Paul R. Yarnold Optimal Data Analysis, LLC Two prediction-interval scaling strategies are illustrated for a 10-point ordinal attribute: one strategy compresses the most extreme scores, and the other strategy compresses the least extreme scores on the scale. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This first correspondence note addresses the question of how one may obtain a personal copy of ODA, MegaODA and/or CTA software. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC For class variables with two categories, ODA and MegaODA software employ Fisher’s one-tailed exact test to assess p associated with LOO classification performance. For class variables having three or more categories, LOO p is not provided. This article discusses how to use ODA and MegaODA software to obtain … Continue reading Obtaining LOO p in Analysis Involving Three or More Class Categories

Paul R. Yarnold Optimal Data Analysis, LLC ANOVA with three between-groups factors is used to compare an ordered attribute (score) between four independent categories of class variable “A”, three independent categories of class variable “B”, and two independent categories of class variable “C”. The novometric multiple regression analogue is demonstrated. View journal article

Ariel Linden & Paul R. Yarnold Linden Consulting Group, LLC & Optimal Data Analysis, LLC In 1997, Australia implemented a gun buyback program that reduced the stock of firearms by around one-fifth, and nearly halved the number of gun-owning households. Leigh and Neill evaluated if the reduction in firearms availability affected homicide and suicide rates, … Continue reading The Australian Gun Buyback Program and Rate of Suicide by Firearm

Paul R. Yarnold Optimal Data Analysis, LLC ANOVA with two between-groups factors is used to compare an ordered attribute (score) between four independent categories of class variable “A”, and three independent categories of class variable “B”. The novometric multiple regression analogue is demonstrated. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC An integer attribute (dependent) variable is compared between four categories of a class (independent) variable using ANOVA with one between-groups factor, as well as novometric analogues to ANOVA (to predict class status) and to multiple regression (to predict score). View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This note reviews creation and use of staging tables for CTA models. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC The Friedman test, ODA, and novometric statistical analysis conducted via ODA, are used to compare ten expert violinists’ independent, blind evaluations of three different violins, each rated using 10-point scales. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This note suggests tips on learning about ODA, from a cursory understanding through paradigm mastery. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This note addresses transforming a data table into a data set. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC ODA models may be developed to maximize (i.e., optimize) overall classification accuracy, mean sensitivity across class categories, or precision forecasting of specific class categories. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC Dichotomous measurement scales are likely insufficiently granular to empower breakthrough scientific discoveries for classical phenomena. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis, LLC & Linden Consulting Group, LLC The use of CTA to construct propensity score weights is complicated by division by zero in models having any perfectly predicted endpoints: omitting undefined propensity scores yields a degenerate solution. This note presents an algorithmic remedy to this situation. View … Continue reading Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints

Paul R. Yarnold Optimal Data Analysis, LLC Preview begins by describing the ODA algorithm, requisite special-purpose software, and applied investigations using ODA to conduct statistical analysis. Discussion next addresses the development and application of multivariable linear and non-linear optimal models. Preview concludes by discussing current research and development foci, including causal inference methodology, system automation, … Continue reading What is Optimal Data Analysis?

Paul R. Yarnold Optimal Data Analysis, LLC This note offers tips on finding articles on specific topics in ODA. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC A total of 6,005 hospital patients rated their satisfaction with time taken for a nurse to respond to the call button (1=very dissatisfied; 2=somewhat dissatisfied; 3=neutral; 4=somewhat satisfied; 5=very satisfied). Responses were consolidated into four successive quarters (3rd and 4th Quarter of 1989, 1st and 2nd Quarter of … Continue reading Novometric Pairwise Comparisons in Consolidated Temporal Series

Paul R. Yarnold Optimal Data Analysis, LLC A segmentation printout or “banner pass” constitutes a consolidated crosstabulation of user/customer responses to specific prompts, for desired reporting periods and/or organizational units. In this study a total of 6,005 hospital patients rated their satisfaction with time for a nurse to respond to the call button (1=very dissatisfied; … Continue reading Novometric Comparison of Patient Satisfaction with Nurse Responsiveness Over Successive Quarters

Paul R. Yarnold Optimal Data Analysis, LLC The American National Election Panel Study modeled transitions in social class (Working versus Middle) identification occurring between 1956, 1958, and 1960, and although a first-order Markov model was judged unsatisfactory, insufficient measurements were available to validate higher-order models. The sample was thus stratified with respect to whether respondents’ … Continue reading Novometric Comparison of Markov Transition Matrices for Heterogeneous Populations

Paul R. Yarnold Optimal Data Analysis, LLC The American National Election Panel Study modeled transitions in social class identification occurring between 1956, 1958, and 1960. Visual analysis suggested “…that transition probabilities linking class identifications in 1958 and 1960 vary with 1956 identification”. Transition tables were compared using Goodman’s chi-square procedure: chi-square=98.2, df=2, p<0.0001. Based on … Continue reading Novometric Analysis of Transition Matrices to Ascertain Markovian Order