Extent and limitations of density functional theory in describing magnetic systems

The performance of density-functional theory to solve the exact, nonrelativistic, many-electron problem for magnetic systems has been explored in a new implementation imposing space and spin symmetry constraints, as in ab initio wave function theory. Calculations on selected systems representative of organic diradicals, molecular magnets and antiferromagnetic solids carried out with and without these constraints lead to contradictory results, which provide numerical illustration on this usually obviated problem. It is concluded that the present exchange-correlation functionals provide reasonable numerical results although for the wrong physical reasons, thus evidencing the need for continued search for more accurate expressions.

Conditional manipulation of sex ratios by ant workers: A test of kin selection theory

Variable queen mating frequencies provide a unique opportunity to study the resolution of worker-queen conflict over sex ratio in social Hymenoptera, because the conflict is maximal in colonies headed by a singly mated queen and is weak or nonexistent in colonies headed by a multiply mated queen. In the wood ant Formica exsecta, workers in colonies with a singly mated queen, but not those in colonies with a multiply mated queen, altered the sex ratio of queen-laid eggs by eliminating males to preferentially raise queens. By this conditional response to queen mating frequency, workers enhance their inclusive fitness.

Instrumental leadership: An extension of the Full-Range Leadership Theory

From a theoretical perspective, an extension to the Full Range leadership Theory (FRLT) seems needed. In this paper, we explain why instrumental leadership--a class of leadership includes leader behaviors focusing on task and strategic aspects that are neither values nor exchange oriented--can fulfill this extension. Instrument leadership is composed of four factors: environmental monitoring, strategy formulation and implementation, path-goal facilitation and outcome monitoring; these aspects of leadership are currently not included in any of the FRLT's nine leadership scales (as measured by the MLQ--Multifactor Leadership Questionnaire). We present results from two empirical studies using very large samples from a wide array of countries (N > 3,000) to examine the factorial, discriminant and criterion-related validity of the instrumental leadership scales. We find support for a four-factor instrumental leadership model, which explains incremental variance in leader outcomes in over and above transactional and transformational leadership.

P value and the theory of hypothesis testing: an explanation for new researchers.

In the 1920s, Ronald Fisher developed the theory behind the p value and Jerzy Neyman and Egon Pearson developed the theory of hypothesis testing. These distinct theories have provided researchers important quantitative tools to confirm or refute their hypotheses. The p value is the probability to obtain an effect equal to or more extreme than the one observed presuming the null hypothesis of no effect is true; it gives researchers a measure of the strength of evidence against the null hypothesis. As commonly used, investigators will select a threshold p value below which they will reject the null hypothesis. The theory of hypothesis testing allows researchers to reject a null hypothesis in favor of an alternative hypothesis of some effect. As commonly used, investigators choose Type I error (rejecting the null hypothesis when it is true) and Type II error (accepting the null hypothesis when it is false) levels and determine some critical region. If the test statistic falls into that critical region, the null hypothesis is rejected in favor of the alternative hypothesis. Despite similarities between the two, the p value and the theory of hypothesis testing are different theories that often are misunderstood and confused, leading researchers to improper conclusions. Perhaps the most common misconception is to consider the p value as the probability that the null hypothesis is true rather than the probability of obtaining the difference observed, or one that is more extreme, considering the null is true. Another concern is the risk that an important proportion of statistically significant results are falsely significant. Researchers should have a minimum understanding of these two theories so that they are better able to plan, conduct, interpret, and report scientific experiments.

Reception of Walras' theory of exchange and theory of production in Russia

This paper evaluates the reception of Léon Walras' ideas in Russia before 1920. Despite an unfavourable institutional context, Walras was read by Russian economists. On the one hand, Bortkiewicz and Winiarski, who lived outside Russia and had the opportunity to meet and correspond with Walras, were first class readers and very good ambassadors for Walras' ideas, while on the other, the economists living in Russia were more selective in their readings. They restricted themselves to Walras' Elements of Pure Economics, in particular, its theory of exchange, while ignoring its theory of production. We introduce a cultural argument to explain their selective reading. JEL classification numbers: B 13, B 19.

Retaining principal components for discrete variables

The present study discusses retention criteria for principal components analysis (PCA) applied to Likert scale items typical in psychological questionnaires. The main aim is to recommend applied researchers to restrain from relying only on the eigenvalue-than-one criterion; alternative procedures are suggested for adjusting for sampling error. An additional objective is to add evidence on the consequences of applying this rule when PCA is used with discrete variables. The experimental conditions were studied by means of Monte Carlo sampling including several sample sizes, different number of variables and answer alternatives, and four non-normal distributions. The results suggest that even when all the items and thus the underlying dimensions are independent, eigenvalues greater than one are frequent and they can explain up to 80% of the variance in data, meeting the empirical criterion. The consequences of using Kaiser"s rule are illustrated with a clinical psychology example. The size of the eigenvalues resulted to be a function of the sample size and the number of variables, which is also the case for parallel analysis as previous research shows. To enhance the application of alternative criteria, an R package was developed for deciding the number of principal components to retain by means of confidence intervals constructed about the eigenvalues corresponding to lack of relationship between discrete variables.