Applying data mining to explore the risk factors of parenting stress

Hsiang Yang Chen, Ting Wei Hou, Chao Hua Chuang

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


High parenting stress has been shown to be associated with illness, poor marital relationships, and child abuse. It is thus important to detect and reduce the stress early. Numerous situational, socioeconomic, child, and parent factors contribute to parenting stress. However, regression analysis, the traditional method of exploring risk factors in medicine and social science, has the limitation of not showing the classification, nor exploring unknown potentional factors. Hence, the goal of this study is to explore the risk factors of parenting stress using data mining with decision tree C5.0, to obtain more information. The data are from a professional research group, TBPS, in the National Taiwan University. A total of 206 mother-term born child dyads were recruited to complete the measures of the Parenting Stress Index (PSI), the Child Behaviour Checklist (CBCL), the Comprehensive Developmental Inventory for Infants and Toddlers (CDIIT), and the Chinese Toddler Temperament Scale (CTTS), and so this database includes thousands of variables. The study results indicate that a child development problem, CDIIT, is the major contributing factor to parents with the highest stress, the 90% parenting stress group. For the 80%, 70%, and 60% parenting stress groups, the behavioural problem of children, CBCL, is the major factor causing parenting stress. The data mining decision tree showing the classification route of risk factors is better than the regression model at detecting the significant factors. The findings in this work are considered helpful references for medical staff and social workers to help parents prevent and reduce their parenting stress and thus promote health.

Original languageEnglish
Pages (from-to)598-601
Number of pages4
JournalExpert Systems With Applications
Issue number1
Publication statusPublished - 2010 Jan

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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