An Evaluation of Impostor Phenomenon in Data Science Students
| Autor: | Lindsay Duncan, Gita Taasoobshirazi, Ashana Vaudreuil, Jitendra Sai Kota, Sweta Sneha |
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| Rok vydání: | 2023 |
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| Zdroj: | International Journal of Environmental Research and Public Health Volume 20 Issue 5 Pages: 4115 |
| ISSN: | 1660-4601 |
| Popis: | Impostor Phenomenon (IP), also called impostor syndrome, involves feelings of perceived fraudulence, self-doubt, and personal incompetence that persist despite one’s education, experience, and accomplishments. This study is the first to evaluate the presence of IP among data science students and to evaluate several variables linked to IP simultaneously in a single study evaluating data science. In addition, it is the first study to evaluate the extent to which gender identification is linked to IP. We examined: (1) the degree to which IP exists in our sample; (2) how gender identification is linked to IP; (3) whether there are differences in goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value for different levels of IP; and (4) the extent to which goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value predict IP. We found that most students in the sample showed moderate and frequent levels of IP. Moreover, gender identification was positively related to IP for both males and females. Finally, results indicated significant differences in perfectionism, value, self-efficacy, anxiety, and avoidance goals by IP level and that perfectionism, self-efficacy, and anxiety were particularly noteworthy in predicting IP. Implications of our findings for improving IP among data science students are discussed. |
| Databáze: | OpenAIRE |
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