With decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

To understand how SEM methods perform in practice where models always have misfit, simulation studies often involve incorrect models. To create a wrong model, traditionally one specifies a perfect model first and then removes some paths. This approach becomes difficult or even impossible to implement in moment structure analysis and fails to control the amounts of misfit separately and precisely for the mean and covariance parts. Most importantly, this approach assumes a perfect model exists and wrong models can eventually be made perfect, whereas in practice models are all implausible if taken literally and at best provide approximations of the real world. To improve the traditional approach, we propose a more realistic and flexible way to create model misfit for multiple group moment structure analysis. Given (a) the model $\mathbf{\mu }(·)$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{{\upmu }}} (\cdot ) $$\end{document} and $\mathbf{\Sigma }(·)$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{\Sigma }} (\cdot ) $$\end{document}, (b) population model parameters ${\mathbf{\theta }}_0$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{{\uptheta }}} _0$$\end{document}, and (c) $F_1$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$F_1$$\end{document} and $F_2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$F_2$$\end{document} specified by the researcher, our method creates ${\mathbf{\mu }}^{\ast }$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{{\upmu }}} ^*$$\end{document} and ${\mathbf{\Sigma }}^{\ast }$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{\Sigma }} ^*$$\end{document} to simultaneously satisfy (a) ${\mathbf{\theta }}_0=argminF[{\mathbf{\mu }}^{\ast },{\mathbf{\Sigma }}^{\ast };\mathbf{\mu }(·),\mathbf{\Sigma }(·)]$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{{{\uptheta }}} _0 = \arg \min F[\varvec{{{\upmu }}} ^*, \varvec{{\Sigma }} ^*; \varvec{{{\upmu }}} (\cdot ), \varvec{{\Sigma }} (\cdot )]$$\end{document}, (b) the mean structure’s misfit equals $F_1$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$F_1$$\end{document}, and (c) the covariance structure’s misfit equals $F_2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$F_2$$\end{document}.

Ideal point discriminant analysis is a classification tool which uses highly intuitive multidimensional scaling procedures. However, in the last paper, Takane wrote about it. He concludes that the interpretation is rather intricate and calls that a weakness of the model. We summarize the conditions that provide an easy interpretation and show that in maximum dimensionality they can be obtained without any loss. For reduced dimensionality, it is conjectured that loss is minor which is examined using several data sets.

The year 2024 marks an important double anniversary for the journal The Americas. On the one hand, it marks 80 years of publication, with the first issue dated July 1944. On the other, it marks the quincentenary of the arrival of the first Franciscans to North America, when the first group of missionary friars landed in what is now Mexico. The history of the journal is rooted in the Franciscan Order. In April 1944 Franciscan historians from throughout North America met in Washington, DC, and founded the Academy of American Franciscan History. The goal of the Academy, as articulated in the records of that inaugural meeting is to “discover and assemble documents and books of Franciscan interest, to compile a complete bibliographical index of American Franciscana, to edit and publish documents, and to issue original historical works.”1 The Academy additionally pledged to publish a journal, a quarterly review of inter-American cultural history: The Americas.

In the Netherlands, national assessments at the end of primary school (Grade 6) show a decline of achievement on problems of complex or written arithmetic over the last two decades. The present study aims at contributing to an explanation of the large achievement decrease on complex division, by investigating the strategies students used in solving the division problems in the two most recent assessments carried out in 1997 and in 2004. The students’ strategies were classified into four categories. A data set resulted with two types of repeated observations within students: the nominal strategies and the dichotomous achievement scores (correct/incorrect) on the items administered.

It is argued that latent variable modeling methodology is appropriate to analyze these data. First, latent class analyses with year of assessment as a covariate were carried out on the multivariate nominal strategy variables. Results showed a shift from application of the traditional long division algorithm in 1997, to the less accurate strategy of stating an answer without writing down any notes or calculations in 2004, especially for boys. Second, explanatory IRT analyses showed that the three main strategies were significantly less accurate in 2004 than they were in 1997.

Art librarians work with images. It’s one of the things that separates us from many of our fellow subject librarians. As the academy continues to grapple with the benefits, drawbacks, and effects of AI, art librarians are uniquely positioned to teach students how to critically engage with AI image generators. Considerations concerning copyright, bias in datasets, formal analysis, and AI image generators’ potential as an art medium are some examples of topics that art librarians have at their disposal.

Item response theory scoring based on summed scores is employed frequently in the practice of educational and psychological measurement. Lord and Wingersky (Appl Psychol Meas 8(4):453–461, 1984) proposed a recursive algorithm to compute the summed score likelihood. Cai (Psychometrika 80(2):535–559, 2015) extended the original Lord–Wingersky algorithm to the case of two-tier multidimensional item factor models and called it Lord–Wingersky algorithm Version 2.0. The 2.0 algorithm utilizes dimension reduction to efficiently compute summed score likelihoods associated with the general dimensions in the model. The output of the algorithm is useful for various purposes, for example, scoring, scale alignment, and model fit checking. In the research reported here, a further extension to the Lord–Wingersky algorithm 2.0 is proposed. The new algorithm, which we call Lord–Wingersky algorithm Version 2.5, yields the summed score likelihoods for all latent variables in the model conditional on observed score combinations. The proposed algorithm is illustrated with empirical data for three potential application areas: (a) describing achievement growth using score combinations across adjacent grades, (b) identification of noteworthy subscores for reporting, and (c) detection of aberrant responses.

Reporting effect size index estimates with their confidence intervals (CIs) can be an excellent way to simultaneously communicate the strength and precision of the observed evidence. We recently proposed a robust effect size index (RESI) that is advantageous over common indices because it’s widely applicable to different types of data. Here, we use statistical theory and simulations to develop and evaluate RESI estimators and confidence/credible intervals that rely on different covariance estimators. Our results show (1) counter to intuition, the randomness of covariates reduces coverage for Chi-squared and F CIs; (2) when the variance of the estimators is estimated, the non-central Chi-squared and F CIs using the parametric and robust RESI estimators fail to cover the true effect size at the nominal level. Using the robust estimator along with the proposed nonparametric bootstrap or Bayesian (credible) intervals provides valid inference for the RESI, even when model assumptions may be violated. This work forms a unified effect size reporting procedure, such that effect sizes with confidence/credible intervals can be easily reported in an analysis of variance (ANOVA) table format.

The Australian Constitution was drafted, and the institutions of national government were established, during a period in which the atomism of laissez-faire liberalism was being rejected. Instead, progressive liberals of the era were searching for ways to encourage collective action and social ties, believing that this would, in turn, enhance personal wellbeing. This article contends that a clearer appreciation of the influence of the ‘social’ turn in liberalism upon Australia’s constitutional and institutional development might contribute to a fuller understanding of Australia’s distinctive constitutional and public law traditions.

The Court of Justice of the European Union (the Court) has famously sought to eliminate intra-European Union (EU) investment arbitration under bilateral investment treaties and the multilateral Energy Charter Treaty. In doing so, the Court has navigated settled case law concerning commercial arbitration. In this regard, Achmea and subsequent rulings are premised upon a distinction drawn by the Court between investment and contract-based arbitration, based on the origin of arbitral proceedings and the intensity of the review of the relevant award. This article demonstrates that this distinction disregards important commonalities and the diversity of enforcement regimes. It is further argued that, even in the light of Achmea, EU law rightly permits intra-EU arbitration under investment contracts, that is, contracts between States and foreign investors. The article thus examines investment contract-based arbitration as the only surviving form of intra-EU investment arbitration and cautions against expansive applications of the Achmea reasoning to contractual agreements, signs of which are already emerging.

In this paper, we present the academic genealogy of presidents of the Psychometric Society by constructing a genealogical tree, in which Ph.D. students are encoded as descendants of their advisors. Results show that most of the presidents belong to five distinct lineages that can be traced to Wilhelm Wundt, James Angell, William James, Albert Michotte or Carl Friedrich Gauss. Important psychometricians Lee Cronbach and Charles Spearman play only a marginal role. The genealogy systematizes important historical knowledge that can be used to inform studies on the history of psychometrics and exposes the rich and multidisciplinary background of the Psychometric Society.

Fit indices are highly frequently used for assessing the goodness of fit of latent variable models. Most prominent fit indices, such as the root-mean-square error of approximation (RMSEA) or the comparative fit index (CFI), are based on a noncentrality parameter estimate derived from the model fit statistic. While a noncentrality parameter estimate is well suited for quantifying the amount of systematic error, the complex weighting function involved in its calculation makes indices derived from it challenging to interpret. Moreover, noncentrality-parameter-based fit indices yield systematically different values, depending on the indicators’ level of measurement. For instance, RMSEA and CFI yield more favorable fit indices for models with categorical as compared to metric variables under otherwise identical conditions. In the present article, approaches for obtaining an approximation discrepancy estimate that is independent from any specific weighting function are considered. From these unweighted approximation error estimates, fit indices analogous to RMSEA and CFI are calculated and their finite sample properties are investigated using simulation studies. The results illustrate that the new fit indices consistently estimate their true value which, in contrast to other fit indices, is the same value for metric and categorical variables. Advantages with respect to interpretability are discussed and cutoff criteria for the new indices are considered.

Protein circular dichroism (CD) and infrared absorbance (IR) spectra are widely used to estimate the secondary structure content of proteins in solution. A range of algorithms have been used for CD analysis (SELCON, CONTIN, CDsstr, SOMSpec) and some of these have been applied to IR data, though IR is more commonly analysed by bandfitting or statistical approaches. In this work we provide a Python version of SELCON3 and explore how to combine CD and IR data to best effect. We used CD data in Δε/amino acid residue and scaled the IR spectra to similar magnitudes. Normalising the IR amide I spectra scaled to a maximum absorbance of 15 gives best general performance. Combining CD and IR improves predictions for both helix and sheet by ~2% and helps identify anomalously large errors for high helix proteins such as haemoglobin when using IR data alone and high sheet proteins when using CD data alone.

Stephen Yablo suggested that the relation of mental properties to physical properties is the same as that between red and scarlet: one of determinable property to determinate property. So just as being scarlet is a specific way of being red, on Yablo’s proposal a subject’s having a certain neurological property (c-fibres firing, say) is a specific way of a subject’s having a certain mental property (pain, in this case). I explain the virtues of this theory, in particular as defended and developed by Jessica Wilson, but raise some problems for it. I then describe a novel theory of the mental/physical relationship, which inverts the Yablo-Wilson proposal. On this theory mental properties, notably phenomenal properties – or, as I will say, qualia – are determinates of determinable physical properties. I explain the virtues of this view, and argue that they at least match, and plausibly exceed, those of the Yablo-Wilson theory. In particular, this new theory is able to account for certain prominent perplexities of the mind/body problem that tend to go unexplained. I distinguish the view from nearby theories, in particular the increasingly popular ‘Russellian monism’. I end by likening it to a recent interpretation of Aristotle’s philosophy of mind due to David Charles.

Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area is the model that was proposed by Gottman and Murray, which is based on nonlinear coupled difference equations. In this paper, it is shown that their model is a special case of the threshold autoregressive (TAR) model. As a consequence, we can make use of existing knowledge about TAR models with respect to parameter estimation, model alternatives and model selection. We propose a new estimation procedure and perform a simulation study to compare it to the estimation procedure developed by Gottman and Murray. In addition, we include an empirical example based on interaction data of three dyads.

We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while countries within these groups are still allowed to be heterogeneous. A simulation study is conducted that shows that all parameters can be recovered. We also apply the model to real data on the two components of affective subjective well-being: positive affect and negative affect. The psychometric behavior of these two scales is studied in 28 countries across four continents.

The paper presents inequalities between four descriptive statistics that can be expressed in the form [P − E(P)]/[1 − E(P)], where P is the observed proportion of agreement of a k × k table with identical categories, and E(P) is a function of the marginal probabilities. Scott’s π is an upper bound of Goodman and Kruskal’s λ and a lower bound of both Bennett et al. S and Cohen’s κ. We introduce a concept for the marginal probabilities of the k×k table called weak marginal symmetry. Using the rearrangement inequality, it is shown that Bennett et al. S is an upper bound of Cohen’s κ if the k×k table is weakly marginal symmetric.

We introduce a novel way to elicit individuals’ strength of altruistic motivation in the context of charitable donations, ranging from pure warm glow to pure altruism. Using the giving-type elicitation task of Gangadharan et al. (2018) and assuming that individuals maximise a Cobb–Douglas impure altruism utility function, as is used in Ottoni-Wilhelm et al. (2017), we can uniquely identify the strength of altruistic motivation for impure altruists, which is typically found to be the largest category of donors. We compare the introduced measure to an alternative survey-based elicitation from Carpenter (2021).