Novometrics vs. Regression Analysis: Modeling Patient Satisfaction with Care Received in the Emergency Room

Paul R. Yarnold

Optimal Data Analysis, LLC

Ordered dependent (class) variables are ordinarily modeled by Pearson correlation (r) in univariable applications with one ordered independent variable (attribute), and by multiple regression analysis (MRA) in multivariable applications involving more than one attribute. Prior research demonstrated the use of ODA to maximize predictive accuracy of r and MRA models. The present paper demonstrates the use of novometrics, the maximum-accuracy alternative to r and MRA.

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Novometrics vs. Regression Analysis: Literacy, and Age and Income, of Ambulatory Geriatric Patients

Paul R. Yarnold

Optimal Data Analysis, LLC

A convenience sample of 293 ambulatory women patients, all older than 65 years of age, were surveyed in a general medicine clinic. Correlation (r), multiple regression analysis (MRA), and novometric analysis were used to model the relationship of scores (even integers) on the TOFHLA literacy measure (the dependent or class variable) with age (recorded to two significant digits to the right of the decimal) and income (measured as 1 to 8, inclusive, integer annual increments of $10,000). Regression- and novometric-based findings are contrasted.

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Novometric Analysis with Ordered Class Variables: The Optimal Alternative to Linear Regression Analysis

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis, LLC

Employed to model an ordered dependent (class) variable, Pearson correlation (r) is used in univariable applications featuring one ordered independent variable (attribute), and multiple regression analysis (MRA) is utilized in multivariable applications featuring two or more attributes. Prior research demonstrated how to maximize the predictive accuracy of univariable and multivariable regression models vis-à-vis an ODA-based procedure. The present paper instead demonstrates optimal alternatives to r and MRA.

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