An Evaluation of Impostor Phenomenon in Data Science Students

Area of Science:

• Psychology
• Data Science Education

Background:

• Impostor Phenomenon (IP) involves persistent feelings of inadequacy despite achievements.
• Data science programs are growing, but IP in this field is understudied.
• Understanding IP's prevalence and predictors in data science students is crucial.

Purpose of the Study:

• To assess the prevalence of IP among data science students.
• To investigate the relationship between gender identification and IP.
• To examine various psychological factors (goal orientation, perfectionism, self-efficacy, anxiety, etc.) in relation to IP and their predictive power.

Main Methods:

• Survey-based study evaluating Impostor Phenomenon (IP) in data science students.
• Analysis of gender identification, goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value.
• Statistical examination of the links between these variables and IP levels.

Main Results:

• A majority of data science students reported moderate to frequent levels of IP.
• Gender identification showed a positive correlation with IP for both males and females.
• Significant differences in perfectionism, value, self-efficacy, anxiety, and avoidance goals were observed across different IP levels.

Conclusions:

• Impostor Phenomenon is prevalent among data science students.
• Perfectionism, self-efficacy, and anxiety are significant predictors of IP in this cohort.
• Findings suggest targeted interventions to mitigate IP in data science education.