We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle’s approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein–Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.

We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.

In this article, we present a general theorem and proof for the global identification of composed CFA models. They consist of identified submodels that are related only through covariances between their respective latent factors. Composed CFA models are frequently used in the analysis of multimethod data, longitudinal data, or multidimensional psychometric data. Firstly, our theorem enables researchers to reduce the problem of identifying the composed model to the problem of identifying the submodels and verifying the conditions given by our theorem. Secondly, we show that composed CFA models are globally identified if the primary models are reduced models such as the CT-C$(M-1)$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(M-1)$$\end{document} model or similar types of models. In contrast, composed CFA models that include non-reduced primary models can be globally underidentified for certain types of cross-model covariance assumptions. We discuss necessary and sufficient conditions for the global identification of arbitrary composed CFA models and provide a Python code to check the identification status for an illustrative example. The code we provide can be easily adapted to more complex models.

We contribute to a large literature that explores prosocial behavior in public goods experiments. We adopt an experimental design that allows full contribution to the public good to be sustained in equilibrium. We study the effect of the time horizon on a subject's propensity to contribute to a public good by varying the stopping rule for the game. While many studies examine the effect of a random stopping rule in prisoner's dilemma games, to our knowledge, only two other studies have directly compared behavior in public goods experiments with finite and random stopping rules. Consistent with existing studies, we find that contribution rates are similar across treatments in early rounds of play, and contribution rates are higher with random verses finite stopping rules in later rounds. Overall, we find significantly higher contributions to the public good when donors face a known probability of future interactions with the same group of participants compared to interactions with a finite endpoint. Further, the difference in cooperative behavior is driven primarily by the stopping rule, rather than the length of the game.

International organisations emphasise how Governments around the world must use the public procurement process to aid a global drive to eliminate human trafficking in their supply chains. In this significant and original contribution, the authors examine a leading procurement model, the Australian Commonwealth Procurement Rules (CPR), for the purpose of examining whether the CPR model satisfies the necessary standards of Legal Certainty and Effectiveness for addressing the risk of trafficking occurring in public sector supply chains. The research generates new insights for countries seeking to tackle trafficking via public procurement systems and identifies pitfalls for countries to avoid if seeking to emulate the Australia CPR model, making appropriate reference to US and UK models where appropriate. The authors demonstrate how key elements of the CPR model fail to provide for the required degree of Legal Certainty and Effectiveness to tackle trafficking. System failure is demonstrated by analysis of the CPR, showing either how key CPR provisions fail to satisfy these 2 key tests, or because there is a complete absence of appropriate provisions to comprehensively deal with the risk of trafficking in public sector supply chains. This article should serve not only as a guide to countries yet to address human rights considerations in their public procurement supply chains, but also as a blueprint for countries around the world seeking to re-evaluate whether existingprovisions in their domestic procurement frameworks are fit to tackle the global scourge of trafficking in public supply chains.

Gwendolen Bishop is a name that appears in the margins of my recent account of the English avant-garde theatre. Prior to that she barely made it even into the margins, and then often with some rather significant indecision as to how actually to spell her name. The aim of this essay is to retrieve her from the margins and bring her more centrally into view. In doing so I consciously weave together her arts practices and her personal life, for these are deeply connected. The making of an avantist culture in early twentieth-century England was done not simply by arts experiments but also by kinds of behavior that challenged dominant ideas. In our Western twenty-first century we note and make much of Edwardian behaviors that contested assumptions about gender and sexuality, but we should note, alongside that, some equally striking challenges to ideas about class. Both are apparent in the Bishop story, which I tell more or less as a biographical narrative. The danger when one recovers a person from the shadows is that, in trying to situate them among their contemporaries, one writes overmuch about those contemporaries, such that our person fades again into the mists. With our biographical focus fixed solidly on her we can, I hope, discover how Gwendolen Bishop made her very particular contribution to this exciting cultural period on the eve of modernism.

Are economic decisions affected by short-term stress, failure, or both? Such effects have not been clearly distinguished in previous experimental research, and have the potential to worsen economic outcomes, especially in disadvantaged socioeconomic groups. We validate a novel experimental protocol to examine the individual and combined influences of stress, failure, and success. The protocol employs a 2 × 3 experimental design in two sessions and can be used online or in laboratory studies to analyse the impact of these factors on decision-making and behaviour. The stress protocol was perceived as significantly more stressful than a control task, and it induced a sizeable and significant rise in state anxiety. The provision of negative feedback (“failure”) significantly lowered participants’ assessment of their performance, induced feelings of failure, and raised state anxiety.

Randomized response is an interview technique for sensitive questions designed to eliminate evasive response bias. Since this elimination is only partially successful, two models have been proposed for modeling evasive response bias: the cheater detection model for a design with two sub-samples with different randomization probabilities and the self-protective no sayers model for a design with multiple sensitive questions. This paper shows the correspondence between these models, and introduces models for the new, hybrid “ever/last year” design that account for self-protective no saying and cheating. The model for one set of ever/last year questions has a degree of freedom that can be used for the inclusion of a response bias parameter. Models with multiple degrees of freedom are introduced for extensions of the design with a third randomized response question and a second set of ever/last year questions. The models are illustrated with two surveys on doping use. We conclude with a discussion of the pros and cons of the ever/last year design and its potential for future research.

In the late 1950s, a widespread drive to defy gravity pervaded architectural culture. Bold and seemingly weightless structures gave spatial form to a moment poised on a knife-edge. Cold War tensions, the space race, nuclear anxiety, economic recession and decolonisation fostered a global instability reflected in cultural concerns with balance and its disruption: vertigo. At this historical juncture, gravity became a vital theme of research and expression. Drawing on Roger Caillois’s sociology of play, this essay posits that a distinct phenomenon emerged from this context: what might be called architectural ilinx. While the pursuit of weightlessness had animated the Modern Movement from its inception, the 1950s saw this ideal assume increasingly disembodied forms that appeared to defy gravity outright. By the decade’s end, architects embraced vertigo’s thrill (ilinx) as a design principle, producing a new realm of spatial play. That juncture was characterised by an underlying tension between exhilaration and anxiety, which culminated in the Brussels World’s Fair of 1958, a watershed event showcasing designs that tested the boundaries of structural stability through forms suspended in space. Expo 58 marked a broader shift toward a built environment untethered from the ground. However, many critics were troubled by its ‘acrobatic exhibitionism’, warning against a drift towards structural experimentation for its own sake; and indeed, among other things, the proliferation of gravity-defying architecture seems to have been one of the forebears of what we now know as the ‘experience economy’. Situating the emergence of architectural ilinx within its cultural and historical context, the essay offers a critical reading of a phenomenon that continues to hold relevance.

The social relations model (SRM) is widely used in psychology to investigate the components that underlie interpersonal perceptions, behaviors, and judgments. SRM researchers are often interested in investigating the multivariate relations between SRM effects. However, at present, it is not possible to investigate such relations without relying on a two-step approach that depends on potentially unreliable estimates of the true SRM effects. Here, we introduce a way to combine the SRM with the structural equation modeling (SEM) framework and show how the parameters of our combination can be estimated with a maximum likelihood (ML) approach. We illustrate the model with an example from personality psychology. We also investigate the statistical properties of the model in a small simulation study showing that our approach performs well in most simulation conditions. An R package (called srm) is available implementing the proposed methods.

Dorotheos of Gaza (6th cent.) was a monastic leader whose works, along with the correspondence of his mentors, Barsanuphios (d. after 543) and John of Gaza (d. 543), provide insight into the Second Origenist Controversy and the tenor of theological investigation at a key juncture in late antiquity. The evidence of Dorotheos, who several times cites Evagrios by name, has been noted but its significance not yet fully appreciated. This essay reassesses Dorotheos’s theology and Gazan monastic culture through study of his eschatology in Instruction 12, in context of which the Evagrian passages appear, and which he develops from Origen’s On First Principles. Analysis of Dorotheos’s modifications and developing ideas suggests a more vigorous—indeed, “Origenist”—theological life in Gazan monasticism than has been recognized and calls for a new perspective on the effects of the Second Origenist Controversy as well as Dorotheos’s own position relative to it.

Most scholars tend to focus on the metaphysical and epistemological aspects of Anselm of Aosta’s Proslogion and they often consider any meditative features to be of little importance. I will argue that reading the Proslogion as a meditative text can be justified based on the manuscript evidence and its textual history. A careful examination of the manuscript witnesses to this text reveals at least four versions, which enabled different readings of the text. I will argue that one version was a more meditative reading, namely, the first version of the Proslogion. That focus is also attested by the type of texts that travelled with this version from the twelfth to the fifteenth century. Having established the validity of such a reading, I will bring to the surface the features of the Proslogion that make it a meditative text. Of the many possible outcomes focusing on the meditative features of the Proslogion, this essay will explore only one here: the fool of chapter two emerges not as some heretic, pagan, or proto-atheist, with whom Anselm has engaged in intellectual combat. Instead, in the mimetic tradition of meditative texts of the eleventh and twelfth centuries, the fool is Anselm himself and, by extension, the reader.

The present article proposes and evaluates marginal maximum likelihood (ML) estimation methods for hierarchical multinomial processing tree (MPT) models with random and fixed effects. We assume that an identifiable MPT model with S parameters holds for each participant. Of these S parameters, R parameters are assumed to vary randomly between participants, and the remaining $S-R$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$S-R$$\end{document} parameters are assumed to be fixed. We also propose an extended version of the model that includes effects of covariates on MPT model parameters. Because the likelihood functions of both versions of the model are too complex to be tractable, we propose three numerical methods to approximate the integrals that occur in the likelihood function, namely, the Laplace approximation (LA), adaptive Gauss–Hermite quadrature (AGHQ), and Quasi Monte Carlo (QMC) integration. We compare these three methods in a simulation study and show that AGHQ performs well in terms of both bias and coverage rate. QMC also performs well but the number of responses per participant must be sufficiently large. In contrast, LA fails quite often due to undefined standard errors. We also suggest ML-based methods to test the goodness of fit and to compare models taking model complexity into account. The article closes with an illustrative empirical application and an outlook on possible extensions and future applications of the proposed ML approach.

With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of test equating scores on different test forms. This paper argues, however, that the use of item response models does not require any test score equating. Instead, it involves the necessity of parameter linking due to a fundamental problem inherent in the formal nature of these models—their general lack of identifiability. More specifically, item response model parameters need to be linked to adjust for the different effects of the identifiability restrictions used in separate item calibrations. Our main theorems characterize the formal nature of these linking functions for monotone, continuous response models, derive their specific shapes for different parameterizations of the 3PL model, and show how to identify them from the parameter values of the common items or persons in different linking designs.

Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.