Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise $L^p$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L^p$$\end{document} loss functions $(0<p\le 1)$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(0 < p\le 1)$$\end{document} that is closely related to an $L^p$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L^p$$\end{document} regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions are nonsmooth, we develop an iteratively reweighted gradient projection algorithm for solving the optimisation problem. We also develop theoretical results that establish the statistical consistency of the estimation, model selection, and post-selection inference. We evaluate the proposed method and compare it with regularised estimation and traditional rotation methods via simulation studies. We further illustrate it using an application to the Big Five personality assessment.

The use of Candecomp to fit scalar products in the context of INDSCAL is based on the assumption that the symmetry of the data matrices involved causes the component matrices to be equal when Candecomp converges. Ten Berge and Kiers gave examples where this assumption is violated for Gramian data matrices. These examples are believed to be local minima. It is now shown that, in the single-component case, the assumption can only be violated at saddle points. Chances of Candecomp converging to a saddle point are small but still nonzero.

This article reviews the experience of Ireland’s Combat Poverty Agency and asks what lessons it may have for fourth branch scholarship. The lesson of the Agency is, in part, one about the pitfalls for novel institutions operating within a traditional tripartite model of constitutional government. The article also suggests, however, that the Combat Poverty Agency’s history may point to the positive potential for the design and operational strategies of non-traditional bodies charged with the promotion of specific social or economic goals. In so doing some reservations about both the specific implications and overall utility of framing these bodies in ‘fourth branch’ terms are also raised. These include concerns regarding the distinctiveness and (relatedly) authority of some conceptions of a ‘fourth branch’. In particular, however, the article queries whether the elevation of independent agencies to ‘branch’ status is always beneficial; and whether, in fact, the location of anti-poverty agencies at a sub-constitutional level may, under certain conditions at least, offer advantages in terms of flexibility and practical problem-solving power.

How does power affect threat perception? Drawing on advances in psychological research on power, I find that the sense of state power inflates the perception of threats. The sense of power activates intuitive thinking in the decision-making process, including a reliance on gut feelings and cognitive shortcuts like heuristics and prior beliefs. In turn, as psychological IR research shows, these mechanisms tend to inflate threat perception. The powerful assess threats from the gut rather than the head. Experimental evidence from the US and China, a reanalysis of a survey of Russian elites, and a large-scale text analysis of Cold War US foreign policy elites lend support to this expectation. The findings help to psychologically reconcile enduring theoretical puzzles—from “underbalancing” to “overextension”—and generate entirely new ones, like the possibility that decision makers of rising, not declining, states feel more fear. Together, the paper offers a “first image reversed” challenge to bottom-up accounts of psychological IR. Decision-maker psychology is also a dependent variable shaped by the balance of power, with important implications for a world returning to great power competition.

I am Mélida Hodgson, president of ASIL, and on behalf of the Society, I welcome you to this lecture by Leila Sadat, this year’s awardee of our Goler T. Butcher Medal.

The model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal. We develop a unified MIIV approach that applies to a mixture of binary, ordinal, censored, or continuous endogenous observed variables. We include estimates of factor loadings, regression coefficients, variances, and covariances along with their asymptotic standard errors. In addition, we create new goodness of fit tests of the model and overidentification tests of single equations. Our simulation study shows that the proposed MIIV approach is more robust to structural misspecifications than diagonally weighted least squares (DWLS) and that both the goodness of fit model tests and the overidentification equations tests can detect structural misspecifications. We also find that the bias in asymptotic standard errors for the MIIV estimators of factor loadings and regression coefficients are often lower than the DWLS ones, though the differences are small in large samples. Our analysis shows that scaling indicators with low reliability can adversely affect the MIIV estimators. Also, using a small subset of MIIVs reduces small sample bias of coefficient estimates, but can lower the power of overidentification tests of equations.

This article argues that Mark uses matronymics, that is, identifying someone by the name of their mother, to construct female communities that resist Jesus’ message. This happens precisely twice in the Gospel of Mark, at Mk 6.3 (Jesus ‘the son of Mary’) and at Mk 6.22 (‘the daughter of Herodias’). Through comparison with other Greek uses of the matronymic, I will show that both scenes draw on the link between matronymics and female lines of authority, but with slightly different valences. Mark 6.3 heightens the female context of the Nazareth speakers and the hometown resistance, while Mk 6.22 is more concerned to establish a competing line of authority to that of Jesus in the person of Herodias and her daughter. My argument complements previous research into the Markan characterisation of the positive portrayals of multiple unnamed women in Mark’s Gospel (e.g. the women with the flow of blood (Mk 5.25–34), the Syrophoenician woman (Mk 7.24–30), the poor widow (Mk 12.41–4) and the woman who anoints Jesus in Bethany (Mk 14.3–9)). Joining the negative named women to the positive unnamed women reveals a unique feminine pattern of Markan characterisation, with its own dynamics and inflections.

Democracies may be defined as civic arrangements wherein all citizens have equal political standing. The problem is that no real-world democracy has successfully achieved this arrangement. Are they really democracies, then? For that matter, are there any democracies at all? Aikin and Talisse propose that ‘democracy’ is an aspirational concept, one that holds those who strive to achieve particular ends to exceedingly high standards. This makes democracies intelligible as democracies in their collective aspirations, but it also makes their failures instructive parts of what they are as democracies.

We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests, respectively. Next, we study the effect of socioeconomic status on brain structure, using data on education and income together with hippocampal volumes estimated by magnetic resonance imaging. By combining semiparametric estimation with latent variable modeling, GALAMMs allow a more realistic representation of how brain and cognition vary across the lifespan, while simultaneously estimating latent traits from measured items. Simulation experiments suggest that model estimates are accurate even with moderate sample sizes.

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya–Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combined model, the so-called factor tree copula model, with individual benefits from each of the two approaches. Rather than adding factors and causing computational problems and difficulties in interpretation and identification, a truncated vine structure is assumed on the residuals conditional on one or two latent variables. This structure can be better explained as a conditional dependence given a few interpretable latent variables. On the one hand, the parsimonious feature of factor models remains intact and any residual dependencies are being taken into account on the other. We discuss estimation along with model selection. In particular, we propose model selection algorithms to choose a plausible factor tree copula model to capture the (residual) dependencies among the item responses. Our general methodology is demonstrated with an extensive simulation study and illustrated by analyzing Post-Traumatic Stress Disorder.

Academic research libraries that build and steward collections in support of art research are always developing and executing strategies for their physical and virtual spaces, preservation, and access. NYU Libraries’ Institute of Fine Arts Library welcomes readers of a wide range of expertise, subject focus, and languages and works to make the library collections easier to discover and use in more creative ways in the pursuit of research, teaching, and learning. This work raises the question, whom do librarians turn to when they are responsible for subject areas or languages they may not know?

This article concerns collection development at NYU Libraries’ Institute of Fine Arts Library focusing on the African American and Black Diaspora, Asian, and Latin American & Caribbean art collections as distinct collections within a larger art library setting. In addition, it provides ways libraries can implement collection development policies that prioritize materials by underrepresented groups and offer community engagement with partners focused on inclusion, diversity, belonging, equity, and accessibility.

Mouse lemurs Microcebus spp. are small, nocturnal primates endemic to Madagascar. The genus is extraordinarily diverse, with 25 extant species, several of which have been described recently. The Endangered Claire's mouse lemur Microcebus mamiratra was first described in 2006, but, similarly to other newly described mouse lemurs, remains understudied, and estimates of its population size are unavailable, hampering effective conservation management. We conducted line transect distance sampling surveys of M. mamiratra across several habitat types in and around Lokobe National Park on the island of Nosy Be in north-western Madagascar. Using a systematic random design we surveyed 15 transects over a 6-week period in 2023, recording 92 detections from a total survey effort of 46.5 km. We estimate the density of M. mamiratra on Nosy Be to be 125.1 individuals/km2, which extrapolates to an estimate of c. 4,700 individuals across the forested areas of its range on the island. Our results indicate that Nosy Be harbours moderately high densities of M. mamiratra, with the highest encounter rates in the unprotected secondary and degraded forests around Lokobe National Park. Our population estimate will inform future conservation status assessments and conservation planning for this range-restricted species and provide a baseline for monitoring population changes over time. We present recommendations for the conservation of M. mamiratra and highlight the potential for lemur watching, sustained by the strong tourism industry on Nosy Be, to help protect lemur habitat and generate economic opportunities for local communities.

Legal and political battles about health policy in the immediate post-war years have cast a long shadow in Australia. The ‘civil conscription’ sub-provision in s 51(xxiiiA) (health and welfare power) of the Australian Constitution is still cited as a major barrier to developing health policy. But long after the High Court moved on from a very restrictive interpretation of Commonwealth powers, policymakers appear to be cautious about testing whether the Commonwealth has power to make laws about medical services to pursue a bold agenda about access, quality, and efficiency of medical care. In this article we will first describe the origin and phrasing of s 51(xxiiiA), the main head of power, then trace the development of the interpretation of the civil conscription sub-provision, and finally discuss whether politically realistic policy options are likely to founder on the shoals of High Court interpretation. We argue that the civil conscription limitation in s 51 (xxiiiA) in the Constitution looms larger as a policy constraint on regulation of health care by the Commonwealth government in the minds of decision-makers, and as a weapon in the hands of stakeholders, than contemporary analysis of it warrants.

The relationship between political philosophy and real-life politics is one that is heavily contested. On the one hand, it has been argued that political affiliation is a biasing force that stands in the way of our ability as political philosophers to maintain an objective perspective (Van der Vossen, 2015; 2020). On the other hand, it has been argued that political philosophers run the risk of bias whether they are politically active or not (Jones, 2020). In this paper, I nuance the debate at hand: I specify what kind of activism we should be concerned with as a biasing force, elaborate on what biases we should aim to mitigate as political philosophers, as well as what tools we have at our disposal in combatting biases within the discipline. This allows me to argue that participation in certain forms of political activism can be a powerful method for avoiding the most pernicious and pervasive biases we are prone to, namely biases against marginalised groups, and in favour of the political status quo. This has the implication that we must avoid a blanket ban on political activism within political philosophy, and instead recognise the epistemic merits of political activism where it is due.

Constrained fourth-order latent differential equation (FOLDE) models have been proposed (e.g., Boker et al. 2020) as alternative to second-order latent differential equation (SOLDE) models to estimate second-order linear differential equation systems such as the damped linear oscillator model. When, however, only a relatively small number of measurement occasions T are available (i.e., T = 50), the recommendation of which model to use is not clear (Boker et al. 2020). Based on a data set, which consists of T = 56 observations of daily stress for N = 44 individuals, we illustrate that FOLDE can help to choose an embedding dimension, even in the case of a small T. This is of great importance, as parameter estimates depend on the embedding dimension as well as on the latent differential equations model. Consequently, the wavelength as quantity of potential substantive interest may vary considerably. We extend the modeling approaches used in past research by including multiple subjects, by accounting for individual differences in equilibrium, and by including multiple instead of one single observed indicator.

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.

This article argues that critical and emerging technologies, evolving geopolitical dynamics and the urgent need to pursue the green agenda are changing the traditional approach of the European Union (EU) and its Member States towards their trade and security strategies, and this is particularly evident in the domain of export controls. In search of a balance between green energy, security and technological progress, this article explores the potential for a more cohesive and comprehensive regulatory framework for export controls at the EU level. It takes the debate beyond a technical level of export control lists to discuss geopolitical and strategic assumptions surrounding inter-State cooperation on the regulation of critical and emerging technologies and their components. The article underscores, in particular, the potential unintended repercussions of controls for the EU's technological future and the prospects of the green transition in Europe and beyond. Finally, it advocates for what is often overlooked in discussions: the necessity for the EU to set clear, long-term objectives for its export controls and to align them with the purposes and objectives of other EU economic instruments.