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Supplemental material, sj-pdf-1-cpx-10.1177_2167702620954216 for Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research by Ross Jacobucci, Andrew K. Littlefield, Alexander J. Millner, Evan M. Kleiman and Douglas Steinley in Clinical Psychological Science

Supplemental material, sj-pdf-1-cpx-10.1177_2167702620954216 for Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research by Ross Jacobucci, Andrew K. Littlefield, Alexander J. Millner, Evan M. Kleiman and Douglas Steinley in Clinical Psychological Science

Supplemental material, sj-pdf-1-cpx-10.1177_2167702620961067 for Machine Learning to Classify Suicidal Thoughts and Behaviors: Implementation Within the Common Data Elements Used by the Military Suicide Research Consortium by Andrew K. Littlefield, Jeffrey T. Cooke, Courtney L. Bagge, Catherine R. Glenn, Evan M. Kleiman, Ross Jacobucci, Alexander J. Millner and Douglas Steinley in Clinical Psychological Science

Supplemental material, Supplementary_material for Item Responses in Quantity–Frequency Questionnaires: Implications for Data Generalizability by Jordan E. Stevens, Emilie Shireman, Douglas Steinley, Thomas M. Piasecki, Daniel Vinson and Kenneth J. Sher in Assessment

This study used a person-centered approach to test whether drinking motive typologies could be identified.Longitudinal study of college students within the intensive multivariate prospective alcohol college-transitions (IMPACTS) data set.University campus in the United States.University students (baseline n reporting alcohol motives = 2158; baseline age = 18.60 years old).The drinking motives questionnaire-revised (DMQ-R).Using SteinleyBrusco's cluster analysis approach [based on the theoretical ratio expected between the within sum of squares and the total sum of squares when the data are divided into two clusters when no cluster structure is present; the cut-off for the ratio is 0.25 for uniform (multivariate uniform) distributions and 0.36 for normal (multivariate normal) distributions], we examined whether there was evidence for distinct clusters of individuals that differed on their overall level of motives to drink. We tested the fit of a one-group (cluster) solution compared to multi-cluster solutions. Both cross-sectionally and prospectively, the data could not be partitioned into two or more clusters [regardless of whether the cut-off assuming a multivariate uniform distribution (i.e. 0.25) or the more liberal multivariate normal distribution (i.e., 0.36) was used]. These findings showed that enhancement and coping drinkers do not form two distinct groups but, rather, these motives exist on a continuum such that individuals who are high in one internal motive tend to be high in the other motive.Coping and enhancement drinkers do not form two distinct groups. Variable-centered approaches to drinking motives may be a better alternative to classifying all drinkers as either enhancement or coping drinkers for both clinical and research endeavors.