Analysis of variance--why it is more important than ever

Analysis of variance (ANOVA) is an extremely important method in exploratory and confirmatory data analysis. Unfortunately, in complex problems (e.g., split-plot designs), it is not always easy to set up an appropriate ANOVA. We propose a hierarchical analysis that automatically gives the correct ANOVA comparisons even in complex scenarios. The inferences for all means and variances are performed under a model with a separate batch of effects for each row of the ANOVA table. We connect to classical ANOVA by working with finite-sample variance components: fixed and random effects models are characterized by inferences about existing levels of a factor and new levels, respectively. We also introduce a new graphical display showing inferences about the standard deviations of each batch of effects. We illustrate with two examples from our applied data analysis, first illustrating the usefulness of our hierarchical computations and displays, and second showing how the ideas of ANOVA are helpful in understanding a previously fit hierarchical model.Comment: This paper discussed in: [math.ST/0508526], [math.ST/0508527], [math.ST/0508528], [math.ST/0508529]. Rejoinder in [math.ST/0508530

The European Union as a masculine military power:European Union security and defence policy in 'Times of Crisis'

Against the background of a sense of crisis in the European Union and in international politics, European Union Member States have since 2016 increased their cooperation within the Common Security and Defence Policy, for example, establishing the European Defence Fund. Scholars have long pointed out that the European Union lacks the necessary ‘hard’ military power to influence international politics, subscribing to and constituting an image of the European Union as not masculine enough. We are critical of these accounts and develop a different argument. First, building on insights from feminist security and critical military studies, we argue that the European Union is a military power constituted by multiple masculinities. We consider the European Union to be a masculine military power, not only because it uses and aims to develop military instruments, but also because of how militarism and military masculinities permeate discourses, practices and policies within Common Security and Defence Policy and the European Union more broadly. We argue, second, that the crisis narrative allows the European Union to strengthen Common Security and Defence Policy and exhibit more aggressive military masculinities based on combat, which exist alongside entrepreneurial and protector masculinities. These developments do not indicate a clear militarisation of Common Security and Defence Policy, but, rather, an advancement and normalisation of militarism and the militarised masculinities associated with it

Evaluating manifest monotonicity using Bayes factors

The assumption of latent monotonicity in item response theory models for dichotomous data cannot be evaluated directly, but observable consequences such as manifest monotonicity facilitate the assessment of latent monotonicity in real data. Standard methods for evaluating manifest monotonicity typically produce a test statistic that is geared toward falsification, which can only provide indirect support in favor of manifest monotonicity. We propose the use of Bayes factors to quantify the degree of support available in the data in favor of manifest monotonicity or against manifest monotonicity. Through the use of informative hypotheses, this procedure can also be used to determine the support for manifest monotonicity over substantively or statistically relevant alternatives to manifest monotonicity, rendering the procedure highly flexible. The performance of the procedure is evaluated using a simulation study, and the application of the procedure is illustrated using empirical data. Keywords: Bayes factor, essential monotonicity, item response theory, latent monotonicity, manifest monotonicit

Technology, agency, critique:An interview with Claudia Aradau

info:eu-repo/semantics/publishe

BIEMS: A Fortran 90 Program for Calculating Bayes Factors for Inequality and Equality Constrained Models

This paper discusses a Fortran 90 program referred to as BIEMS (Bayesian inequality and equality constrained model selection) that can be used for calculating Bayes factors of multivariate normal linear models with equality and/or inequality constraints between the model parameters versus a model containing no constraints, which is referred to as the unconstrained model. The prior that is used under the unconstrained model is the conjugate expected-constrained posterior prior and the prior under the constrained model is proportional to the unconstrained prior truncated in the constrained space. This results in Bayes factors that appropriately balance between model fit and complexity for a broad class of constrained models. When the set of equality and/or inequality constraints in the model represents a hypothesis that applied researchers have in, for instance, (M)AN(C)OVA, (multivariate) regression, or repeated measurements, the obtained Bayes factor can be used to determine how much evidence is provided by the data in favor of the hypothesis in comparison to the unconstrained model. If several hypotheses are under investigation, the Bayes factors between the constrained models can be calculated using the obtained Bayes factors from BIEMS. Furthermore, posterior model probabilities of constrained models are provided which allows the user to compare the models directly with each other