Perceived Parental Control and Risk-Taking From a Machine Learning Approach

Abstract

The purpose of the current study was to examine the relationship of parental control and risk-taking among emerging adults. Specifically, the study examined the differences between high risk-taking and normal college students in parental control, risk-taking, and risky decision-making.Data were drawn from 538 college students by using an online survey. The measurements included demographic questions, parental control, risk tolerance, risk self-schema, and risk-taking. Two unsupervised learning methods, including data cloud geometry tree (DCG-tree) and agglomerative hierarchical clustering tree (HC-tree), were used to get clusters of participants based on the pattern of their responses on risky decision-making. Next, post hoc tests were conducted to examine the differences between the potential high risk-taking group and normal group.Among the participants, 46 students showed a special pattern in their responses and clustered into a group as potential high risk-takers. Compared to the normal group, the potential high risk-takers were more likely to engage in risk-taking behaviors (e.g., risky driving, smoking) and reported higher parental behavioral control and psychological control. In addition, the t-tests indicated that the high risk-takers could tolerate more risks and were more likely to have a self-schema of being a risk-taker in the decision-making process.The study suggests that parental control plays an important role in risk-taking among emerging adults. In addition, using machine learning approach can help identify the potential high risk-takers, who show distinctive characteristics that are different from the normal emerging adults and can be included as target in future intervention programs.



Author Information
Catherine Chou, Southeast Missouri State University, United States
Elizabeth Pei Ting Chou, National Chengchi University, Taiwan
Cheng Hsian Lee, National Chengchi University, Taiwan

Paper Information
Conference: ECP2018
Stream: Qualitative/Quantitative Research in any other area of Psychology

This paper is part of the ECP2018 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window


Posted by amp21