This study examines the maritime networks of Patara during the fourth and third centuries BC, employing numismatic and amphora evidence as proxies indicative of the city’s significant role in ancient maritime routes. The two types of evidence offer perspectives on two different types of connectivity. The numismatic analysis focuses on the presence in Patara of low-value civic bronze coins minted by non-Lycian cities, thereby offering a window onto human mobility at the scale of the individual traveller, not necessarily the traders. In contrast, an examination of transport amphorae imported to Patara helps to reveal the extent of Patara’s commercial connections. These findings enhance our comprehension of Patara’s crucial role in ancient maritime networks, illuminating the interdependence of Mediterranean societies during this period. They demonstrate the complexity of these networks, suggesting that different kinds of networks operated simultaneously. This research contributes to the discourse on ancient maritime mobilities, considering the overlaps and interactions between different forms and scales of connectivity.

Effect size indices are useful tools in study design and reporting because they are unitless measures of association strength that do not depend on sample size. Existing effect size indices are developed for particular parametric models or population parameters. Here, we propose a robust effect size index based on M-estimators. This approach yields an index that is very generalizable because it is unitless across a wide range of models. We demonstrate that the new index is a function of Cohen’s d, $R^2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$R^2$$\end{document}, and standardized log odds ratio when each of the parametric models is correctly specified. We show that existing effect size estimators are biased when the parametric models are incorrect (e.g., under unknown heteroskedasticity). We provide simple formulas to compute power and sample size and use simulations to assess the bias and standard error of the effect size estimator in finite samples. Because the new index is invariant across models, it has the potential to make communication and comprehension of effect size uniform across the behavioral sciences.

Populism is both prolific and resilient. By now, populist forces around the globe have managed to enter the highest echelons of power (Rovira Kaltwasser and Taggart 2016). It is no wonder that the contemporary academic debate has shifted its focus to exploring the consequences of populism in power, particularly its impact on democracy. Although populism and democracy are not synonymous, the representation of “the people” is a central claim to both. Most populism scholars agree that “all forms of populism without exception involve some kind of exaltation of and appeal to ‘the people’” (Canovan 1981, 94). However, depending on which democratic ideas are emphasized over others—as well as which political practices and structures are favored to institutionalize these ideas (Dahl 1991; Held 2006; Lijphart 2012)—the basic tenet of the “rule by the people” may have many different meanings.

Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell–cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.

The rise of populism as a global phenomenon has captured the attention of scholars and raised concerns about its impact on democracy. Thanks to a growing academic consensus around an ideational definition of populism, one can observe the generation of important cumulative knowledge on the relationship between populism and democracy. Political science has been at the forefront of this development, and this symposium seeks to both offer state-of-the art information on this topic and discuss blind spots that future studies should try to address.

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Careless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed.Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance–difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.

In this rejoinder, we examine some of the issues Peter Bentler, Eunseong Cho, and Jules Ellis raise. We suggest a methodological solid way to construct a test indicating that the importance of the particular reliability method used is minor, and we discuss future topics in reliability research.

This article draws on documentary texts from multilingual archives of early Islamic Central Asia to illustrate connections between the Arabic and Middle Iranian scribal world. Here, I contend that some lesser-known evidence from Sogdia contributes new elements to current debates on the contact between Arabic and Middle Iranian scribal traditions and provides a measure of “intensity” of Arab rule in the region more generally. In particular, ostraca from various Transoxanian administrative centers provide documentary confirmation that a class of biliterate Arabic-Sogdian scribes was active in the local bureaucracy as early as the mid-8th century. When viewed in dialogue with archives from coeval Iran and Iraq, the Transoxanian evidence helps lead to a more nuanced understanding of the so-called “Pahlavi diplomatic substrate” model.

In this article, we present the findings of an oral history project on the past, present, and future of psychometrics, as obtained through structured interviews with twenty past Psychometric Society presidents. Perspectives on how psychometrics should be practiced vary strongly. Some presidents are psychology-oriented, whereas others have a more mathematical or statistical approach. The originally strong relationship between psychometrics and psychology has weakened, and contemporary psychometrics has become a diverse and multifaceted discipline. The presidents are confident psychometrics will continue to be relevant but believe psychometrics needs to become better at selling its strong points to relevant research areas. We recommend for psychometrics to cherish its plurality and make its goals and priorities explicit.

In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions.

Empirical evidence has shown that people with better self-control to a greater extent have the self-regulatory ability to act in line with their long-term goals. In this pre-registered study, the relationship between self-control and self-regulatory behavior was investigated both directly and indirectly, i.e., through affective forecasting ability. This is of great interest as it is necessary to be able to forecast one's emotional response to future events in order to make choices that maximize one's happiness. However, in a laboratory experiment with undergraduate students, I found no evidence of self-control being associated with affective forecasting ability, or that people with better self-control more often acted in a way that maximized their expected happiness.

Problem solving has been recognized as a central skill that today’s students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student’s problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students’ problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.

Standard response formats such as rating or visual analogue scales require respondents to condense distributions of latent states or behaviors into a single value. Whereas this is suitable to measure central tendency, it neglects the variance of distributions. As a remedy, variability may be measured using interval-response formats, more specifically the dual-range slider (RS2). Given the lack of an appropriate item response model for the RS2, we develop the Dirichlet dual response model (DDRM), an extension of the beta response model (BRM; Noel & Dauvier in Appl Psychol Meas, 31:47–73, 2007). We evaluate the DDRM’s performance by assessing parameter recovery in a simulation study. Results indicate overall good parameter recovery, although parameters concerning interval width (which reflect variability in behavior or states) perform worse than parameters concerning central tendency. We also test the model empirically by jointly fitting the BRM and the DDRM to single-range slider (RS1) and RS2 responses for two Extraversion scales. While the DDRM has an acceptable fit, it shows some misfit regarding the RS2 interval widths. Nonetheless, the model indicates substantial differences between respondents concerning variability in behavior. High correlations between person parameters of the BRM and DDRM suggest convergent validity between the RS1 and the RS2 interval location. Both the simulation and the empirical study demonstrate that the latent parameter space of the DDRM addresses an important issue of the RS2 response format, namely, the scale-inherent interdependence of interval location and interval width (i.e., intervals at the boundaries are necessarily smaller).

In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie and Holland give a theoretically important characterization of the marginal Rasch model. Because their representation of the marginal Rasch model does not involve any latent trait, nor any specific distribution of a latent trait, it opens up the possibility for constructing a Markov chain - Monte Carlo method for Bayesian inference for the marginal Rasch model that does not rely on data augmentation. Such an approach would be highly efficient as its computational cost does not depend on the number of respondents, which makes it suitable for large-scale educational measurement. In this paper, such an approach will be developed and its operating characteristics illustrated with simulated data.

In the major port city of Patara on the southern coast of Roman Asia Minor, excavations unearthed a pharos (lighthouse) with an inscription that referred to an antipharos (a structure ‘opposite’ the lighthouse). It is unknown where the antipharos stood in Patara’s harbour, and scholars’ brief speculations about its location all assume that the antipharos was a second lighthouse. Yet a number of factors combine to suggest that there was only one pharos at Patara, including cautious Roman nocturnal sailing practices, the norm of single lighthouses in the ancient world, evidence of the pharos’ high visibility, and the only other instance of the word antipharos referring to something other than an operating lighthouse. Instead, the antipharos was probably either an unlit tower or a beacon instead of a lighthouse. I establish six possible locations for such an antipharos, and consider their likelihood based on how they might have ameliorated dangers to sailors entering the harbour. While there is not enough evidence to be completely confident, a rock islet that was in the middle of ancient Patara’s harbour emerges as the most probable location for the antipharos. The choice to build both a pharos and an antipharos, and where to place them, can illuminate the decision processes behind Roman harbour construction and the currently little-understood meaning of the word antipharos in antiquity.

We consider the dependence of a broad class of chance-corrected weighted agreement coefficients on the weighting scheme that penalizes rater disagreements. The considered class encompasses many existing coefficients with any number of raters, and one real-valued power parameter defines the weighting scheme that includes linear, quadratic, identity, and radical weights. We obtain the first-order and second-order derivatives of the coefficients with respect to the power parameter and decompose them into components corresponding to all pairs of different category distances. Each component compares its two distances in terms of the ratio of observed to expected-by-chance frequency. A larger ratio for the smaller distance than the larger distance contributes to a positive relationship between the power parameter and the coefficient value; the opposite contributes to a negative relationship. We provide necessary and sufficient conditions for the coefficient value to increase or decrease and the relationship to intensify or weaken as the power parameter increases. We use the first-order and second-order derivatives for corresponding measurement. Furthermore, we show how these two derivatives allow other researchers to obtain quite accurate estimates of the coefficient value for unreported values of the power parameter, even without access to the original data.

We discuss properties that association coefficients may have in general, e.g., zero value under statistical independence, and we examine coefficients for 2×2 tables with respect to these properties. Furthermore, we study a family of coefficients that are linear transformations of the observed proportion of agreement given the marginal probabilities. This family includes the phi coefficient and Cohen’s kappa. The main result is that the linear transformations that set the value under independence at zero and the maximum value at unity, transform all coefficients in this family into the same underlying coefficient. This coefficient happens to be Loevinger’s H.