Educational and Psychological Measurement最新文献

This note is concerned with the chance of the one-parameter logistic (1PL-) model or the Rasch model being true for a unidimensional multi-item measuring instrument. It is pointed out that if a single dimension underlies a scale consisting of dichotomous items, then the probability of either model being correct for that scale can be zero. The question is then addressed, what the consequences could be of removing items not following these models. Using a large number of simulated data sets, a pair of empirically relevant settings is presented where such item elimination can be problematic. Specifically, dropping items from a unidimensional instrument due to them not satisfying the 1PL-model, or the Rasch model, can yield potentially seriously misleading ability estimates with increased standard errors and prediction error with respect to the latent trait. Implications for educational and behavioral research are discussed.

An important task in clinical neuropsychology is to evaluate whether scores obtained on a test battery, such as the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV), can be considered "credible" or "valid" for a particular patient. Such evaluations are typically made based on responses to performance validity tests (PVTs). As a complement to PVTs, we propose that WAIS-IV profiles also be evaluated using a residual-based M-distance ( $d_{\mathrm{ri}}^2$ ) person fit statistic. Large $d_{\mathrm{ri}}^2$ values flag profiles that are inconsistent with the factor analytic model underlying the interpretation of test scores. We first established a well-fitting model with four correlated factors for 10 core WAIS-IV subtests derived from the standardization sample. Based on this model, we then performed a Monte Carlo simulation to evaluate whether a hypothesized sampling distribution for $d_{\mathrm{ri}}^2$ was accurate and whether $d_{\mathrm{ri}}^2$ was computable, under different degrees of missing subtest scores. We found that when the number of subtests administered was less than 8, $d_{\mathrm{ri}}^2$ could not be computed around 25% of the time. When computable, $d_{\mathrm{ri}}^2$ conformed to a ${\chi }^2$ distribution with degrees of freedom equal to the number of tests minus the number of factors. Demonstration of the $d_{\mathrm{ri}}^2$ index in a large sample of clinical cases was also provided. Findings highlight the potential utility of the $d_{\mathrm{ri}}^2$ index as an adjunct to PVTs, offering clinicians an additional method to evaluate WAIS-IV test profiles and improve the accuracy of neuropsychological evaluations.

High-stakes personality assessments are often compromised by faking, where test-takers distort their responses according to social desirability. Many previous models have accounted for faking by modeling an additional latent dimension that quantifies each test-taker's degree of faking. Such models assume a homogeneous response strategy among all test-takers, reflected in a measurement model in which substantive traits and faking jointly influence item responses. However, such a model will be misspecified if, for some test-takers, item responding is only a function of substantive traits or only a function of faking. To address this limitation, we propose a mixture modeling extension of the multidimensional nominal response model (M-MNRM) that can be used to account for qualitatively different response strategies and to model relationships of strategy use with external variables. In a simulation study, the M-MNRM exhibited good parameter recovery and high classification accuracy across multiple conditions. Analyses of three empirical high-stakes datasets provided evidence for the consistent presence of the specified latent classes in different personnel selection contexts, emphasizing the importance of accounting for such kind of response behavior heterogeneity in high-stakes assessment data. We end the article with a discussion of the model's utility for psychological measurement.

This study investigates methods for item difficulty modeling in large-scale assessments using both small and large language models (LLMs). We introduce novel data augmentation strategies, including augmentation on the fly and distribution balancing, that surpass benchmark performances, demonstrating their effectiveness in mitigating data imbalance and improving model performance. Our results showed that fine-tuned small language models (SLMs) such as Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa yielded lower root mean squared error than the first-place model in the BEA 2024 Shared Task competition, whereas domain-specific models like BioClinicalBERT and PubMedBERT did not provide significant improvements due to distributional gaps. Majority voting among SLMs enhanced prediction accuracy, reinforcing the benefits of ensemble learning. LLMs, such as GPT-4, exhibited strong generalization capabilities but struggled with item difficulty prediction, likely due to limited training data and the absence of explicit difficulty-related context. Chain-of-thought prompting and rationale generation approaches were explored but did not yield substantial improvements, suggesting that additional training data or more sophisticated reasoning techniques may be necessary. Embedding-based methods, particularly using NV-Embed-v2, showed promise but did not outperform our best augmentation strategies, indicating that capturing nuanced difficulty-related features remains a challenge.

This study investigates the incorporation of historical measurement information into structural equation models (SEM) with small samples to enhance the estimation of structural parameters. Given the availability of published factor analysis results with loading estimates and standard errors for popular scales, researchers may use this historical information as informative priors in Bayesian SEM (BSEM). We focus on estimating the correlation between two constructs using BSEM after generating data with significant bias in the Pearson correlation of their sum scores due to measurement error. Our findings indicate that incorporating historical information on measurement parameters as priors can improve the accuracy of correlation estimates, mainly when the true correlation is small-a common scenario in psychological research. Priors derived from meta-analytic estimates were especially effective, providing high accuracy and acceptable coverage. However, when the true correlation is large, weakly informative priors on all parameters yield the best results. These results suggest leveraging historical measurement information in BSEM can enhance structural parameter estimation.

This study explores the performance of the item response tree (IRTree) approach in modeling missing data, comparing its performance to the expectation-maximization (EM) algorithm and multiple imputation (MI) methods. Both simulation and empirical data were used to evaluate these methods across different missing data mechanisms, test lengths, sample sizes, and missing data proportions. Expected a posteriori was used for ability estimation, and bias and root mean square error (RMSE) were calculated. The findings indicate that IRTree provides more accurate ability estimates with lower RMSE than both EM and MI methods. Its overall performance was particularly strong under missing completely at random and missing not at random, especially with longer tests and lower proportions of missing data. However, IRTree was most effective with moderate levels of omitted responses and medium-ability test takers, though its accuracy decreased in cases of extreme omissions and abilities. The study highlights that IRTree is particularly well suited for low-stakes tests and has strong potential for providing deeper insights into the underlying missing data mechanisms within a data set.

Maintaining consistent item difficulty across test forms is crucial for accurately and fairly classifying examinees into pass or fail categories. This article presents a practical procedure for classifying items based on difficulty levels using functional data analysis (FDA). Methodologically, we clustered item characteristic curves (ICCs) into difficulty groups by analyzing their functional principal components (FPCs) and then employed a neural network to predict difficulty for ICCs. Given the degree of similarity between many ICCs, categorizing items by difficulty can be challenging. The strength of this method lies in its ability to provide an empirical and consistent process for item classification, as opposed to relying solely on visual inspection. The findings reveal that most discrepancies between visual classification and FDA results differed by only one adjacent difficulty level. Approximately 67% of these discrepancies involved items in the medium to hard range being categorized into higher difficulty levels by FDA, while the remaining third involved very easy to easy items being classified into lower levels. The neural network, trained on these data, achieved an accuracy of 79.6%, with misclassifications also differing by only one adjacent difficulty level compared to FDA clustering. The method demonstrates an efficient and practical procedure for classifying test items, especially beneficial in testing programs where smaller volumes of examinees tested at various times throughout the year.

In computerized adaptive testing (CAT), examinees see items targeted to their ability level. Postoperational data have a high degree of missing information relative to designs where everyone answers all questions. Item responses are observed over a restricted range of abilities, reducing item-total score correlations. However, if the adaptive item selection depends only on observed responses, the data are missing at random (MAR). We simulated data from three different testing designs (common items, randomly selected items, and CAT) and found that it was possible to re-estimate both person and item parameters from postoperational CAT data. In a multidimensional CAT, we show that it is necessary to include all responses from the testing phase to avoid violating missing data assumptions. We also observed that some CAT designs produced "reversals" where item discriminations became negative causing dramatic under and over-estimation of abilities. Our results apply to situations where researchers work with data drawn from adaptive testing or from instructional tools with adaptive delivery. To avoid bias, researchers must make sure they use all the data necessary to meet the MAR assumptions.

This study investigates the treatment of rapid-guess (RG) responses as missing data within the context of the effort-moderated model. Through a series of illustrations, this study demonstrates that the effort-moderated model assumes missing at random (MAR) rather than missing completely at random (MCAR), explaining the conditions necessary for MAR. These examples show that RG responses, when treated as missing under the effort-moderated model, do not introduce bias into ability estimates if the missingness mechanism is properly accounted for. Conversely, using a standard item response theory (IRT) model (scoring RG responses as if they were valid) instead of the effort-moderated model leads to considerable biases, underestimating group means and overestimating standard deviations when the item parameters are known, or overestimating item difficulty if the item parameters are estimated.

Factor analysis is commonly used in behavioral sciences to measure latent constructs, and researchers routinely consider approximate fit indices to ensure adequate model fit and to provide important validity evidence. Due to a lack of generalizable fit index cutoffs, methodologists suggest simulation-based methods to create customized cutoffs that allow researchers to assess model fit more accurately. However, simulation-based methods are computationally intensive. An open question is: How many simulation replications are needed for these custom cutoffs to stabilize? This Monte Carlo simulation study focuses on one such simulation-based method-dynamic fit index (DFI) cutoffs-to determine the optimal number of replications for obtaining stable cutoffs. Results indicated that the DFI approach generates stable cutoffs with 500 replications (the currently recommended number), but the process can be more efficient with fewer replications, especially in simulations with categorical data. Using fewer replications significantly reduces the computational time for determining cutoff values with minimal impact on the results. For one-factor or three-factor models, results suggested that in most conditions 200 DFI replications were optimal for balancing fit index cutoff stability and computational efficiency.