Paul R. Yarnold, Ph.D., Nathaniel J. Rhodes, Pharm.D., Ariel Linden, Dr.P.H. Optimal Data Analysis, LLC; Chicago College of Pharmacy, Midwestern University; Linden Consulting Group, LLC Overviews of optimal discriminant analysis (ODA) and novometric theory are presented. Discussion addresses the role of accuracy in translational and precision forecasting research, and of parsimony in theoretical research; the … Continue reading Overview of the Optimal Discriminant Analysis and Novometric Paradigm
Category: Volume 10, Release 1
Using ODA to Estimate Propensity-Weight-Adjusted Treatment Effects for Multi-Valued Treatments
Paul R. Yarnold, Ph.D., Fred B. Bryant, Ph.D., Ariel Linden, Dr.P.H. Optimal Data Analysis, LLC, Loyola University Chicago, Linden Consulting Group, LLC We demonstrate the use of optimal data analysis to obtain a hierarchically optimal classification tree-based propensity score model for an application with three (treatment) groups, and to assess outcome differences between treatment groups … Continue reading Using ODA to Estimate Propensity-Weight-Adjusted Treatment Effects for Multi-Valued Treatments
Implementing ODA from Within Stata: Evaluating Test-Retest Reliability of Positive and Negative Emotional States, vs. Personality Traits, Assessed Using Likert Scales, for Males vs. Females
Paul R. Yarnold, Ph.D. and Ariel Linden, Dr.P.H. Optimal Data Analysis, LLC and Linden Consulting Group, LLC This paper illustrates testing directional hypotheses for test-retest Likert ratings of positive and negative emotional states and personality traits for males and females, using the Stata package for implementing ODA. View journal article
Executing ODA from Within Stata: Combining Random Forests and ODA to Estimate Treatment Effects for Multi-Valued Treatments (Invited)
Ariel Linden, Dr. P.H. Linden Consulting Group, LLC This paper demonstrates how the random forest algorithm can be used in conjunction with ODA to estimate treatment effects for multivalued treatments using the new Stata package for implementing ODA. View journal article
Implementing ODA from Within Stata: Assessing Split-Half Reliability Using a Polychotomous Attribute
Paul R. Yarnold, Ph.D. and Ariel Linden, Dr. P.H. Optimal Data Analysis, LLC & Linden Consulting Group, LLC This paper illustrates testing a directional (i.e., confirmatory) hypotheses for a split-half reliability study using a polychotomous attribute having four categories, via the Stata package for implementing ODA. View journal article
Implementing ODA from Within Stata: Assessing Parallel-Forms Reliability Using a Binary and an Ordered Attribute
Paul R. Yarnold, Ph.D. and Ariel Linden, Dr. P.H. Optimal Data Analysis, LLC & Linden Consulting Group, LLC This paper illustrates testing a directional (i.e., confirmatory) hypotheses for a parallel-forms reliability study using a binary and an ordered measure, via the Stata package for implementing ODA. View journal article
Executing ODA from Within Stata: Combining Boosted Regression and ODA to Estimate Treatment Effects for Multi-Valued Treatments
Ariel Linden, Dr. P.H. and Paul R. Yarnold, Ph.D. Linden Consulting Group, LLC & Optimal Data Analysis, LLC This paper demonstrates how boosted regression can be used in conjunction with ODA to estimate treatment effects for multivalued treatments using the new Stata package for implementing ODA. View journal article
Implementing ODA from Within Stata: Confirmatory and Exploratory Inter-Rater Reliability Hypothesis with a Three-Category Ordinal Rating
Paul R. Yarnold, Ph.D. and Ariel Linden, Dr. P.H. Optimal Data Analysis, LLC & Linden Consulting Group, LLC This paper illustrates testing directional (confirmatory) and non-directional (exploratory) hypotheses for an inter-rater reliability study using a three-category ordinal measure, via the Stata package for implementing ODA. View journal article
Implementing ODA from Within Stata: Exploratory Hypothesis, Three-Category Class Variable, Continuous Attribute
Paul R. Yarnold, Ph.D. and Ariel Linden, Dr. P.H. Optimal Data Analysis, LLC & Linden Consulting Group, LLC This paper describes how to test a non-directional (exploratory) hypothesis for a design relating a three-category class (“dependent”) variable and a continuous attribute vis-à-vis the Stata package for implementing ODA. View journal article
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