Statistical Evaluation of the Findings of Qualitative Comparative Analysis

Paul R. Yarnold

Optimal Data Analysis, LLC

Qualitative data analysis is a structured observational and clustering methodology which facilitates hypothesis development and variable generation for quantitative research, fruitfully employed in agriculture, anthropology, astronomy, biology, forensic investigation, education, history, marketing, medicine, political science, psychology, sociology, and zoology, to name a handful of diciplines. The method known as Qualitative Comparative Analysis (QCA) is adept in producing evidence in complex policy problems involving interdependencies among multiple causes. Recent research used QCA to study factors underlying high rates of teenage conceptions in high-risk areas in England. Nine binary attributes reflecting five different ‚Äúvariable constellations‚ÄĚ were identified. Variable constellations are putatively associated with areas with teenage conception rates which are narrowing versus not narrowing (the outcome or class variable) with respect to the national average. This article discusses use of UniODA and CTA to ascertain which attributes are statistically reliable predictors of outcome.

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Exploratory Analysis for an Ordered Series of a Dichotomous Attribute: Airborne Radiation and Congenital Hypothyroidism of California Newborns

Paul R. Yarnold & Robert C. Soltysik

Optimal Data Analysis, LLC

Confirmatory hypothesis-testing methodology was recently demonstrated with an example assessing the effect of airborne beta nuclear radiation emanating from the Fukushima nuclear meltdown on the risk of confirmed congenital hypothyroidism (CH) for newborns in California in the years 2011-2012. Eyeball inspection of the data suggests that the a priori hypothesis which was evaluated is inconsistent with the actual data, so an exploratory analysis is conducted.

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Confirmatory Analysis for an Ordered Series of a Dichotomous Attribute: Airborne Radiation and Congenital Hypothyroidism of California Newborns

Paul R. Yarnold & Robert C. Soltysik

Optimal Data Analysis, LLC

Ordered series involving a dichotomous (binary) variable are widely used to describe changes in phenomena which occur across time. Examples of such series include the percentage of a sample or population each year (or other unit of time) that marries, dies, or is arrested. This article demonstrates how UniODA is used for such designs to test a confirmatory (a priori), omnibus (overall), optimal (maximum-accuracy) hypothesis, subsequently disentangled by a confirmatory (if hypotheses are composed) or otherwise by an exploratory (post hoc) optimal range test. This methodology is demonstrated with an example assessing the effect of airborne beta nuclear radiation emanating from the Fukushima nuclear meltdown on the risk of congenital hypothyroidism (CH) for newborns in California in the years 2011-2012.

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