Implementing ODA from Within Stata: Nondirectional Hypothesis, Binary Class Variable, Categorical Ordinal Attribute

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.

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Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, Categorical Ordinal Attribute

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.

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Implementing ODA from Within Stata: Confirmatory Hypothesis, Binary Class Variable, and Ordinal Attribute

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.

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Optimal Weighted Markov Analysis: Predicting Serial RG-Score

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 class variable) and the attribute which is being assessed, thereby maximizing measurement precision, accuracy of prediction, and statistical power.

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Modeling an Individual’s Weekly Change in RG-Score via Novometric Single-Case Analysis

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 the number of citations, recommendations, and article views and downloads. Two analyses were conducted: one forced the model to have stable classification in leave-one-out (LOO) jackknife analysis; the second permitted jackknife instability so long as the LOO Type I error rate was p<0.05. A single-attribute model which achieved relatively strong LOO accuracy was identified.

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Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, and Ordinal (Rank) Attribute

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.

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