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ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social sciences in general. In this article, we present a new feature introduced in the recent version of the package, called "SIA modules", which allows researchers and practitioners to offer new analytical methods for broader use via add-on extensions. We describe how to build the add-on modules with the support of the new SIAtools package and demonstrate the concepts using sample modules from the newly introduced SIAmodules package. SIA modules are designed to integrate with and build upon the SIA interactive application, enabling them to leverage the existing infrastructure for tasks such as data uploading and processing. They can access a range of outputs from various analyses, including item response theory models, exploratory factor analysis, or differential item functioning models. Because SIA modules come in R packages (or extend the existing ones), they may come bundled with their datasets, use object-oriented systems, or even compiled code. We discuss the possibility of broader use of the concept of SIA modules in other areas.

This work proposes a 4-parameter factor analytic (4P FA) model for multi-item measurements composed of binary items as an extension to the dichotomized single latent variable FA model. We provide an analytical derivation of the relationship between the newly proposed 4P FA model and its counterpart in the item response theory (IRT) framework, the 4P IRT model. A Bayesian estimation method for the proposed 4P FA model is provided to estimate the four item parameters, the respondents' latent scores, and the scores cleaned of the guessing and inattention effects. The newly proposed algorithm is implemented in R and Python, and the relationship between the 4P FA and 4P IRT is empirically demonstrated using real datasets from admission tests and the assessment of anxiety.

Pre-trained language models have been known to perpetuate biases from the underlying datasets to downstream tasks. However, these findings are predominantly based on monolingual language models for English, whereas there are few investigative studies of biases encoded in language models for languages beyond English. In this paper, we fill this gap by analysing gender bias in West Slavic language models. We introduce the first template-based dataset in Czech, Polish, and Slovak for measuring gender bias towards male, female and non-binary subjects. We complete the sentences using both mono- and multilingual language models and assess their suitability for the masked language modelling objective. Next, we measure gender bias encoded in West Slavic language models by quantifying the toxicity and genderness of the generated words. We find that these language models produce hurtful completions that depend on the subject's gender. Perhaps surprisingly, Czech, Slovak, and Polish language models produce more hurtful completions with men as subjects, which, upon inspection, we find is due to completions being related to violence, death, and sickness.

This paper explores innovations to parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates. We introduce a new implementation of the EM algorithm and propose a new algorithm based on the parametrized link function. The two novel iterative algorithms are compared to existing methods in a simulation study. Additionally, the study examines software implementation, including the specification of initial values for numerical algorithms and asymptotic properties with an estimation of standard errors. Overall, the newly proposed algorithm based on the parametrized link function outperforms other procedures, especially for small sample sizes. Moreover, the newly implemented EM algorithm provides additional information regarding respondents' inclination to guess or pretend and slip or dissimulate when answering the item. The study also discusses applications of the methods in the context of the detection of differential item functioning and addresses the measurement error. Methods are offered in the difNLR package and in the interactive application of the ShinyItemAnalysis package; demonstration is provided using real data from psychological and educational assessments.

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome; the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the inter-rater reliability and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss possible other uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health-related measurement and implement the computations in IRR2FPR R package.

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors to select the best performing model, and we suggest using Bayesian model-averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion Bayes factors considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer-review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs.

There is growing interest in using measures of teacher applicant quality to improve hiring decisions, but the statistical properties of such measures are poorly understood. We present evidence on structured ratings solicited from teacher applicants' references. We find that the reference ratings capture only one underlying dimension of applicant quality, which may indicate a need to broaden the range of questions posed to professional references. Point estimates of inter-rater reliability range between 0.23 and 0.31 and are significantly lower for novice applicants. It is difficult to judge whether these levels of reliability are high or low in the current context given so little evidence on comparable applicant assessment tools.

Inter-rater reliability, commonly assessed by intra-class correlation coefficient ICC, is an important index for describing the extent to which there is consistency amongst two or more raters in assigned measures. In organizational research, the data structure is often hierarchical and designs deviate substantially from the ideal of a balanced (fully crossed or nested) design. Also, often it is necessary to include covariates in the model, making it impossible to use traditional correlation-based or analysis of variance (ANOVA)-based methods for estimation of inter-rater reliability. We advocate the use of hierarchical (mixed effect model)-based methods, where variance components can be estimated by restricted maximum likelihood or Bayesian approaches. In this work, we use data from teacher hiring in Spokane public schools to demonstrate the usage of hierarchical (mixed effect) models to estimate inter-rater reliability and to demonstrate how reliability can be estimated with more complex data structures. We generally find low levels of inter-rater reliability, though this overall reliability varies according to whether the measure is assessed "across' or "within" schools, or even within job openings. We also find evidence that inter-rater reliability of some subcomponents of the hiring rubric varies according to the type of position to which applicants are applying, or according to whether the applicant is internal or from outside of the district. The direct effect of reliability on predictive power of the selection instrument is demonstrated and policy implications for public school hiring are discussed. [Additional support for this study was provided by the Czech Science Foundation through grant #15-15856Y.]