Educational and Psychological Measurement最新文献

In item response theory modeling, item fit analysis using posterior expectations, otherwise known as pseudocounts, has many advantages. They are readily obtained from the E-step output of the Bock-Aitkin Expectation-Maximization (EM) algorithm and continue to function as a basis of evaluating model fit, even when missing data are present. This paper aimed to improve the interpretability of the root mean squared deviation (RMSD) index based on posterior expectations. In Study 1, we assessed its performance using two approaches. First, we employed the poor person's posterior predictive model checking (PP-PPMC) to compute their significance levels. The resulting Type I error was generally controlled below the nominal level, but power noticeably declined with smaller sample sizes and shorter test lengths. Second, we used receiver operating characteristic (ROC) curve analysis (±) to empirically determine the reference values (cutoff thresholds) that achieve an optimal balance between false-positive and true-positive rates. Importantly, we identified optimal reference values for each combination of sample size and test length in the simulation conditions. The cutoff threshold approach outperformed the PP-PPMC approach with greater gains in true-positive rates than losses from the inflated false-positive rates. In Study 2, we extended the cutoff threshold approach to conditions with larger sample sizes and longer test lengths. Moreover, we evaluated the performance of the optimized cutoff thresholds under varying levels of data missingness. Finally, we employed response surface analysis (±) to develop a prediction model that generalizes the way the reference values vary with sample size and test length. Overall, this study demonstrates the application of the PP-PPMC for item fit diagnostics and implements a practical frequentist approach to empirically derive reference values. Using our prediction model, practitioners can compute the reference values of RMSD that are tailored to their dataset's sample size and test length.

Ensuring fairness in educational and psychological assessments is critical, particularly in detecting differential item functioning (DIF), where items perform differently across subgroups. The Rasch tree method, a model-based recursive partitioning approach, is an innovative and flexible DIF detection tool that does not require the pre-specification of focal and reference groups. However, research systematically examining its performance under realistic measurement conditions, such as when multiple DIF items do not consistently favor one subgroup, is limited. This study builds on prior research, evaluating the Rasch tree method's ability to detect DIF by investigating the impact of DIF balance, along with other key factors such as DIF magnitude, sample size, test length, and contamination levels. Additionally, we incorporate the Educational Testing Service effect size heuristic as a criterion to compare the DIF detection rate performance with only statistical significance. Results indicate that the Rasch tree has better true DIF detection rates under balanced DIF conditions and large DIF magnitudes. However, its accuracy declines when DIF is unbalanced and the percentage of DIF contamination increases. The use of an effect size reduces the detection of negligible DIF. Caution is recommended with smaller samples, where detection rates are the lowest, especially for larger DIF magnitudes and increased DIF contamination percentages in unbalanced conditions. The study highlights the strengths and limitations of the Rasch tree method under a variety of conditions, underscores the importance of the impact of DIF group imbalance, and provides recommendations for optimizing DIF detection in practical assessment scenarios.

Sequential procedures have been shown to be effective methods for real-time detection of compromised items in computerized adaptive testing. In this study, we propose three item response theory-based sequential procedures that involve the use of item scores and response times (RTs). The first procedure requires that either the score-based statistic or the RT-based statistic be extreme, the second procedure requires that both the score-based statistic and the RT-based statistic be extreme, and the third procedure requires that a combined score and RT-based statistic be extreme. Results suggest that the third procedure is the most promising, providing a reasonable balance between the false-positive rate and the true-positive rate while also producing relatively short lag times across a wide range of simulation conditions.

Forced-choice (FC) questionnaires have gained increasing attention as a strategy to reduce social desirability in self-reports, supported by advancements in confirmatory models that address the ipsativity of FC test scores. However, these models assume a known dimensionality and structure, which can be overly restrictive or fail to fit the data adequately. Consequently, exploratory models can be required, with accurate dimensionality assessment as a critical first step. FC questionnaires also pose unique challenges for dimensionality assessment, due to their inherently complex multidimensional structures. Despite this, no prior studies have systematically evaluated dimensionality assessment methods for FC data. To fill this gap, the present study examines five commonly used methods: the Kaiser Criterion, Empirical Kaiser Criterion, Parallel Analysis (PA), Hull Method, and Exploratory Graph Analysis. A Monte Carlo simulation study was conducted, manipulating key design features of FC questionnaires, such as the number of dimensions, items per dimension, response formats (e.g., binary vs. graded), and block composition (e.g., inclusion of heteropolar and unidimensional blocks), as well as factor loadings, inter-factor correlations, and sample size. Results showed that the Maximal Kaiser Criterion and PA methods outperformed the others, achieving higher accuracy and lower bias. Performance improved particularly when heteropolar or unidimensional blocks were included or when the questionnaire length increased. These findings emphasize the importance of thoughtful FC test design and provide practical recommendations for improving dimensionality assessment in this format.

Measurement appropriateness concerns the question of whether the test or survey scale under consideration can provide a valid measure for a specific individual. An aberrant item response pattern would provide internal counterevidence against using the test/scale for this person, whereas a more typical item response pattern would imply a fit of the measure to the person. Traditional approaches, including the popular Lz person fit statistic, are hampered by their two-stage estimation procedure and the fact that the fit for the person is determined based on the model calibrated on data that include the misfitting persons. This calibration bias creates suboptimal conditions for person fit assessment. Solutions have been sought through the derivation of approximating bias-correction formulas and/or iterative purification procedures. Yet, here we discuss an alternative one-stage solution that involves calibrating a model expansion of the measurement model that includes a mixture component for target aberrant response patterns. A simulation study evaluates the approach under the most unfavorable and least-studied conditions for person fit indices, short polytomous survey scales, similar to those found in large-scale educational assessments such as the Program for International Student Assessment or Trends in Mathematics and Science Study.

A procedure for evaluation of the proportion explained component variance by the underlying trait in behavioral scales with second-order structure is outlined. The resulting index of accounted for variance over all scale components is a useful and informative complement to the conventional omega-hierarchical coefficient as well as the proportion of explained component correlation. A point and interval estimation method is described for the discussed index, which utilizes a confirmatory factor analysis approach within the latent variable modeling methodology. The procedure can be used with widely available software and is illustrated on data.

Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score predictor (HRFS) with larger determinacy than the conventional regression factor score predictor (RFS) when individuals have different factor loadings. First, a method for the estimation of individual loadings is proposed. The individual loading estimates are used to compute the HRFS. Then, a binomial test for loading heterogeneity of a factor is proposed to compute the HRFS only when the test is significant. Otherwise, the conventional RFS should be used. A simulation study reveals that the HRFS has larger determinacy than the conventional RFS in populations with substantial loading heterogeneity. An empirical example based on subsamples drawn randomly from a large sample of Big Five Markers indicates that the determinacy can be improved for the factor emotional stability when the HRFS is computed.

In longitudinal mixture models like latent transition analysis (LTA), identical items are often repeatedly measured across multiple time points to define latent classes and individuals' similar response patterns across multiple time points, which attributes to residual correlations. Therefore, this study hypothesized that an LTA model assuming residual correlations among indicator variables measured repeatedly across multiple time points would provide more accurate estimates of transition probabilities than a traditional LTA model. To test this hypothesis, a Monte Carlo simulation was conducted to generate data both with and without specified residual correlations among the repeatedly measured indicator variables, and the two LTA models-one that accounted for residual correlations and one that did not-were compared. This study included transition probabilities, numbers of indicator variables, sample sizes, and levels of residual correlations as the simulation conditions. The estimation performances were compared based on parameter estimate bias, mean squared error, and coverage. The results demonstrate that LTA with residual correlations outperforms traditional LTA in estimating transition probabilities, and the differences between the two models become prominent when the residual correlation is .3 or higher. This research integrates the characteristics of longitudinal data in an LTA simulation study and suggests an improved version of LTA estimation.

Proposed is a new method of scoring multidimensional forced-choice (MFC) questionnaires referred to as the dominant trait profile (DTP) method. The DTP method identifies a dominant response vector (DRV) for each trait-a vector of binary scores for preferences in item pairs within MFC blocks from the perspective of a respondent for whom the trait under consideration dominates over the other traits being measured. The respondents' observed response vectors are matched to the DRV for each trait to produce (1/0) matching scores that are then analyzed via latent trait modeling, with scaling options (a) bounded D-scale (from 0 to 1), or (b) item response theory logit scale. The DTP method allows for the comparison of individuals on a trait of interest, as well as their standing in relation to a dominant trait "standard" (criterion). The study results indicate that DTP-based trait estimates are highly correlated with those produced by the popular Thurstonian item response theory model and the Zinnes and Griggs pairwise preference item response theory model, while avoiding the complexity of their designs and some computations issues.

In this article, we explore the application of Large Language Models (LLMs) in assessing the content validity of psychometric instruments, focusing on the Big Five Questionnaire (BFQ) and Big Five Inventory (BFI). Content validity, a cornerstone of test construction, ensures that psychological measures adequately cover their intended constructs. Using both human expert evaluations and advanced LLMs, we compared the accuracy of semantic item-construct alignment. Graduate psychology students employed the Content Validity Ratio to rate test items, forming the human baseline. In parallel, state-of-the-art LLMs, including multilingual and fine-tuned models, analyzed item embeddings to predict construct mappings. The results reveal distinct strengths and limitations of human and AI approaches. Human validators excelled in aligning the behaviorally rich BFQ items, while LLMs performed better with the linguistically concise BFI items. Training strategies significantly influenced LLM performance, with models tailored for lexical relationships outperforming general-purpose LLMs. Here we highlight the complementary potential of hybrid validation systems that integrate human expertise and AI precision. The findings underscore the transformative role of LLMs in psychological assessment, paving the way for scalable, objective, and robust test development methodologies.