University of Oklahoma

About: University of Oklahoma is a education organization based out in Norman, Oklahoma, United States. It is known for research contribution in the topics: Population & Radar. The organization has 25269 authors who have published 52609 publications receiving 1821706 citations. The organization is also known as: OU & Oklahoma University.

Qualitative Descriptors of Strength of Association and Effect Size

Abstract: Cohen's (1988) work in power analysis connects values of r and d(difference in means in standard deviation units) with the qualitative descriptors small, medium, and large. This paper discusses the pros and cons of using qualitative (nonmathematical) descriptors of strength of association and effect size in data interpretation. It proposes an extension of Cohen's classification scheme through the addition of a “very large” descriptive category. The paper presents advantages of the odds ratio for the analysis of categorical data, argues for increased use of the odds ratio, and develops qualitative descriptors of effect size for the odds ratio: about 1.5 to 1 = small effect (or weak association), about 2.5 to 1 = medium (or moderate), about 4 to 1 = large (or strong), about 10 to 1 = very large (or very strong). When percentages are between about 15 and 85, qualitative descriptors of effect size can be utilized for the difference in percentages: about 7 percentage points = small, about 18 points = ...

The Central Question in Entrepreneurial Cognition Research 2007

Abstract: In this article, we take note of advances in the entrepreneurial cognition research stream. In doing so, we bring increasing attention to the usefulness of entrepreneurial cognition research. First, we offer and develop a central research question to further enable entrepreneurial cognition inquiry. Second, we present the conceptual background and some representative approaches to entrepreneurial cognition research that form the context for this question. Third, we introduce the articles in this Special Issue as framed by the central question and approaches to entrepreneurial cognition research, and suggest how they further contribute to this developing stream. Finally, we offer our views concerning the challenges and opportunities that await the next generation of entrepreneurial cognition scholarship. We therefore invite (and seek to enable) the growing community of entrepreneurship researchers from across multiple disciplines to further develop the “thinking– doing” link in entrepreneurship research. It is our goal to offer colleagues an effective research staging point from which they may embark upon many additional research expeditions and investigations involving entrepreneurial cognition.

Declining Amphibian Populations: The Problem of Separating Human Impacts from Natural Fluctuations

Abstract: Reports of declining amphibian populations in many parts of the world are numerous, but supporting long-term census data are generally unavailable. Census data from 1979 to 1990 for three salamander species and one frog species at a breeding pond in South Carolina showed fluctuations of substantial magnitude in both the size of breeding populations and in recruitment of juveniles. Breeding population sizes exhibited no overall trend in three species and increased in the fourth. Recent droughts account satisfactorily for an increase in recruitment failures. These data illustrate that to distinguish between natural population fluctuations and declines with anthropogenic causes may require long-term studies.

Apolipoprotein E polymorphism and cardiovascular disease: a HuGE review.

Abstract: This review examines the association between the apolipoprotein (apo) var epsilon gene polymorphism (or its protein product (apo E)), metabolic regulation of cholesterol, and cardiovascular disease. The apo var epsilon gene is located at chromosome 19q13.2. Among the variants of this gene, alleles (*) epsilon2, (*) epsilon3, and (*) epsilon4 constitute the common polymorphism found in most populations. Of these variants, apo (*) epsilon3 is the most frequent (>60%) in all populations studied. The polymorphism has functional effects on lipoprotein metabolism mediated through the hepatic binding, uptake, and catabolism of chylomicrons, chylomicron remnants, very low density lipoprotein (VLDL), and high density lipoprotein subspecies. Apo E is the primary ligand for two receptors, the low density lipoprotein (LDL) receptor (also known as the B/E receptor) found on the liver and other tissues and an apo E-specific receptor found on the liver. The coordinate interaction of these lipoprotein complexes with their receptors forms the basis for the metabolic regulation of cholesterol. Allelic variation in apo var epsilon is consistently associated with plasma concentrations of total cholesterol, LDL cholesterol, and apo B (the major protein of LDL, VLDL, and chylomicrons). Apo var epsilon has been studied in disorders associated with elevated cholesterol levels or lipid derangements (i.e., hyperlipoproteinemia type III, coronary heart disease, strokes, peripheral artery disease, and diabetes mellitus). The apo var epsilon genotype yields poor predictive values when screening for clinically defined atherosclerosis despite positive, but modest associations with plaque and coronary heart disease outcomes. In addition to genotype-phenotype associations with vascular disease, the alleles and isoforms of apo var epsilon have been related to dementias, most commonly Alzheimer's disease.

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

Abstract: The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.