Using Exploratory Data Mining to Identify Important Correlates of Nonsuicidal Self-Injury Frequency.

Abstract

Non-suicidal self-injury (NSSI) has been linked to many adverse outcomes, with more frequent NSSI increasing the likelihood of impairment, severity, and more serious self-harming behavior (e.g., suicidality; Andover & Gibb, 2010; Darke et al., 2010). Despite the determined importance of NSSI frequency in understanding the severity of one's behavior, there is still a need to identify which constructs may be influential in predicting frequency. The current study aimed to fill this gap by identifying which correlates are most important in relation to NSSI frequency through two exploratory data mining methods. Seven hundred twelve undergraduate students with a history of NSSI completed self-report measures of NSSI behavior, suicidality, cognitive-affective deficits, and psychopathology symptomology. Both exploratory data mining methods, lasso regression and random forests, demonstrated number of NSSI methods to be the factor with the most importance in relation to lifetime NSSI frequency. Once this variable was removed, suicide plan and depressive symptomology were significant correlates across methods. The current findings support the literature between NSSI frequency and NSSI methods, but also implicate suicide plans, an often-overlooked factor, and depression in NSSI severity.