UniODA and Small Samples

Paul R. Yarnold

Optimal Data Analysis, LLC

Statistical hypotheses investigated by researchers representing a vast domain of empirical disciplines often involve discrimination and prediction of discrete outcomes: life versus death in medicine, innocent versus guilty in law, profit versus loss in finance, victory versus defeat in military science, etcetera. Although some studies feature large samples of a thousand or more observations, most of the literature employs much smaller samples. Preliminary research and examination of theoretical distributions indicates that UniODA is capable of identifying statistically significant effects in applications offering very small samples.

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Analysis Involving Categorical Attributes Having Many Response Categories

Paul R. Yarnold & Fred B. Bryant

Optimal Data Analysis, LLC & Loyola University Chicago

Attributes measured on a categorical response scale are common in the literature. Categorical scales for attributes such as, for example, political affiliation, ethnic origin, marital status, state of residence, or diagnosis may consist of many qualitative response categories. Such disorganized variables rarely appear in multivariable models: some effects are missed in analysis due to inadequate statistical power for the many categories, and some findings are dismissed due to inability of the investigator to recognize the dimension(s) underlying segmented categories. This research note recommends that such multicategorical attributes are replaced by a new set of attributes created via content analysis. In this approach observations are scored on new dimensions all theoretically motivated to predict the class variable. The methodology is illustrated using a hypothetical example in the field of investment realty.

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Minimum Standards for Reporting UniODA Findings

Paul R. Yarnold

Optimal Data Analysis, LLC

As the number of researchers using any statistical method and the domain of disciplines they represent increases, the opportunity for and likelihood of the development of disparate traditions for the reporting of analytic findings also increases. The great advantage of establishing a minimum set of standards for reporting findings obtained using any method—is that researchers from all fields will be able to easily and clearly understand the fundamental statistical results of any study reporting findings using that method. This note discusses a tabular presentation of the minimum information which is required in order to understand a UniODA analysis.

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