Data Mining in Psychological Treatment Research: A Primer on Classification and Regression Trees

Data mining of treatment study results can reveal unforeseen but critical insights, such as who receives the most benefit from treatment and under what circumstances. The usefulness and legitimacy of exploratory data analysis have received relatively little recognition, however, and analytic methods...

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Bibliographic Details
Published in:Journal of consulting and clinical psychology 2014-10, Vol.82 (5), p.895-905
Main Authors: King, Matthew W, Resick, Patricia A
Format: Article
Language:English
Subjects:
Algorithms
Classification
Data Mining
Decision making models
Female
Human
Humans
Models, Theoretical
Psychiatry
Psychodiagnostic Typologies
Psychology
Psychotherapy
Regression Analysis
Research methodology
Statistical Analysis
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Summary:Data mining of treatment study results can reveal unforeseen but critical insights, such as who receives the most benefit from treatment and under what circumstances. The usefulness and legitimacy of exploratory data analysis have received relatively little recognition, however, and analytic methods well suited to the task are not widely known in psychology. With roots in computer science and statistics, statistical learning approaches offer a credible option: These methods take a more inductive approach to building a model than is done in traditional regression, allowing the data greater role in suggesting the correct relationships between variables rather than imposing them a priori. Classification and regression trees are presented as a powerful, flexible exemplar of statistical learning methods. Trees allow researchers to efficiently identify useful predictors of an outcome and discover interactions between predictors without the need to anticipate and specify these in advance, making them ideal for revealing patterns that inform hypotheses about treatment effects. Trees can also provide a predictive model for forecasting outcomes as an aid to clinical decision making. This primer describes how tree models are constructed, how the results are interpreted and evaluated, and how trees overcome some of the complexities of traditional regression. Examples are drawn from randomized clinical trial data and highlight some interpretations of particular interest to treatment researchers. The limitations of tree models are discussed, and suggestions for further reading and choices in software are offered.
ISSN:0022-006X
1939-2117
DOI:10.1037/a0035886