Precision and Convergence of Monte Carlo Estimation of Two-Category UniODA Two-Tailed p

Precision and Convergence of Monte Carlo Estimation of Two-Category UniODA Two-Tailed p

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

Optimal Data Analysis, LLC

Monte Carlo (MC) research was used to study precision and convergence properties of MC methodology used to assess Type I error in exploratory (post hoc, or two-tailed) UniODA involving two balanced (equal N) classes. Study 1 ran 106 experiments for each N, and estimated cumulative p’s were compared with corresponding exact p for all known p values. Study 2 ran 105 experiments for each N, and observed the convergence of the estimated p’s. UniODA cumulative probabilities estimated using 105 experiments are only modestly less accurate than probabilities estimated using 106 experiments, and the maximum observed error (±0.002) is small. Study 3 ran 105 experiments for Ns ranging as high as 8,000 observations in order to examine asymptotic properties of optimal values for balanced designs.

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Aggregated vs. Referenced Categorical Attributes in UniODA and CTA

Aggregated vs. Referenced Categorical Attributes in UniODA and CTA 

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

Optimal Data Analysis, LLC

Multivariable linear methods such as logistic regression analysis, discriminant analysis, or multiple regression analysis, for example, directly incorporate binary categorical attributes into their solution. However, for categorical attributes having more than two levels, each level must first be individually dummy-coded, then one level must be selected for use as a reference category and omitted from analysis. Selection of one or another level as the reference category can mask effects which otherwise would have materialized, if a different level had been chosen. Neither UniODA nor CTA require reference categories in analysis using multicategorical attributes.

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Manual vs. Automated CTA: Optimal Preadmission Staging for Inpatient Mortality from Pneumocystis cariini Pneumonia

Manual vs. Automated CTA: Optimal Preadmission Staging for Inpatient Mortality from Pneumocystis cariini Pneumonia

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

Optimal Data Analysis, LLC

Two severity-of-illness models used for staging risk of in-hospital mortality from AIDS-related Pneumocystis cariini pneumonia (PCP) were developed using hierarchically optimal classification tree analysis (CTA), with models derived manually via UniODA
software. The first of the “Manual vs. Automated CTA” series, this study contrasts classification results between original models and corresponding new models derived using automated analysis. Findings provide superior staging systems which may be employed to improve results of applied research in this area.

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Manual vs. Automated CTA: Psychosocial Adaptation in Young Adolescents

Manual vs. Automated CTA: Psychosocial Adaptation in Young Adolescents

Rachael Millstein Coakley, Ph.D., Grayson N. Holmbeck,Ph.D., Fred B. Bryant, Ph.D., and Paul R. Yarnold, Ph.D.

Children’s Hospital, Boston / Harvard Medical School, Loyola University Chicago,  Optimal Data Analysis, LLC

Compared to the manually-derived model, the enumerated CTA model was 20% more parsimonious, 3.6% more accurate and 30% more efficient, and was more consistent with a priori hypotheses.

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Gen-UniODA vs. Log-Linear Model: Modeling Organizational Discrimination

Gen-UniODA vs. Log-Linear Model: Modeling Organizational Discrimination 

Paul R. Yarnold, Ph.D. and Robert C. Soltysik, M.S.
Optimal Data Analysis, LLC

An application involving a binary class variable (gender), an ordinal attribute (academic rank), and two testing periods (separated by six years) was troublesome for the log-linear model, but was easily analyzed using Gen-UniODA.

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UniODA vs. Chi-Square: Ordinal Data Sometimes Feign Categorical

UniODA vs. Chi-Square: Ordinal Data Sometimes Feign Categorical

Paul R. Yarnold, Ph.D. and Robert C. Soltysik, M.S.
Optimal Data Analysis, LLC

Assessed using perhaps the most widely used type of measurement scale in all science, ordinal data are often misidentified as being categorical, and incorrectly analyzed by chi-square analysis. Three examples drawn from the literature are reanalyzed.

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The Use of Unconfounded Climatic Data Improves Atmospheric Prediction

The Use of Unconfounded Climatic Data Improves Atmospheric Prediction  

Robert C. Soltysik, M.S., and Paul R. Yarnold, Ph.D.
Optimal Data Analysis, LLC

This report improves measurement properties of data and analytic methods widely used in meteorological modeling and forecasting. Paradoxical confounding is defined and demonstrated using global temperature land-ocean index data. It is shown that failure to address paradoxical confounding results in suboptimal atmospheric circulation pattern models, and correcting prior measurement and analytic deficiencies results in more accurate prediction of temperature and precipitation anomalies, and export of Arctic sea ice.

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