Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC UniODA models maximize Effect Strength for Sensitivity (ESS), a normed measure of classification accuracy (0=chance, 100=perfect classification) that indexes the models ability to accurately identify the members of different class categories in the sample. In a study discriminating genders, for example, the percent of each gender accurately … Continue reading Standards for Reporting UniODA Findings Expanded to Include ESP and All Possible Aggregated Confusion Tables

Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC The use of optimal data analysis (ODA) in making a map reporting the findings of confirmatory statistical analyses is demonstrated by comparing the annual crude mortality rate in counties of North Dakota, before versus after large-scale commercial usage of toxic chemicals and biocides in the environment began … Continue reading Statistically Significant Increases in Crude Mortality Rate of North Dakota Counties Occurring After Massive Environmental Usage of Toxic Chemicals and Biocides Began There in 1998: An Optimal Static Statistical Map

Paul R. Yarnold & Robert C. Soltysik Optimal Data Analysis, LLC An omnipresent experimental method in all quantitative scientific disciplines involves what is commonly called, for example, a time-series, repeated measures, clinical trial, test-retest, longitudinal, prospective, pre-post, AB, or, more generally, a serial design. In a serial design each observation is assessed on a measure … Continue reading Ipsative Standardization is Essential in the Analysis of Serial Data

Paul R. Yarnold Optimal Data Analysis, LLC An incontrovertible advantage of establishing a minimum set of standards for reporting findings obtained using any method—is researchers from all fields will be able to easily and clearly understand fundamental statistical results of any study reporting findings using that method. This note extends minimum standards proposed for reporting … Continue reading Minimum Standards for Reporting UniODA Findings for Class Variables with Three or More Response Categories

Paul R. Yarnold Optimal Data Analysis, LLC This study extends recent quality-of-care research undertaken to enhance understanding of ratings of overall satisfaction with care received as an Emergency Room (ER) patient. Multiple regression analysis, optimized by UniODA to maximize predictive accuracy, was used to separately evaluate the ability of ratings of technicians (n=535), nurses (n=1,800) … Continue reading Assessing Technician, Nurse, and Doctor Ratings as Predictors of Overall Satisfaction of Emergency Room Patients: A Maximum-Accuracy Multiple Regression Analysis

Paul R. Yarnold Optimal Data Analysis, LLC Quality improvement research conducted in an effort to obtain and maintain the maximum possible level of patient satisfaction is an on-going process at many hospitals. This study analyzes survey data from 773 patients receiving care at a private Midwestern hospital’s Emergency Room (ER). Multiple regression analysis, optimized by … Continue reading Maximum-Accuracy Multiple Regression Analysis: Influence of Registration on Overall Satisfaction Ratings of Emergency Room Patients

Paul R. Yarnold Optimal Data Analysis, LLC Statistical hypotheses investigated by researchers representing a vast domain of empirical disciplines often involve discrimination and prediction of discrete outcomes: life versus death in medicine, innocent versus guilty in law, profit versus loss in finance, victory versus defeat in military science, etcetera. Although some studies feature large samples … Continue reading UniODA and Small Samples

Paul R. Yarnold & Fred B. Bryant Optimal Data Analysis, LLC & Loyola University Chicago Attributes measured on a categorical response scale are common in the literature. Categorical scales for attributes such as, for example, political affiliation, ethnic origin, marital status, state of residence, or diagnosis may consist of many qualitative response categories. Such disorganized … Continue reading Analysis Involving Categorical Attributes Having Many Response Categories

Paul R. Yarnold Optimal Data Analysis, LLC As the number of researchers using any statistical method and the domain of disciplines they represent increases, the opportunity for and likelihood of the development of disparate traditions for the reporting of analytic findings also increases. The great advantage of establishing a minimum set of standards for reporting … Continue reading Minimum Standards for Reporting UniODA Findings

How to Create an ASCII Input Data File for UniODA and CTA Software Fred B. Bryant & Patrick R. Harrison Loyola University Chicago UniODA and CTA software require an ASCII (unformatted text) file as input data. Arguably the most difficult task an operator faces in conducting analyses is converting the original data file from (a) … Continue reading How to Create an ASCII Input Data File for UniODA and CTA Software

Initial Use of Hierarchically Optimal Classification Tree Analysis In Medical Research Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC Initial use of hierarchically optimal classification tree analysis in medicine and allied health disciplines is reviewed. View journal article

Maximizing the Accuracy of Multiple Regression Models using UniODA: Regression Away From the Mean Paul R. Yarnold, Ph.D., Fred B. Bryant, Ph.D., and Robert C. Soltysik, M.S. Optimal Data Analysis, LLC and Loyola University Chicago Standard regression models best predict values that lie near the mean. Three examples illustrate how optimization of the regression model … Continue reading Maximizing the Accuracy of Multiple Regression Models using UniODA: Regression Away From the Mean

Statistical Power of Optimal  Discrimination with a Normal Attribute and Two Classes: One-Tailed Hypotheses Robert C. Soltysik, M.S., and Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC This note reports statistical power (1-β) obtained by ODA when used with a normally-distributed attribute, as a function of alpha and effect size. View journal article

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