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 current data available). Different county types identified included those for which ACMR increased significantly (p<0.05) at the experimentwise criterion; increased significantly (p<0.05) at the generalized criterion; had a statistically marginal increase (p<0.10) at the generalized criterion; or did not have a statistically significant increase (p>0.10) at the generalized criterion. Prior research is extended by investigating the path of ACMR over time for these four county groupings, and for each county considered separately. The pattern of mean ipsative ACMR scores across time, observed for counties experiencing a statistically significant increase in ACMR after 1998, revealed the means fell into three almost perfectly discriminable levels: (a) low mean scores (mean z<0) seen early (1965 or earlier); (b) medium scores (0<z<0.5) in the middle of the series (1966-1985); and (c) high scores (mean z>0) late in the series (1986 or later). Series achieved mean z>1 in 1998, and mean scores observed since 1998 have been among the highest on record.

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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 classified by the model is indexed using ESS. Unlike ESS, the Effect Strength for Predictive Value (ESP) varies across base-rate. Measured using the identical scale as ESS, ESP indexes the models ability to produce accurate classifications. In the study discriminating genders, for example, the percent of the time the model made an accurate prediction that an observation was either male or female is indexed using ESP. While ESS is important in helping to guide the development and testing of theory, ESP is important in translating theory from laboratory to real-world applications, and is thus added to the recommended minimum standards for reporting of all UniODA findings. In addition, the evaluation of all possible aggregated confusion tables aids in interpreting UniODA findings, and evaluating the potential for increasing classification accuracy by improving measurement of ordered class variables and/or attributes, and so was also added as a recommended minimum standard. Current standards are demonstrated using three examples: (1) using income to discriminate gender in a sample of 416 general internal medicine (GIM) patients, testing the a priori hypothesis that men have higher income than women; (2) using body mass index (BMI) to discriminate income in a sample of 411 GIM patients, testing the a priori hypothesis that BMI and income are positively related; and (3) discriminating mental focus using GHA (a measure of barometric pressure) in a post hoc analysis of 297 sequential daily entries of a fibromyalgia patient using an intelligent health diary, that were separated into training and hold-out validity samples.

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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 there in 1998.

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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 on two or more test sessions spaced by a theoretically meaningful time span. This note presents a classic serial study with n=12 observations, each measured at the same four theoretically-significant times. Scatter plots illustrating test session and raw, normative, and ipsative standardized data demonstrate that ipsatively standardized data are clearly the most appropriate to statistically address fundamental questions that motivate such research.

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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 of UniODA findings with binary class variables to applications involving class variables with more than two response categories, and testing confirmatory or exploratory hypotheses.

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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) and doctors (n=1,806) seen in the ER to predict overall satisfaction. Results showed that the classification accuracy, as assessed using ESS, which was achieved by optimized MRA models of overall satisfaction was moderate for ratings of technicians (39.6) and nurses (42.0), and relatively strong for ratings of doctors (50.9). Illustrations show how the method works.

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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 UniODA to maximize predictive accuracy, was used to assess the extent to which ratings concerning the registration process are predictive of overall satisfaction ratings. Results indicated that ratings of helpfulness of the triage nurse, ease of the process to obtain insurance/billing information, waiting time before going to the treatment area, and convenience of parking together explained 50.1% of the variance in overall satisfaction ratings, corresponding to moderate accuracy (ESS=32.8). The maximum accuracy was obtained using UniODA to adjust model decision thresholds, and improved by 20.1% but remained moderately strong (ESS=39.4).

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