Assessing Hold-Out Validity of CTA Models Using UniODA  Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC The use of UniODA software to assess hold-out validity of a CTA model is demonstrated using both raw and standardized data. View journal article

Modeling Individual Reactivity in Serial Designs: Changes in Weather and Physical Symptoms in Fibromyalgia Paul R. Yarnold, Ph.D., Robert C. Soltysik, M.S., and William Collinge, Ph.D. Optimal Data Analysis, LLC and Collinge and Associates This note criticizes current statistical convention, and discusses and illustrates appropriate statistical methodology for investigating the relationship between weather and individual … Continue reading Modeling Individual Reactivity in Serial Designs: Changes in Weather and Physical Symptoms in Fibromyalgia

Reverse CTA Versus Multiple Regression Analysis  Paul R. Yarnold, Ph.D. and Robert C. Soltysik, M.S. Optimal Data Analysis, LLC This paper illustrates how to reverse CTA for applications having an ordered class variable and categorical attributes. Whereas a regression model is used to make point predictions for the dependent measure based on values of the … Continue reading Reverse CTA Versus Multiple Regression Analysis

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 … Continue reading Manual vs. Automated CTA: Predicting Freshman Attrition

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 … Continue reading Comparing Knot Strength Using UniODA

Percent Oil-Based Energy Consumption and Average Percent GDP Growth: A Small Sample UniODA Analysis Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC UniODA is used to evaluate an a priori hypothesis involving an ordered attribute and two groups each having N=4 observations. View journal article

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

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 … Continue reading Optimal Data Analysis: A General Statistical Analysis Paradigm

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 … Continue reading Maximizing Accuracy of Classification Trees by Optimal Pruning

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

Unconstrained Covariate Adjustment in CTA  Paul R. Yarnold, Ph.D. and Robert C. Soltysik, M.S. Optimal Data Analysis, LLC In traditional statistical covariate analysis it is common practice to force entry of the covariate into the model first, to eliminate the effect of the covariate (i.e., “equate the groups”) on the dependent measure. In contrast, in … Continue reading Unconstrained Covariate Adjustment in CTA

Maximizing the Accuracy of Probit Models via UniODA  Barbara M. Yarnold, J.D., Ph.D. and Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC Paralleling the procedure used to maximize ESS of linear models derived using logistic regression analysis or Fisher’s discriminant analysis, univariate optimal discriminant analysis (UniODA) is applied to the predicted response function values provided … Continue reading Maximizing the Accuracy of Probit Models via UniODA

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

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, … Continue reading Aggregated vs. Referenced Categorical Attributes in UniODA and CTA

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

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% … Continue reading Manual vs. Automated CTA: Psychosocial Adaptation in Young Adolescents

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. View journal … Continue reading Gen-UniODA vs. Log-Linear Model: Modeling Organizational Discrimination

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

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 … Continue reading The Use of Unconfounded Climatic Data Improves Atmospheric Prediction

Here Today, Gone Tomorrow: Understanding Freshman Attrition Using Person-Environment Fit Theory Jennifer Howard Smith, Ph.D., Fred B. Bryant, Ph.D., David Njus, Ph.D., and Emil J. Posavac, Ph.D. Applied Research Solutions, Inc., Loyola University Chicago, Luther College, Loyola University Chicago (Emeritus) Person-Environment (PE) fit theory was used to explore the relationship between student involvement and freshman retention. Incoming … Continue reading Here Today, Gone Tomorrow: Understanding Freshman Attrition Using Person-Environment Fit Theory