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- All Subjects: Self-efficacy
- Creators: Ganesh, Tirupalavanam
- Status: Published
However, this relationship may not be a simple cause-and-effect association. Individuals may possess a certain aptitude (emotional intelligence) and not perceive themselves as competent as counselors. Resilience, one’s ability to “bounce-back” and persevere through adversity may moderate the relation between emotional intelligence and counselor self-efficacy (Wagnild, 1990).
The current study explored the relations among clinical experience, emotional intelligence and resilience in predicting self-efficacy. In addition, whether resilience would moderate the relationship between emotional intelligence and counselor self-efficacy was examined. Eighty counselor trainees enrolled in CACREP-accredited master’s programs participated in this study online. They completed a demographics form, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, et al., 2002), the Counselor Activities Self-Efficacy Scales (CASES; Lent et al., 2003), and The Resilience Scale (RS; Wagnild & Young, 1993). Multiple hierarchical regressions revealed clinical experience (specifically a completed practicum), emotional intelligence, and resilience predicted counselor self-efficacy. The moderation was not significant. These findings support the value of the exploration of clinical experience, emotional intelligence and resilience in developing counselor self-efficacy. A more comprehensive discussion of the findings, limitations, and implications of the current study as well as suggested direction for future research are discussed herein.
Self-efficacy in engineering, engineering identity, and coping in engineering have been shown in previous studies to be highly important in the advancement of one’s development in the field of engineering. Through the creation and deployment of a 17 question survey, undergraduate and first year masters students were asked to provide information on their engagement at their university, their demographic information, and to rank their level of agreement with 22 statements relating to the aforementioned ideas. Using the results from the collected data, exploratory factor analysis was completed to identify the factors that existed and any correlations. No statistically significant correlations between the identified three factors and demographic or engagement information were found. There needs to be a significant increase in the data sample size for statistically significant results to be found. Additionally, there is future work needed in the creation of an engagement measure that successfully reflects the level and impact of participation in engineering activities beyond traditional coursework.