Longitudinal mental health assessments in mobile health (mHealth) settings are useful for monitoring subjects’ mental health statuses but are often difficult to analyze because they generally appear on an ordinal scale and at unequal time intervals. In this article, we explore the use of Gaussian processes (GPs) and hierarchical modeling techniques to understand mental health trajectories based on repeated multi-item mHealth surveys on a Likert scale. We introduce the GP model for health trajectories, which is based on item response theory. In the study of trajectories, a subject’s longitudinal collection of mHealth responses can be thought of as a single high-dimensional observation. We show how the GP is flexible enough to capture trends in individual trajectories even with the challenges associated with high-dimensional data. We also demonstrate how basis splines can be used to effectively capture nonlinear trends in the mean function of the GP. The high-dimension and ordinal nature of the data often make sampling from the posterior distribution in a Bayesian setting too slow to be practical. We show that using a Hilbert approximation for the GP trajectories can facilitate efficient sampling. We apply these methods to a longitudinal study that monitored college students’ self-esteem.

Psychological research has long centered around questionnaire assessments, but now digital devices, especially smartphones, enable the collection of real-world behavioral data through mobile sensing. While this data collection method offers unique opportunities, it also introduces new methodological challenges, as mobile-sensing data are highly complex and high in dimensionality (i.e., timestamped events with millisecond resolution), requiring advanced preprocessing to derive psychologically meaningful variables. This article highlights these challenges by reviewing the current state of data preprocessing based on app usage logs from smartphones. Afterward, it presents three preprocessing cases that vary in complexity across the dimensions of data enrichment—which involves adding context to raw data by integrating information from external and internal sources (including ecological momentary assessments)—and data aggregation—which entails summarizing data in different ways, from basic descriptive statistics to sophisticated machine-learning models. For each case, potential pitfalls are identified, and extensions are discussed to refine our preprocessing pipelines and accommodate different data types and research questions. By outlining these preprocessing strategies, this manuscript demonstrates the rich potential of mobile-sensing data for extracting nuanced behavioral variables beyond simple person-level summaries and aims to inspire the development of more advanced research questions based on sensing data.

In psychometric sciences, such as social or behavioral sciences, and, similarly, in medical sciences, it is increasingly common to deal with longitudinal data organized as high-dimensional multidimensional arrays, also known as tensors. Within this framework, the time-continuous property of longitudinal data often implies a smooth functional structure on one of the tensor modes. To help researchers investigate such data, we introduce a new tensor decomposition approach based on the PARAFAC decomposition. Our approach allows researchers to represent a high-dimensional functional tensor as a low-dimensional set of functions and feature matrices. Furthermore, to capture the underlying randomness of the statistical setting more efficiently, we introduce a probabilistic latent model in the decomposition. A covariance-based block-relaxation algorithm is derived to obtain estimates of model parameters. Thanks to the covariance formulation of the solving procedure and thanks to the probabilistic modeling, the method can be used in sparse and irregular sampling schemes, making it applicable in numerous settings. Our approach is applied in the psychometric setting to help characterize multiple neurocognitive scores observed over time in the Alzheimer’s Disease Neuroimaging Initiative study. Finally, intensive simulations show a notable advantage of our method in reconstructing tensors.

Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed three-dimensional (3D) path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.

The growing use of computer-based assessments has produced complex process data that capture learners’ cognitive and behavioral processes in real time. Among these, eye-tracking data provide rich temporal information on how individuals attend to and process visual information during problem solving. Yet, analyzing such high-dimensional, temporally dependent, and multimodal data remains a methodological challenge. This study introduces a two-component data-analytic framework (DAK) for integrating and interpreting structured and unstructured data in educational assessments. The first component employs a time-aware long short-term memory Autoencoder to extract latent features representing dynamic visual attention patterns. The model extends conventional architectures by incorporating fixation duration and elapsed time between actions, using a data-driven temporal decay function, and optimizing a multi-target reconstruction objective. The second component integrates these extracted features through clustering, categorical data analyses, and mixed-effects modeling to generate construct-relevant validity evidence for test-taking and learning behaviors. We demonstrate the DAK using structured scores and unstructured eye-tracking data from a spatial rotation learning program. Results reveal distinct behavioral patterns linked to test performance and intervention effectiveness, highlighting the potential of multimodal process data to advance psychometric modeling and instrument design.

This article develops an analysis pipeline for quantifying and relating mouth shape variation to the emotions perceived from facial expressions. We use open-source data that contains ratings from 802 fairgoers on 27 smile-like expressions. Each rater was given a list of seven emotions (happy, sad, anger, contempt, fear, surprise, and disgust) and asked to select all of the words that best described the facial expression. To develop a generalizable method for quantifying mouth shape variation, we leverage statistical shape analysis techniques to parameterize each mouth’s shape in terms of 30 systematically placed landmarks that outline the upper and lower lips. Furthermore, we demonstrate that a three-dimensional representation of these landmark coordinates produces an interpretable feature set that outperforms the original and full-dimensional feature sets in terms of predictive performance. To connect the mouth shape features to the emotion ratings, we develop a nonparametric multinomial regression model that is capable of shrinkage and selection with high-dimensional predictors. Our results demonstrate that the proposed method can produce easily interpretable model predictions that enhance our understanding of the nature in which subtle variations in mouth shape affect the perception of a facial expression.

Traditional perceptual models are ill-equipped for the high-dimensional data, such as text embeddings, central to modern psychology and AI. We introduce the double machine learning lens model, a framework that utilizes machine learning to handle such data. We applied this model to analyze how a modern AI and human perceivers judge social class from 9,513 aspirational essays written by 11-year-olds in 1969. A systematic comparison of 45 analytical approaches revealed that regularized linear models using dimensionality-reduced language embeddings significantly outperformed traditional dictionary-based methods and more complex non-linear models. Our top model accurately predicted human $(R^{2}_{CV} =0.61)$ and AI $(R^{2}_{CV} =0.56)$ social class perceptions, capturing over 85% of the total accuracy. These results suggest that “unmodeled knowledge” in perception may be an artifact of insufficient measurement tools rather than an unmeasurable intuitive process. We find that both AI and humans use many of the same textual cues (e.g., grammar, occupations, and cultural activities), only a subset of which are valid. Both appear to amplify subtle, real-world patterns into powerful, yet potentially discriminatory heuristics, where a small difference in actual social class creates a large difference in perception.

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact. In this study, we analyze the multi-block HCP data using data integration via analysis of subspaces (DIVAS). We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14% of the variation in functional connectivity (FC) and roughly 12% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and substance use) subspace, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain’s role in physiological responses to increased substance use. Our findings are validated on genetically relevant subjects not studied in the main analysis.

Large-scale assessment data typically include numerous variables, often affected by missing values. Motivated by the challenges arising in this framework, we extend the knockoffs method for selecting predictors to settings with missing values. Our proposal relies on a preliminary phase of multiple imputation (MI) of missing values. Each imputed dataset is then processed using a suitable knockoff filter. We evaluate the performance of the proposed method through simulation studies, showing satisfactory results consistent with a recently advocated cutting-edge method. We apply the method to large-scale assessment data collected by INVALSI on test scores of Italian students in grade 5, including many background variables. This case study is challenging, as most predictors have unordered categories, a setting not considered by traditional knockoff methods. In addition, some of the key predictors are affected by missing values. Our proposal to implement the knockoffs method within an MI framework is feasible, flexible, and effective.

Change point analysis (CPA) detects structural shifts in a response sequence by partitioning it into segments with different statistical properties. This paper proposes three CPA approaches based on the Schwarz information criterion (SIC; hereafter SIC-CPA): response data only, response time (RT) data only, and the combination of response and RT data, to detect the prevalent test speededness in time-limit tests. To comprehensively investigate the efficiency and accuracy of the proposed approaches, six simulation studies were conducted under diverse conditions. Simulation results demonstrate that SIC-CPA can effectively enhance the power of change point detection and reduce Type I errors, while improving computational efficiency compared to the likelihood ratio and Wald tests. Moreover, the SIC-CPA combining response and RT data outperforms the SIC-CPA based solely on RTs, and the latter is substantially superior to the SIC-CPA based solely on responses. In addition, SIC-CPA accurately identifies two change points in RT patterns, corresponding to early warm-up and later test speededness. Using an iterative detect–clean–recalibrate procedure, SIC-CPA achieves more reliable Type I error control than likelihood ratio and Wald tests when item parameters are estimated from contaminated data. A real data analysis was conducted to show the application of the proposed approaches.

While the joint modeling of item responses and response times (RTs) has received considerable attention, most existing approaches remain limited to dichotomous items and are not applicable to assessments involving polytomous or mixed-format items. To address this limitation, this article proposes a novel joint modeling framework for graded item responses and RTs. Specifically, we develop a conditional RT model given item responses and integrate it with a marginal response model based on Samejima’s graded response model, yielding a conditional joint model for graded item responses and RTs. The model is then embedded within a two-level hierarchical framework to account for the relationship between ability and speed at the population level. A key methodological contribution is the development of a stochastic approximation EM (SAEM) algorithm for estimating the proposed model, which efficiently computes its marginal maximum likelihood estimates. Simulation studies demonstrate the accurate parameter recovery of the SAEM algorithm and indicate that the proposed model outperforms the hierarchical model assuming conditional independence across various testing conditions. Finally, an empirical analysis using data from the 2022 Programme for International Student Assessment illustrates the effectiveness of the graded response–response time model in large-scale assessments.

Violations of the assumption of local independence are a fundamental issue in item response theory as they threaten model validity and bias the parameter estimates. For such a reason, a plethora of tests and approaches has been devised in the last 40 years to detect or to model such violations. Nonetheless, local dependence (LD) remains an open problem, with somewhat blurred boundaries due to the lack of a general framework for dealing with the different notions of dependence that have been suggested in the literature. The present contribution has a two-fold aim: On the one hand, to review and collect some of the approaches available in the literature; on the other hand, by following a unified perspective on assessment models introduced by Noventa et al. (2024, Journal of Mathematical Psychology 122, 102872) to suggest a possible systematization of some existing and some new approaches to LD. As a result, deterministic and probabilistic modeling mechanisms of LD are formalized and discussed.

In a Bayesian modeling approach for differential item functioning (DIF), the dependence structure of (non-)uniform DIF is represented by a structured covariance matrix. Item–group interactions (uniform DIF) but also item-specific person–group interactions (non-uniform DIF) are represented by additional correlations in (latent) item responses. DIF in discriminations and difficulties is modeled simultaneously across items and multiple groups in the covariance matrix, making it possible to examine (non-)uniform DIF without needing anchor item(s) or multiple-step procedures. The modeling framework is very efficient, avoids the computation of any interaction parameter, and requires only a single covariance parameter for the DIF assessment of an item parameter for any number of groups. This supports a simultaneous non-uniform DIF analysis of all items, even for small sample sizes. The proposed DIF procedure is applied to PISA data, where the advantages of the method are illustrated and compared to a multiple-group IRT analysis.

In this study, we review interpretations and connections of chance-corrected agreement coefficients with quadratic weights, applicable when raters classify objects or subjects on an ordinal scale. Whereas correlation interpretations exist for coefficients with noninterchangeable raters, represented by Cohen’s two-rater and Conger’s multirater kappas, interpretations are essentially absent for interchangeable raters, represented by Fleiss’ kappa and its two-rater version, Scott’s pi. We show that Fleiss’ quadratically weighted kappa equals Lin’s generalized concordance correlation coefficient after recentering the rater means and covariance matrix at the grand mean. Furthermore, it equals the Pearson product–moment correlation and associated regression slope for two random raters, computed after concatenating the ratings from all possible rater pairs, including reversed rater order. Next, we demonstrate that Fleiss’ and Conger’s quadratically weighted kappas are linear transformations of each other, entirely determined by the pairwise differences in rater means and corresponding variances. As these kappas coincide if the rater means coincide, the conceptual distinction between interchangeable and noninterchangeable raters becomes empirically irrelevant if raters have (approximately) the same mean rating, even with substantially different rating distributions. Finally, Fleiss’ quadratically weighted kappa (i.e., interchangeable raters) cannot exceed Conger’s (i.e., noninterchangeable raters).

In this study, we develop statistical methods for bivariate causal learning using higher-order moment information from two-wave longitudinal data. Within the framework of linear non-Gaussian models, we derive tests based on joint cumulants and propose a multiple-testing algorithm to detect the existence of a longitudinal causal path in the presence of hidden confounders. By combining temporal information from longitudinal designs with higher-order distributional properties of the observed data, the proposed method allows researchers to draw valid causal conclusions under realistic scenarios commonly encountered in psychological studies. The performance of the proposed algorithm is evaluated through simulation studies. As expected for higher-moment methods, the proposed algorithm may require larger sample sizes than typically needed for second-moment methods to achieve high statistical power. Results demonstrate that the proposed algorithm provides sufficient evidence to establish the existence of a longitudinal causal path, particularly in large-scale data analysis. We then present two real-world data examples to illustrate the application of the causal learning algorithm in psychological research. Finally, we discuss practical implications and potential future research directions.

Computerized adaptive tests (CATs) play a crucial role in educational assessment and diagnostic screening in behavioral health. Unlike traditional linear tests that administer a fixed set of pre-assembled items, CATs adaptively tailor the test to an examinee’s latent trait level based on their previous responses. We introduce a novel CAT system that builds on recent advances in Bayesian multivariate IRT. Our approach leverages direct sampling from the latent factor posterior distributions, significantly accelerating existing information-theoretic item-selection methods by eliminating the need for computationally intensive Markov chain Monte Carlo simulations. To address the potential suboptimality of one-step-ahead item-selection rules, we also develop a double deep Q-learning algorithm that efficiently learns an optimal item-selection policy offline using a calibrated item bank. Through simulation and real-data studies, we demonstrate that our approach not only accelerates existing item-selection methods but also highlights the potential of reinforcement learning (RL) in CATs. Notably, our Q-learning-based strategy consistently achieves the fastest posterior variance reduction, leading to earlier test termination. These results demonstrate the promise of combining exact posterior sampling with RL to deliver scalable, high-precision CATs.