulant use (to name a few)—that was not controlled for in this study; such a true time-of-day effect would apply to college students regardless of their chronotype or chronotype match to time-of-preference. Since these results suggest that our hypotheses may have been significant with more power, we can argue that social cognition may be affected by both the evening and by a personal history of social interactions biased towards evening times. The fact that the AM group rated these faces significantly lower at the high intensities rather than the low intensities, suggests that time-of-day becomes more critical for social cognition as expressive intensity increases. These ...

Exploratory Analyses. The distress measure used in the current study consisted of 5 items, one measuring guilt, three measuring negative affect, and one measuring burdensomeness to the rest of the group. Although the high Cronbach’s alfa (α = .94, throughout three studies) is a strong indication that these 5 items measure the same construct, in this exploratory analysis we looked at these three measures separately. Three independent t-tests explored whether low-performers who chose to leave vs. stay in the group differed in how guilty they felt while they were part of the group (i.e., prior to making the choice to stay/leave the group). Differences in negative affect and experienced burdensomeness are also reported. Guilt, Negative Affect, and Burdensomeness. Low-performers that eventually chose to leave the group had felt more guilty while they were still part of the group (M = 5.69, SD = 1.19) than those who chose to stay in the group, (M = 4.65, SD = 1.69), t(58) = 2.69, p = .009, d = 0.72. The differences between the two groups in negative affect were marginally significant, indicating that those who would choose to leave experienced marginally more negative affect (M = 4.81, SD = 0.91) than those who would choose to stay in the group, (M = 4.30, SD = 1.31), t(58) = 1.68, p = .099, d = 0.45. Similarly, participants that would later choose to leave felt marginally more burdensome while part of the group (M = 5.54, SD = 1.36) than those who would choose to stay in the group, (M = 4.85, SD = 1.71), t(58) = 1.68, p = .099, d = 0.44.

Exploratory Analyses. We will perform Multivariate MANOVAs on all of the driving and vision-based variables to determine if toric contact lenses have any unique driving-specific benefits.

Exploratory Analyses. Apart from the specific hypotheses above, correlations and mean differences between other rater-pairs will be assessed, as non-directional hypotheses, when sufficient data are available. In addition, other potential moderator variables (e.g., score type, socioeconomic status, parental depression, etc.) will be assessed providing a sufficient number of studies are available for analyses to be conducted. In cases where several studies are available but insufficient for a formal statistical analysis, the study results, similarities, and differences will be examined visually and conceptually for potential patterns, which though statistically inconclusive, may be suggestive for future studies to examine.

Exploratory Analyses. Each component of the study endpoint (all-cause death, liver transplant, and hospitalization for hepatic decompensation) will be assessed separately. For the outcome of death, the analysis will proceed using the same approach as for the primary effect size estimation. For the outcome of liver transplant, death will be treated as a competing event. Here, follow-up will end at the earliest of the end of follow-up, liver transplant, death, or censoring. The analysis will proceed as previously described, but instead of a ▇▇▇ proportional hazards model, a Fine-Gray proportional subdistribution hazards model will be used (Fine 1999). Similarly, for the outcome of hospitalization for hepatic decompensation, death and liver transplant will be treated as competing events.