Using Exploratory Data Mining to Identify Important Correlates of Nonsuicidal Self-Injury Frequency
Using Exploratory Data Mining to Identify Important Correlates of Nonsuicidal Self-Injury Frequency
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DOI:
10.1037/vio0000146
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发表时间:
2018-07-01
影响因子:
2.8
通讯作者:
McCloskey, Michael S.
中科院分区:
文献类型:
--
作者:
Ammerman, Brooke A.;Jacobucci, Ross;McCloskey, Michael S.
Objective: Nonsuicidal 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). 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 2 exploratory data mining methods. Method: Seven hundred twelve undergraduate students with a history of NSSI completed self-report measures of NSSI behavior, suicidality, cognitive-affective deficits, and psychopathology symptomology. Results: 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. Conclusions: The current findings support the literature concerning the relationship between NSSI frequency and NSSI methods but also implicate suicide plans, an often-overlooked factor, and depression in NSSI severity.