College student Fear of Missing Out (FoMO) and maladaptive behavior: Traditional statistical modeling and predictive analysis using machine learning

Abstract

This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.

A concept analysis of misconduct: Application to nursing education

“…The researchers concluded that the prevalence of academic misconduct is high among students within online assessments due to one primary antecedent: the unpreparedness of universities to shift from usual practice to online assessment. 12 Congruently, McKee et al 22 examined the relationship between the fear of missing out and unethical behaviors. McKee et al 22 refer to missing out as the state of chronic feeling of being left out of enjoyable or exciting experiences that colleagues are enjoying.…”

“…This literature revealed that the consequences of misconduct were penalties within academic institutions. 12,15–22…”

“…12 Congruently, McKee et al 22 examined the relationship between the fear of missing out and unethical behaviors. McKee et al 22 refer to missing out as the state of chronic feeling of being left out of enjoyable or exciting experiences that colleagues are enjoying. Concluding that fear is an antecedent for breaching academic integrity and committing cheating.…”

“…Concluding that fear is an antecedent for breaching academic integrity and committing cheating. 22 Literature from the academic world discussing misconduct shared common attributes of unethical behavior and breaching academic integrity. Antecedents discussed as factors of misconduct include lack of guidance, weak adherence to rules and regulations, pressure to earn high grades due to other reasons such as parents, or grant requirements, and peers and faculty dishonest behaviors.…”

“…This literature revealed that the consequences of misconduct were penalties within academic institutions. 12,[15][16][17][18][19][20][21][22] Business. The business world views misconduct as a practice done throughout an entire organization or personal behavior.…”