How to Create an ASCII Input Data File for UniODA and CTA Software

How to Create an ASCII Input Data File for UniODA and CTA Software

Fred B. Bryant & Patrick R. Harrison

Loyola University Chicago

UniODA and CTA software require an ASCII (unformatted text) file as input data. Arguably the most difficult task an operator faces in conducting analyses is converting the original data file from (a) whatever software package was used to enter the data, into (b) an ASCII file for analysis. This article first highlights critical issues concerning missing data, variable labels, and variable types that users must address in order to convert their data into an ASCII file for analysis using ODA software. Specific steps needed to convert a data set from its original file-type into a space-delimited ASCII file are then discussed. The process of converting data into ASCII files for use as input data is illustrated for three leading statistical software packages: SPSS, SAS, and STATISTICA.

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Maximizing the Accuracy of Multiple Regression Models using UniODA: Regression Away From the Mean

Maximizing the Accuracy of Multiple Regression Models using UniODA: Regression Away From the Mean

Paul R. Yarnold, Ph.D., Fred B. Bryant, Ph.D., and Robert C. Soltysik, M.S.

Optimal Data Analysis, LLC and Loyola University Chicago

Standard regression models best predict values that lie near the mean. Three examples illustrate how optimization of the regression model using an established UniODA methodology greatly improves accurate prediction of extreme values.

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Statistical Power of Optimal Discrimination with a Normal Attribute and Two Classes: One-Tailed Hypotheses

Statistical Power of Optimal  Discrimination with a Normal Attribute and Two Classes: One-Tailed Hypotheses

Robert C. Soltysik, M.S., and Paul R. Yarnold, Ph.D.

Optimal Data Analysis, LLC

This note reports statistical power (1-β) obtained by ODA when used with a normally-distributed attribute, as a function of alpha and effect size.

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Modeling Individual Reactivity in Serial Designs: Changes in Weather and Physical Symptoms in Fibromyalgia

Modeling Individual Reactivity in Serial Designs: Changes in Weather and Physical Symptoms in Fibromyalgia

Paul R. Yarnold, Ph.D., Robert C. Soltysik, M.S., and William Collinge, Ph.D.

Optimal Data Analysis, LLC and Collinge and Associates

This note criticizes current statistical convention, and discusses and illustrates appropriate statistical methodology for investigating the relationship between weather and individual symptoms.

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Reverse CTA Versus Multiple Regression Analysis

Reverse CTA Versus Multiple Regression Analysis 

Paul R. Yarnold, Ph.D. and Robert C. Soltysik, M.S.

Optimal Data Analysis, LLC

This paper illustrates how to reverse CTA for applications having an ordered class variable and categorical attributes. Whereas a regression model is used to make point predictions for the dependent measure based on values of the independent variables, reverse CTA is used to find domains on the dependent measure which are explained by the independent variables.

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