In the social sciences we are often interested in comparing models specified by parametric equality or inequality constraints. For instance, when examining three group means $\{{\mathit{\mu }}_1,{\mathit{\mu }}_2,{\mathit{\mu }}_3\}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\{ \mu _1, \mu _2, \mu _3\}$$\end{document} through an analysis of variance (ANOVA), a model may specify that ${\mathit{\mu }}_1<{\mathit{\mu }}_2<{\mathit{\mu }}_3$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mu _1<\mu _2<\mu _3$$\end{document}, while another one may state that $\{{\mathit{\mu }}_1={\mathit{\mu }}_3\}<{\mathit{\mu }}_2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\{ \mu _1=\mu _3\} <\mu _2$$\end{document}, and finally a third model may instead suggest that all means are unrestricted. This is a challenging problem, because it involves a combination of nonnested models, as well as nested models having the same dimension. We adopt an objective Bayesian approach, requiring no prior specification from the user, and derive the posterior probability of each model under consideration. Our method is based on the intrinsic prior methodology, suitably modified to accommodate equality and inequality constraints. Focussing on normal ANOVA models, a comparative assessment is carried out through simulation studies. We also present an application to real data collected in a psychological experiment.

Two coefficients are proposed for measuring the extent of overlap in distributions as a direct function of the variance between the arithmetic means (“disco” and “odisco”). They are designed to answer such questions as: “Given the value of a numerical variable x, to which population should an individual be assigned so that minimum error would be incurred?” This is just the reverse of the question addressed by ANOVA. These coefficients are shown to be analytic in x and they are related to Pearson's eta and Fisher's F. Extensions of these coefficients (designed for univariate, one-way discrimination) to k-way and multivariate discriminant analysis and measurement of “interaction” are suggested.

Is the quality of a 91-point wine significantly different from that of an 89-point wine? Which wines are underpriced relative to their evaluation of quality? This paper addresses these questions by constructing a novel wine rating system based on scores assigned by a panel of wine experts to a set of wines. Wines are classified in ranked disjoint quality equivalence classes using measures of statistically significant and commercially relevant score differences. The rating system is applied to the “Judgment of Paris” wine competition, to data of Bordeaux en-primeur expert scores and prices, and to expert scores and price categories of a large database of Italian wines. The proposed wine rating system provides an informative assessment of wine quality for producers and consumers and a flexible rating methodology for commercial applications.

This study explores the relationships between group-affect tone, teams’ transactive memory systems (TMSs), and teams’ incremental creativity. Data were collected from 334 team members and 70 team leaders across 70 teams. Results indicate that positive group-affect tone enhances TMS, while negative group-affect tone impedes it. TMS positively impacts team incremental creativity. Additionally, both types of group-affect tone influence incremental creativity through TMS mediation. This research advances TMS theory and group-affect tone, substantiating the affect-cognition model and deepening the understanding of TMS’s role in incremental creativity.

A hegemonic neoliberal ideology dominates all areas of work in Turkey, including healthcare. Though neoliberalism has been studied extensively from the perspective of meaning, values, and processes, managerial and leadership behavior dynamics require further research. This study analyzes the relationship between managerialism, toxic leadership, and ethical climate in an industry swept up by untamed neoliberalism, particularly in a nation where employment and human rights are ceremoniously protected. Through an analysis of medical doctors working in 207 public and private university hospitals in Turkey, we explored the role of managerialism and four distinct ethical climate types, resulting in the emergence of toxic leadership behaviors during the global pandemic. We theorize the extent to which toxic leaders emerge from managerialism. We further explain why the hegemonic Turkish leadership culture thrives in toxic behaviors such as paternalism, fealty, ingratiation, nepotism, and cronyism in the context of neoliberal expansion.

The germination percentage (GP) is commonly employed to estimate the viability of a seed population. Statistical methods such as analysis of variance (ANOVA) and logistic regression are frequently used to analyse GP data. While ANOVA has a long history of usage, logistic regression is considered more suitable for GP data due to its binomial nature. However, both methods have inherent issues that require attention. In this study, we address previously unexplored challenges associated with these methods and propose the utilization of a likelihood ratio test as a solution. We demonstrate the advantages of employing the likelihood ratio test for GP data analysis through simulations and real data analysis.

We use attribution theory to show that firms that make more internal attributions to positive performance outcomes engage slack resources more freely for corporate entrepreneurship (CE) than firms that make fewer of such attributions. In addition, we show that the way in which companies make external attributions to performance outcomes moderates this relationship. To examine this empirically, we explore how top management teams discuss the factors that contribute to firm performance. Specifically, we look at attributions provided in the Management's Discussion and Analysis section of the annual reports of 144 pharmaceutical firms over a 2-year period. In line with our predictions, we find that greater internal attribution to positive performance outcomes leads to increased use of slack resources for CE. Furthermore, we find that this effect is stronger when firms make more external attributions to negative performance outcomes than positive performance outcomes.

A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.