Paul R. Yarnold Optimal Data Analysis LLC This note interprets a novometric descendant family (DF) as an optimal “cluster” analysis indicating number of discriminable groups and strength of their differences at every differentiable point identified for the sample. View journal article

Paul R. Yarnold Optimal Data Analysis LLC This note updates current MultiODA research activity in the ODA lab. View journal article

Paul R. Yarnold Optimal Data Analysis LLC Novometric (Latin: new measure) statistical theory is introduced. View journal article

Nathaniel J. Rhodes Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University I study the role of the random seed number in affecting the reliability of a statistical finding, which in turn determines upper and lower bounds of statistical confidence in expected gain or loss yielded from associated decision-making. Simulation research reveals … Continue reading Assessing Reproducibility of Novometric Bootstrap Confidence Interval Analysis Using Multiple Seed Numbers (Invited)

Ariel Linden Linden Consulting Group, LLC Data from the National Supported Work (NSW) randomized experiment have been used frequently over the past 30 years to demonstrate the implementation of various non-experimental methods for drawing causal inferences about treatment effects. The present paper reassesses the approach used by Dehejia and Wahba (2002) for estimating propensity scores … Continue reading Implementing CTA from Within Stata: Reassessing the Propensity Score Estimation Approach Used in the National Supported Work Experiment (Invited)

Ariel Linden & Paul R. Yarnold Linden Consulting Group LLC & Optimal Data Analysis LLC Data from the National Supported Work (NSW) randomized experiment have been used frequently over the past 30 years to demonstrate imple­mentation of various non-experimental methods for drawing causal infer­ences about treatment effects. In this paper we reanalyze these data using the … Continue reading Implementing ODA from Within Stata: A Reanalysis of the National Supported Work Experiment

Nathaniel J. Rhodes and Paul R. Yarnold Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University & Optimal Data Analysis LLC We introduce a method for evaluating the upper and lower bounds of statistical confidence [e.g., an exact discrete confidence interval (CI)] in the expected effect strength for sensitivity of a decision … Continue reading Generating Novometric Confidence Intervals in R: Bootstrap Analyses to Compare Model and Chance ESS

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to test a directional (confirmatory) hypothesis for a design relating a three-category class (“dependent”) variable and a four-level categorical ordinal attribute (“Likert-type independent variable”) vis-à-vis the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to assess a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and an ordinal attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper demonstrates how to evaluate a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to evaluate an exploratory (nondirectional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how a confirmatory (a priori, directional, one-tailed) hypothesis involving a binary (dichotomous) class variable and continuous (interval or ratio) attribute is evaluated via MegaODA software using the new Stata package implementing ODA analysis. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how a confirmatory (a priori, directional, one-tailed) hypothesis involving a binary (dichotomous) class variable and a five-level ordinal attribute is evaluated using MegaODA software via the new Stata package implementing ODA analysis. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (post hoc, nondirectional, two-tailed) hypothesis involving a binary (dichotomous) class variable and a categorical ordinal (three-level) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (post hoc, nondirectional, two-tailed) hypothesis involving a binary (dichotomous) class variable and a categorical ordinal (three-level) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how a confirmatory (a priori, directional, one-tailed) hypothesis involving a binary (dichotomous) class variable and an ordinal (quintiles) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This study predicts change in magnitude of weekly RG-scores recorded over a year for a single individual. Attributes include weekly numbers of citations, recommendations, reads, and full-text downloads. Events in the transition table are weighted by the absolute product of ipsative standard scores for RG-score (treated as the … Continue reading Optimal Weighted Markov Analysis: Predicting Serial RG-Score

Paul R. Yarnold Optimal Data Analysis, LLC The study explores the serial nature of weekly RG-score values for a single individual over the period of one year. Events in the transition table are weighted by their corresponding change-in-value, thereby maximizing measurement precision and model accuracy. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC Research Gate (RG) weekly summary statistics—including RG-score (the class or “dependent variable”), and number of citations, recommendations, and article views and downloads (the attributes or “independent variables”), were obtained for a single user. Single-case novometric classification tree analysis (CTA) was used to predict RG-score as a function of … Continue reading Modeling an Individual’s Weekly Change in RG-Score via Novometric Single-Case Analysis

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (i.e., post hoc, nondirectional, two-tailed) hypothesis involving a binary (i.e., dichotomous) class variable and an ordinal (rank) attribute is evaluated using MegaODA software vis-à-vis the new Stata package implementing ODA analysis. View journal article