Analyzing Categorical Attributes Having Many Response Options

Paul R. Yarnold Optimal Data Analysis, LLC Rectangular categorical designs (RCDs) are among the most prevalent designs in science. Categorical designs involve categorical measures. Examples of categorical measures include gender with two response categories (male, female), color with three response categories (red, blue, green), or political affiliation with many response categories. RCDs cross-tabulate two or … Continue reading Analyzing Categorical Attributes Having Many Response Options

Ascertaining an Individual Patient’s Symptom Dominance Hierarchy: Analysis of Raw Longitudinal Data Induces Simpson’s Paradox

Paul R. Yarnold Optimal Data Analysis, LLC Examples of ordered "N-of-1" single-case series include the monthly closing price of a corporate stock, a dieter’s weight each Friday morning upon waking, or real-time heart rate of an ICU patient recorded minute-by-minute. The present study examined a single-case series for a patient with fibromyalgia, consisting of 297 … Continue reading Ascertaining an Individual Patient’s Symptom Dominance Hierarchy: Analysis of Raw Longitudinal Data Induces Simpson’s Paradox

How to Create a Data Set with SAS™ and Compare Attributes with UniODA™ in Serial Single-Case Designs

Paul R. Yarnold Optimal Data Analysis, LLC The use of UniODA to compare response distributions for different attributes has been demonstrated for binary and ordered measures, using specifically-constructed data sets. This note presents SAS™ code for creating such data sets, and UniODA™ code for comparing attributes in single-case applications. View journal article

Comparing Responses to Dichotomous Attributes in Single-Case Designs

Paul R. Yarnold Optimal Data Analysis, LLC Imagine a single case is assessed on two or more binary attributes over a series of measurements or testing sessions. For example, a chronic pain patient might rate their pain and fatigue each evening before sleep, as “worse than average” or “better than average”. Alternatively, imagine following two … Continue reading Comparing Responses to Dichotomous Attributes in Single-Case Designs

Comparing Attributes Measured with “Identical” Likert-Type Scales in Single-Case Designs with UniODA

Paul R. Yarnold Optimal Data Analysis, LLC Often at the advice of their physician, patients managing chronic disease such as fibromyalgia or arthritis, or those undergoing therapy in rehabilitation medicine or oncology, will record weekly or daily—sometimes even real-time ratings—of physical (e.g., pain, fatigue) and emotional (e.g., depression, anxiety) symptoms. Similarly, often at the advice … Continue reading Comparing Attributes Measured with “Identical” Likert-Type Scales in Single-Case Designs with UniODA

The Most Recent, Earliest, and Kth Significant Changes in an Ordered Series: Traveling Backwards in Time to Assess When Annual Crude Mortality Rate Most Recently Began Increasing in McLean County, North Dakota

Paul R. Yarnold Optimal Data Analysis, LLC Serial or longitudinal measurement is widely practiced by people in all facets of real-world phenomena: an athlete wishes to know how many training sessions are required before significant increase in performance is realized; a musician wishes to assess how many on-stage performances are required before a significant increase … Continue reading The Most Recent, Earliest, and Kth Significant Changes in an Ordered Series: Traveling Backwards in Time to Assess When Annual Crude Mortality Rate Most Recently Began Increasing in McLean County, North Dakota

Trajectory of Crude Mortality Rate in North Dakota Counties

Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC Recent research reported support for the a priori hypothesis that annual crude mortality rate (ACMR) was higher after widespread commercial usage of toxic chemicals and biocides began in the environment in North Dakota in 1998. UniODA was used to compare ACMR in 1934-1997 versus 1998-2005 (the most … Continue reading Trajectory of Crude Mortality Rate in North Dakota Counties

Standards for Reporting UniODA Findings Expanded to Include ESP and All Possible Aggregated Confusion Tables

Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC UniODA models maximize Effect Strength for Sensitivity (ESS), a normed measure of classification accuracy (0=chance, 100=perfect classification) that indexes the models ability to accurately identify the members of different class categories in the sample. In a study discriminating genders, for example, the percent of each gender accurately … Continue reading Standards for Reporting UniODA Findings Expanded to Include ESP and All Possible Aggregated Confusion Tables

Statistically Significant Increases in Crude Mortality Rate of North Dakota Counties Occurring After Massive Environmental Usage of Toxic Chemicals and Biocides Began There in 1998: An Optimal Static Statistical Map

Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC The use of optimal data analysis (ODA) in making a map reporting the findings of confirmatory statistical analyses is demonstrated by comparing the annual crude mortality rate in counties of North Dakota, before versus after large-scale commercial usage of toxic chemicals and biocides in the environment began … Continue reading Statistically Significant Increases in Crude Mortality Rate of North Dakota Counties Occurring After Massive Environmental Usage of Toxic Chemicals and Biocides Began There in 1998: An Optimal Static Statistical Map

Ipsative Standardization is Essential in the Analysis of Serial Data

Paul R. Yarnold & Robert C. Soltysik Optimal Data Analysis, LLC An omnipresent experimental method in all quantitative scientific disciplines involves what is commonly called, for example, a time-series, repeated measures, clinical trial, test-retest, longitudinal, prospective, pre-post, AB, or, more generally, a serial design. In a serial design each observation is assessed on a measure … Continue reading Ipsative Standardization is Essential in the Analysis of Serial Data

Minimum Standards for Reporting UniODA Findings for Class Variables with Three or More Response Categories

Paul R. Yarnold Optimal Data Analysis, LLC An incontrovertible advantage of establishing a minimum set of standards for reporting findings obtained using any method—is researchers from all fields will be able to easily and clearly understand fundamental statistical results of any study reporting findings using that method. This note extends minimum standards proposed for reporting … Continue reading Minimum Standards for Reporting UniODA Findings for Class Variables with Three or More Response Categories

Assessing Technician, Nurse, and Doctor Ratings as Predictors of Overall Satisfaction of Emergency Room Patients: A Maximum-Accuracy Multiple Regression Analysis

Paul R. Yarnold Optimal Data Analysis, LLC This study extends recent quality-of-care research undertaken to enhance understanding of ratings of overall satisfaction with care received as an Emergency Room (ER) patient. Multiple regression analysis, optimized by UniODA to maximize predictive accuracy, was used to separately evaluate the ability of ratings of technicians (n=535), nurses (n=1,800) … Continue reading Assessing Technician, Nurse, and Doctor Ratings as Predictors of Overall Satisfaction of Emergency Room Patients: A Maximum-Accuracy Multiple Regression Analysis

Maximum-Accuracy Multiple Regression Analysis: Influence of Registration on Overall Satisfaction Ratings of Emergency Room Patients

Paul R. Yarnold Optimal Data Analysis, LLC Quality improvement research conducted in an effort to obtain and maintain the maximum possible level of patient satisfaction is an on-going process at many hospitals. This study analyzes survey data from 773 patients receiving care at a private Midwestern hospital’s Emergency Room (ER). Multiple regression analysis, optimized by … Continue reading Maximum-Accuracy Multiple Regression Analysis: Influence of Registration on Overall Satisfaction Ratings of Emergency Room Patients

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 … Continue reading UniODA and Small Samples

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 … Continue reading Analysis Involving Categorical Attributes Having Many Response Categories

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 … Continue reading Minimum Standards for Reporting UniODA Findings

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) … Continue reading How to Create an ASCII Input Data File for UniODA and CTA Software

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

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. View journal article