Using Classification and Regression Trees (CART) to Support Worker Decision Making

Article excerpt

Several approaches can be taken to predict case membership in the classes of a dependent variable. Classification and regression trees (CART) analysis has been cited repeatedly as a powerful nonparametric approach in fields where classification or prediction are of concern. To test CART's utility in a social work setting, the authors conducted a secondary analysis of data collected in a national study of child protective services screening practices to identify factors involved with worker decisions to investigate child maltreatment reports. The CART analysis revealed complex interaction effects previously unobserved in the logistic regression. Comparisons of CART with traditional statistical approaches and other tree-based programs are presented.

Key words: classification and regression trees; decision trees; decision making; screening; child protective services

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Depending on the research question, the basic purpose of a classification study is either to produce an accurate classifier or to uncover the predictive structure of the phenomenon under consideration (Breiman, Friedman, Olshen, & Stone, 1984). For most social work professionals, both objectives are of interest; for example, to target resources, a program planner must be able to identify groups of clients that are likely to benefit from a specific approach and to understand the factors that predict the likelihood of success given the client's presenting conditions. Similarly, when a social worker recommends care alternatives, prediction of outcome given the client's condition, available resources, and the factors expected to influence rehabilitation are necessary to appropriately assist the client and family in their decision making. Yet, many social work professionals are faced with complex decision problems without the benefit of a set of rules to organize data. In these situations, most decision makers tend to polarize around only a few variables, potentially missing important aspects of a problem.

Although a variety of traditional statistical approaches can be used to predict the classification of cases from complex data sets, classification and regression trees (CART) analysis (Breiman et al., 1984) has been cited repeatedly as a powerful nonparametric approach in applied fields where classification or prediction are of concern, such as medicine (for example, Goldman et al., 1998; Mair, Smidt, Lechleitner, Dienstl, & Puschendorf, 1998; Thomssen et al., 1998) and mental health (Barnes, Welte, & Dintcheff, 1991; Boerstler & de Figueiredo, 1991; Craig, Siegel, Hopper, Lin, & Sartorius, 1997). For example, in a study of low-income psychiatric patients, Boerstler and de Figueiredo found the client's discharge from inpatient treatment at the most recent admission to psychiatric treatment to be "the most consistent, most powerful, and the only necessary predictor of high use of outpatient psychiatric services" (p. 32); an important implication for program administrators. Mair et al. (1995) used CART to develop an algorithm for use in emergency room settings for the early diagnosis of heart attack based on clinical symptoms, ECG, and other myocardial measures from 114 patients. The method's ability to predict a diagnosis was as high as that of other statistical methods; however, CART's graphical features, essential for use in clinical training and practice, were cited as a primary advantage over other methods.

To demonstrate CART's potential for use in social work settings, this article presents the CART technique, its utility in identifying factors involved with decisions to investigate reports of child maitreatment, and comparisons of CART with traditional statistical approaches and other tree-based software programs.

BACKGROUND

In response to the growing discrepancy between the number of reports made to child protective services (CPS) and the number of reports investigated, the Children's Bureau funded an on-site study of CPS screening practices in 12 communities from five states to illustrate worker decision-making practices at intake (Wells, Fluke, Downing, & Brown, 1989). …