Manual vs. Automated CTA: Predicting Freshman Attrition

Manual vs. Automated CTA: Predicting Freshman Attrition

Paul R. Yarnold, Ph.D., Fred B. Bryant, Ph.D., and Jennifer Howard Smith, Ph.D.

Optimal Data Analysis, LLC, Loyola University Chicago, Applied Research Solutions, Inc.

The enumerated model was 20% more accurate, but 43% less parsimonious and 31% less efficient than the manually-derived model. Granularity afforded by the enumerated model enabled prediction of seven of eight incoming freshmen who left college. Substantive, policy, and methodological implications are considered.

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Comparing Knot Strength Using UniODA

Comparing Knot Strength Using UniODA

Paul R. Yarnold, Ph.D. and Gordon C. Brofft, BS

Optimal Data Analysis, LLC  and Marine and Water Consultant

This study assessed comparative strength of three versatile knots widely used in big-game fishing. Experiment One compared Uni and San Diego knots tied in 30-, 40- and 50-pound-test monofilament line (the modal strengths), finding no statistically significant differences in knot strength. Experiment Two attached 40-pound-test monofilament line to 50- and 65-pound-test solid spectra, and to 60-pound-test hollow spectra line using a
Double Uni knot, and found the 40-to-65 connection was strongest. High levels of variation in knot strength which were observed raises concern about the durability and consistency of monofilament line.

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The Loyola Experience (1993-2009): Optimal Data Analysis in the Department of Psychology

The Loyola Experience (1993-2009): Optimal Data Analysis in the Department of Psychology

Fred B. Bryant, Ph.D.

Loyola University Chicago

This article traces the origins and development of the use of optimal data analysis (ODA) within the Department of Psychology at Loyola University Chicago over the past 17 years. An initial set of ODA-based articles by Loyola faculty laid the groundwork for a sustained upsurge in the use of ODA among graduate students which has lasted for more than a decade and a half. These student projects subsequently fueled an increase in ODA-based publications by other Loyola Psychology faculty, who directly supervised the various student projects. Thus, ODA initially trickled down from faculty to students, but later grew up in the opposite direction. The most frequent use of ODA in Loyola’s Psychology Department has been to conduct classification tree analysis, with less common uses of ODA including optimal discriminant analysis and the iterative structural decomposition of transition tables. As more Loyola Psychology graduate students find academic jobs and continue using ODA in their research, we expect that they will replicate the Loyola experience in these new academic settings.

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Optimal Data Analysis: A General Statistical Analysis Paradigm

Optimal Data Analysis: A General Statistical Analysis Paradigm

Paul R. Yarnold, Ph.D., and Robert C. Soltysik, M.S.

Optimal Data Analysis, LLC

Optimal discriminant analysis (ODA) is a new paradigm in the general statistical analysis of data, which explicitly maximizes the accuracy achieved by a model for every statistical analysis, in the context of exact distribution theory. This paper reviews optimal
analogues of traditional statistical methods, as well as new special-purpose models for which no conventional alternatives exist.

Author’s Note: This paper reviews initial discoveries of the ODA paradigm. Here is a current review: https://odajournal.com/2017/04/18/what-is-optimal-data-analysis/

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Maximizing Accuracy of Classification Trees by Optimal Pruning

Maximizing Accuracy of Classification Trees by Optimal Pruning

Paul R. Yarnold, Ph.D., and Robert C. Soltysik, M.S.

Optimal Data Analysis, LLC

We describe a pruning methodology which maximizes effect strength for sensitivity of classification tree models. After deconstructing the initial “Bonferroni-pruned” model into all possible nested sub-branches, the sub-branch which explicitly maximizes mean sensitivity is identified. This methodology is illustrated using models predicting in-hospital mortality of 1,193 (Study 1) and 1,660 (Study 2) patients with AIDS-related Pneumocystis carinii pneumonia.

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Two-Group MultiODA: A Mixed-Integer Linear Programming Solution with Bounded M

Two-Group MultiODA: A Mixed-Integer Linear Programming Solution with Bounded M

Robert C. Soltysik, M.S., and Paul R. Yarnold, Ph.D.

Optimal Data Analysis, LLC

Prior mixed-integer linear programming procedures for obtaining two-group multivariable optimal discriminant analysis (MultiODA) models require estimation of the value of a parameter, M. A new formulation is presented which establishes a lower bound for M, which executes more quickly than prior formulations. A sufficient condition for the nonexistence of classification gaps and ambiguous solutions, optimal weighted classification, use of nonlinear terms, selecting an optimal subset of attributes, and aggregation of duplicate observations are discussed. When the design involves six or fewer binary attributes, MultiODA models may easily be obtained for massive samples.

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