Paul R. Yarnold
Optimal Data Analysis, LLC
This note empirically compares the use of raw vs. ipsatively standardized variables in optimal weighted Markov analysis involving a series for a single outcome—presently, ratings of sleep difficulties for an individual. Findings indicate that the raw score and ipsatively standardized ordinal ratings yield equivalent results in such designs.
Paul R. Yarnold
Optimal Data Analysis, LLC
A recent article optimized ESS of a suboptimal classification tree model that discriminated hospitalized vs. ambulatory patients with community acquired pneumonia (CAP). This note suggests possible alternatives for two original attributes as a means of increasing model accuracy: patient disease-specific knowledge vs. “college education”, and patient-specific functional status and social support vs. “living arrangement”.
Paul R. Yarnold & Robert C. Soltysik
Optimal Data Analysis, LLC
This paper uses ODA to weight each event in the transition table by its corresponding absolute change-in-value, thereby maximizing precision of the class variable as well as model accuracy.
Paul R. Yarnold
Optimal Data Analysis, LLC
This paper compares findings obtained using multiple regression analysis vs. novometric analysis to identify the relationship between the type of degree earned by 8th Grade math teachers and the training which they received in cultural and cognitive student diversity methods.
Paul R. Yarnold
Optimal Data Analysis, LLC
Pruning to maximize model accuracy (requiring simple hand computation) is applied to a classification tree model developed via S-PLUS to create propensity scores to improve causal inference in comparing hospitalized vs. ambulatory patients with community-acquired pneumonia. Research reported herein constitutes a thought-provoking example of a striking misalliance between forward analytic thinking and vestige statistical tools—a condition that dominates the empirical literature today. Modifications of ubiquitous methodological practices are suggested.
Paul R. Yarnold
Optimal Data Analysis, LLC
A classification tree pruning methodology that maximizes effect strength for sensitivity is demonstrated for a model developed using classification and regression analysis to identify factors which predict missing data.
Paul R. Yarnold
Optimal Data Analysis, LLC
Growth of classification tree models based upon a secondary a priori performance criterion is demonstrated using a suboptimal classification tree model previously developed using the CHAID algorithm.
