In computerized adaptive testing (CAT), information-based item selection rules (ISRs), such as maximum Fisher information (MFI), often excessively rely on discriminating items, leading to unbalanced utilization of the item pool. To address this challenge, the present study introduced the MaxiMin Information (MMI) criterion, which is grounded in decision theory. MMI calculates each item's minimum information (Imin) within the current confidence interval (CI) of the trait level, selecting the item with the maximum Imin to be administered. For examinees with broader CIs (less precise trait estimates), MMI leans toward administering less discriminating items, which tend to yield larger Imin. Conversely, for narrower CIs, MMI aligns more closely with MFI by favoring items with higher discrimination. This indicates that MMI's item selection is tailored to each examinee based on his or her provisional trait estimate and its estimation precision. Five simulation studies were conducted to assess MMI's performance in CAT under various conditions. Results demonstrate that although MMI is comparable with other ISRs in terms of trait estimation precision, it excels in balancing item pool utilization. By fine-tuning confidence levels, MMI not only efficiently schedules the use of discriminating items toward the test's later stages to enhance test efficiency but also effectively adapts to different testing scenarios. From these findings, we generally recommend applying MMI with a confidence level of 95% to optimize item pool utilization without compromising trait estimation accuracy. With its evident advantages, MMI holds promise for practical applications, especially for high-stakes tests requiring utmost test efficiency and security.

Two common aspects of symptom measurement are 1) the occurrence or presence of symptoms, and 2) the intensity or severity of symptoms when they occur. We adopt a latent trait perspective based on item response theory (IRT), using both unidimensional and multidimensional IRT models. We demonstrate how to (a) prepare data for analysis, (b) specify, estimate, and compare models, (c) interpret model parameters, (d) compare scores from models, and (e) visualize analysis results. We develop the relevant sequential IRT model, noting its flexibility, congruence with the theorized data generating process for symptom measures, and its promise for facilitating additional research and practical applications. The sequential model is less frequently used than other IRT models for polytomous data such as the generalized partial credit or graded response models. However, estimation of the sequential model can be readily accomplished with standard latent variable modeling and IRT software for binary indicators that allows constraints on the discrimination parameters. We compare the sequential model to other modeling options. We provide discussion of future research directions.

With the advent of computer-based assessment (CBA), process data have assumed an increasingly pivotal role in estimating examinees' latent abilities by capturing detailed records of their response processes. This study introduces the Multidimensional sequential response model (MSRM), a novel model for assessing multiple abilities through process data in computer-based cognitive and psychological assessments. A Bayesian estimation method for the MSRM is proposed and examined through a Monte Carlo simulation study across varying conditions. The results suggest that the MSRM's parameter estimation demonstrates adequate accuracy and computational efficiency, with estimation quality improving as sample sizes and sequence lengths increase. We demonstrate the practical utility of MSRM through two empirical studies, showing that it can be effectively applied in various contexts. This methodology provides valuable insights for tailored instruction by offering detailed assessments of ability mastery across multiple dimensions, thereby supporting more targeted educational interventions.

The experience sampling method (ESM) is increasingly used by researchers from various disciplines to answer novel questions about individuals' daily lives. Measurement best practices have long been overlooked in ESM research, and recent reviews show that item quality is often not reported in ESM studies. The absence of information about item quality may be partly explained by the lack of consensus on how ESM item quality should be evaluated. As part of the ESM Item Repository project (esmitemrepository.com)-an international open science initiative that collects ESM items in an open item bank and evaluates their quality-we brought together 42 international ESM experts to develop an ESM item quality assessment tool. In four Delphi phases, experts suggested 57 item quality criteria, rated the criteria, provided arguments for and against the criteria, and rated the criteria again, considering reflections from other experts. The result of the Delphi process is ESM-Q: a quality assessment tool consisting of 10 core criteria, as well as an additional 15 supplementary criteria, to be used depending on the type of items being rated and the availability of supplementary information. The criteria cover topics ranging from construct validity to the optimal wording of items. ESM-Q can aid ESM researchers in selecting existing ESM items, developing new high-quality ESM items, and evaluating the quality of ESM items in systematic reviews. Expert reflections also highlight open research questions surrounding ESM item design that form a research agenda for ESM measurement.

Endogeneity is a critical concern in research methodologies, yet it has been insufficiently addressed in longitudinal cross-lagged models, leading to potentially biased outcomes. This study scrutinized the endogeneity inherent in the cross-lagged panel model (CLPM), a prevalent and representative framework in longitudinal studies. We evaluated the efficacy of the instrumental variables (IV) methods, specifically focusing on both the auxiliary IVs (AIVs) and the model-implied IVs (MIIVs), in mitigating endogeneity issues. Simulation results indicated that endogeneity induced bias in CLPM, notably overestimating cross-lagged effects and thereby amplifying the apparent causal relationships. AIV-CLPM showed a smaller, yet still unacceptably high bias, along with low robustness and elevated type I error rates. In contrast, the MIIV-CLPM produced more accurate estimates with fewer type I errors, and, given sufficient observations, it achieved moderate statistical power. An extended simulation incorporating the random-intercept CLPM supported these findings, highlighting the generalizability of this approach. Furthermore, an empirical illustration demonstrated the practicality and feasibility of the MIIV-CLPM. Overall, MIIV is proven to be a superior modeling option within cross-lagged frameworks, effectively mitigating biases caused by endogeneity.

The other-race effect (ORE) is the disadvantage of recognizing faces of another race than one's own. While its prevalence is behaviorally well documented, the representational basis of ORE remains unclear. This study employs StyleGAN2, a deep learning technique for generating photorealistic images to uncover face representations and to investigate ORE's representational basis. To this end, we collected pairwise visual similarity ratings with same- and other-race faces across East Asian and White participants exhibiting robust levels of ORE. Leveraging the significant overlap in representational similarity between the GAN's latent space and perceptual representations in human participants, we designed an image reconstruction approach aiming to reveal internal face representations from behavioral similarity data. This methodology yielded hyper-realistic depictions of face percepts, with reconstruction accuracy well above chance, as well as an accuracy advantage for same-race over other-race reconstructions, which mirrored ORE in both populations. Further, a comparison of reconstructions across participant race revealed a novel age bias, with other-race face reconstructions appearing younger than their same-race counterpart. Thus, our work proposes a new approach to exploiting the utility of GANs in image reconstruction and provides new avenues in the study of ORE.

Examining how well a hypothesized model fits observed data is pivotal in Bayesian structural equation modeling (SEM). Recent efforts have aimed to formulate Bayesian SEM fit indices akin to those used in frequentist SEM. However, the assessment of these fit indices in Bayesian SEM has been limited in scope. This research entailed two simulation studies to explore the impact of various factors, including prior choices, model misspecification, model size, and sample size, on the sensitivity of fit indices. The first study delved into the prior sensitivity of Bayesian SEM fit indices (BRMSEA [Bayesian root mean square error of approximation], BCFI [Bayesian comparative fit index], BTLI [Bayesian Tucker–Lewis index], and PPp [posterior predictive p-value]) under latent factor and cross-loading misspecifications. The second study explored how alterations in model complexity influenced the prior sensitivity of these fit indices to model misspecifications. The findings indicate a robust performance of Bayesian fit indices under relatively severe model misspecification, suggesting a preference for highly informative priors when prior knowledge is certain. As model size increased, PPp exhibited a balanced performance between true and false positive rates, whereas BRMSEA was less reliable in the presence of latent factor misspecification. Implications for applied research are discussed.

The original online version of this article was revised: The following sentences from page 1396 were updated as indicated. The model contains 25 freely estimated parameters. Was updated to: 35 freely estimated parameters. Therefore, this model has 120 − 25 = 95 df. Was updated to: 120 - 35 = 85 df. The resulting power to reject not-close fit equals 0.854. Was updated to: 0.816 The app indicates that for a power of 0.80, we would need a sample size of 183. Was updated to: 195. The figure in Example 4 has been corrected to show the changes made by the updates. (Figure presented.)

Graphical models have become an important method for studying the network structure of multivariate psychological data. Accurate recovery of the underlying network structure is paramount and requires that the models are appropriate for the data at hand. Traditionally, Gaussian graphical models for continuous data and Ising models for binary data have dominated the literature. However, psychological research often relies on ordinal data from Likert scale items, creating a model-data mismatch. This paper examines the effect of dichotomizing ordinal variables on network recovery, as opposed to analyzing the data at its original level of measurement, using a Bayesian analysis of the ordinal Markov random field model. This model is implemented in the R package bgms. Our analysis shows that dichotomization results in a loss of information, which affects the accuracy of network recovery. This is particularly true when considering the interplay between the dichotomization cutoffs used and the distribution of the ordinal categories. In addition, we demonstrate a difference in accuracy when using dichotomized data, depending on whether edges are included or excluded in the true network, which highlights the effectiveness of the ordinal model in recovering conditional independence relationships. These findings underscore the importance of using models that deal directly with ordinal data to ensure more reliable and valid inferred network structures in psychological research.

Bayesian piecewise growth models (PGMs) are useful tools to capture nonlinear trends comprised of distinct developmental phases. An important parameter in Bayesian PGMs is the knot location – the time at which transitions arise between phases. While researchers can specify knot locations when they are known a priori, a more flexible approach is to estimate knot locations based on data. The Bayesian estimation of knot locations is largely affected by prior distributions and missing data; however, little is known about the impact of these two factors in recovering knot placements. In the current article, we conducted a Monte Carlo simulation study to examine the impact of different prior specifications and the presence of missing data on the recovery of knot placements in Bayesian PGMs. Simulation results indicated that in small sample sizes, knot location estimates were dictated by prior distributions. Even with larger sample sizes, the estimates remained sensitive to informative and inaccurate prior specifications. The presence of missing data complicated the recovery linked to certain priors. While negative consequences, such as bias in parameter estimates, were caused by a larger amount of missing data, this could be alleviated by informative and accurate priors. These findings emphasize the critical role and intertwined influence of prior distributions and missing data in reaching conclusions about changepoints. We present an illustrative example using real data with missing values to demonstrate the Bayesian estimation of knot locations under realistic scenarios. Recommendations for applied researchers are discussed.