We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on…

Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…

Diagnostic classification models (DCMs) have grown in popularity as stakeholders increasingly desire actionable information related to students' skill competencies. Longitudinal DCMs offer a psychometric framework for providing estimates of students' proficiency status transitions over time. For both cross-sectional and longitudinal DCMs, it is…

Diagnostic classification models (DCMs) are psychometric models that can be used to estimate the presence or absence of psychological traits, or proficiency on fine-grained skills. Critical to the use of any psychometric model in practice, including DCMs, is an evaluation of model fit. Traditionally, DCMs have been estimated with maximum…

The fit of an item response model is typically conceptualized as whether a given model could have generated the data. We advocate for an alternative view of fit, "predictive fit", based on the model's ability to predict new data. We derive two predictive fit metrics for item response models that assess how well an estimated item response…

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which…

Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…

The distinguishing feature of dynamic assessment (DA) is the dialectical integration of assessment and instruction. However, how to design the targeted instruction or mediation has been relatively underexplored. To address this gap, this study proposes the attribute-based mediation model (AMM), an English-as-a-foreign-language listening mediation…

Scholars have lamented that current methods of assessing student performance do not align with contemporary views of learning as situated within students, contexts, and time. Here, we introduce and describe one theoretical--psychometric paradigm--termed "dynamic measurement"--designed to provide a valid representation of the way students…

There is increased use of Bayesian networks (BN) in educational assessment. In psychometrics, BN serves as a measurement model with high flexibility, suitable to model educational assessment data with a complex structure. BN is a novel psychometric approach and not all aspects of its application are well-known. The article aims to provide the…

The Expectancy-Value model has been extensively used to understand students' achievement motivation. However, recent studies propose the inclusion of cost as a separate construct from values, leading to the development of the Expectancy-Value-Cost model. This study aimed to adapt Kosovich et al.'s ("The Journal of Early Adolescence", 35,…

The study aims to examine the interplay of two critical constructs in evaluation: essential evaluator competency and evaluator practice. The research questions in this study, according to Smith (2008), are essentially, what he defined as "fundamental issues in evaluation." These issues fall into one or multiple of the four aspects…

More robust and rigorous psychometric models, such as multidimensional Item Response Theory models, have been advocated for survey applications. However, item responses may be influenced by construct-irrelevant variance factors such as preferences for extreme response options. Through empirical and simulation methods, this study evaluates the use…

Collaborative problem solving (CPS) is a critical competency in a variety of contexts, including the workplace, school, and home. However, only recently have assessment and curriculum reformers begun to focus to a greater extent on the acquisition and development of CPS skill. One of the major challenges in psychometric modeling of CPS is…