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The Use of Logistic Regression Analyses and Data Classification Mining to Examine Variables Predictive of Long-term Healthcare Staff Giving Cessation Advice

1 This research was funded in part by NIH (1 R03 CA097742-01 Watt Primary Investigator) Abstract This paper examines variables that might be predictive for healthcare staffs’ advising tobacco cessation to residents via logistical regression analysis and classification data mining. Independent variables that might influence the dependent variable outcome (healthcare workers’ willingness to give advice) are the staffs’ smoking status; professional license or position in the facility, their health beliefs regarding the harmful effects of smoking or environmental smoke, the efficacy of quitting for this older population, their beliefs about safety related to smoking, their views of policy and/or administrative supports or barriers, their opinion about older adults’ quality of life and how cessation might impact it, their stance on resident autonomy issues, and other various barriers to advising. Logistic Regression analysis is the more common method for examining variables predictive of an outcome in social sciences, however data classification can also be used. Findings of this research found parallels across the two types of analyses.