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Mangino, Anthony A.; Smith, Kendall A.; Finch, W. Holmes; Hernández-Finch, Maria E. – Measurement and Evaluation in Counseling and Development, 2022
A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network.
Descriptors: Prediction, Suicide, Artificial Intelligence, Networks
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Fox, Rina S.; Merz, Erin L.; Solórzano, Martha T.; Roesch, Scott C. – Measurement and Evaluation in Counseling and Development, 2013
This study used latent profile analysis (LPA) to identify acculturation profiles. A three-profile solution fit the data best, and comparisons on demographic and psychosocial outcomes as a function of profile yielded expected results. The findings support using LPA as a parsimonious way to model acculturation without anticipating profiles in…
Descriptors: Statistical Analysis, Profiles, Acculturation, Comparative Analysis
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Kim, Seong-Hyeon; Beretvas, S. Natasha; Sherry, Alissa R. – Measurement and Evaluation in Counseling and Development, 2010
This study investigated the Outcome Questionnaire's (OQ-45) factor structure and demonstrated the use of factor mixture modeling (FMM) for the purpose of score validation. OQ-45 scores did not fit the one-class, one- and three-factor models. Use of FMM to identify a two-class model is detailed. Implications for OQ-45 users are provided. (Contains…
Descriptors: Validity, Factor Structure, Factor Analysis, Scores
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Jones, W. Paul – Measurement and Evaluation in Counseling and Development, 1993
Investigated model for reducing time for administration of Myers-Briggs Type Indicator (MBTI) using real-data simulation of Bayesian scaling in computerized adaptive administration. Findings from simulation study using data from 127 undergraduates are strongly supportive of use of Bayesian scaled computerized adaptive administration of MBTI.…
Descriptors: Bayesian Statistics, Classification, College Students, Computer Assisted Testing