Personality and Individual Differences

There are three files attached. (1) Dataset of STUDY 1 (DSM-5.sav) (2) Dataset of STUDY 2 (STUDY2.sav) (3) Script containing the SFI function

Collated valid data

In the Mind of the Beholder Dataset.

The file contains survey data from N = 911 participants. The 37 TCI-Items have been generated to measure individual differences on four temporal coping strategies: Impermanence focus, present centeredness, positive temporal refocussing, and negative temporal contrasting. The data has been analysed to develope the final 20-item version of the Temporal Coping Inventory. The file contains sociodemographic information, such as age, gender, education, but also biographical information (critical life events, acute level of distress), and a rough measure for time perspective.

Data files and syntax for analysis of 3 experimental studies regarding the effects of power-incongruence and self-control on prosocial motivation and behavior.

A data set of an Irish survey into attitude to ART

Data

Data for: Emotional stability and citizenship fatigue: The role of emotional exhaustion and job stressors

There are three datasets corresponding to three experiments exploring the effects of environmental cues on prosociality. The authors focused on two overarching environmental factors: unpredictability, which represents the variability of extrinsic threats, and competition, which represents the relevance of others' relative performance to one's fitness. Method: In each experiment, participants were exposed to cues of unpredictability and/or competition before assessment of spontaneous prosocial behaviors (Studies 1 and 2) or prosocial judgments in dual-choice dilemmas (Study 3). We also took into account the interaction between the two environmental factors and two moderators, namely resource availability and prosocial thinking types. Main Results: unpredictable cues generally led to lower prosocial behaviors and fewer prosocial judgments (Studies 2 & 3). In contrast, competitive cues led to lower prosocial behaviors among individuals with resource disadvantages (Study 1), and when combined with unpredictable cues (Study 2), but also led to higher prosocial behaviors among individuals with resource advantages (Study 1) and more prosocial judgments in response to rational, utilitarian dilemmas (Study 3). Taken together, these results indicated that human prosociality is affected by environmental factors in predictable ways.

Personality research has traditionally relied on questionnaires, which bring with them inherent limitations, such as response style bias. With the emergence of large language models such as ChatGPT, the question arises as to what extent these models can be used in personality research. In this study, ChatGPT (GPT-4) generated 2000 text-based personas. Next, for each persona, ChatGPT completed a short form of the Big Five Inventory (BFI-10), the Brief Sensation Seeking Scale (BSSS), and a Short Dark Triad (SD3). The mean scores on the BFI-10 items were found to correlate strongly with means from previously published research, and principal component analysis revealed a clear five-component structure. Certain relationships between traits, such as a negative correlation between the age of the persona and the BSSS score, were clearly interpretable, while some other correlations diverged from the literature. An additional analysis using four new sets of 2000 personas each, including a set of ‘realistic’ personas and a set of cinematic personas, showed that the correlation matrix among personality constructs was affected by the persona set. It is concluded that evaluating questionnaires and research hypotheses prior to engaging with real individuals holds promise.