Journal of Educational Measurement最新文献

In this study, we introduced a cross-classified multidimensional nominal response model (CC-MNRM) to account for various response styles (RS) in the presence of cross-classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to address the computational challenge of estimating the proposed model. To demonstrate our new approach, we analyzed empirical student evaluation of teaching (SET) data collected from a large public university with three models: a CC-MNRM with RS, a CC-MNRM with no RS, and a multilevel MNRM with RS. Results indicated that the three models led to different inferences regarding the observed covariates. Additionally, in the example, ignoring/incorporating RS led to changes in student substantive scores, while the instructor substantive scores were less impacted. Misspecifying the cross-classified data structure resulted in apparent changes on instructor scores. To further evaluate the proposed modeling approach, we conducted a preliminary simulation study and observed good parameter and score recovery. We concluded this study with discussions of limitations and future research directions.

The Lognormal Response Time (LNRT) model measures the speed of test-takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test-taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we demonstrate the connection between the existing LNRT model parameters and the “level” component of profile similarity, and we define two new parameters for the LNRT model representing profile “dispersion” and “shape.” We show that while the LNRT model measures level (speed), profile dispersion and shape are conflated in model residuals, and that distinguishing them provides meaningful and useful parameters for identifying anomalous testing behavior. Results from data in a situation where many test-takers gained preknowledge of test items revealed that profile shape, not currently measured in the LNRT model, was the most sensitive response time index to the abnormal test-taking behavior patterns. Results strongly support expanding the LNRT model to measure not only each test-taker's level of speed, but also the dispersion and shape of their response time profiles.

The purpose of this study is to develop a nonparametric DIF method that (a) compares focal groups directly to the composite group that will be used to develop the reported test score scale, and (b) allows practitioners to explore for DIF related to focal groups stemming from multicategorical variables that constitute a small proportion of the overall testing population. We propose the nonparametric root expected proportion squared difference (REPSD) index that evaluates the statistical significance of composite group DIF for relatively small focal groups stemming from multicategorical focal variables, with decisions of statistical significance based on quasi-exact p values obtained from Monte Carlo permutations of the DIF statistic under the null distribution. We conduct a simulation to evaluate conditions under which the index produces acceptable Type I error and power rates, as well as an application to a school district assessment. Practitioners can calculate the REPSD index in a freely available package we created in the R environment.

The efficiency of cognitive component skills is typically assessed with speeded performance tests. Interpreting only effective ability or effective speed as efficiency may be challenging because of the within-person dependency between both variables (speed-ability tradeoff, SAT). The present study measures efficiency as effective ability conditional on speed by controlling speed experimentally. Item-level time limits control the stimulus presentation time and the time window for responding (timed condition). The overall goal was to examine the construct validity of effective ability scores obtained from untimed and timed condition by comparing the effects of theory-based item properties on item difficulty. If such effects exist, the scores reflect how well the test-takers were able to cope with the theory-based requirements. A German subsample from PISA 2012 completed two reading component skills tasks (i.e., word recognition and semantic integration) with and without item-level time limits. Overall, the included linguistic item properties showed stronger effects on item difficulty in the timed than the untimed condition. In the semantic integration task, item properties explained the time required in the untimed condition. The results suggest that effective ability scores in the timed condition better reflect how well test-takers were able to cope with the theoretically relevant task demands.

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or nonproficiency of specified latent characteristics. These models are well suited for providing diagnostic and actionable feedback to support intermediate and formative assessment efforts. Several DCMs have been developed and applied in different settings. This study examines a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate and prove that the proposed DCM has measurement properties akin to the Rasch and one-parameter logistic item response theory models, including sum score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We introduce some potential applications for this model, and discuss the implications and limitations of these developments, as well as directions for future research.

Understanding the intraindividual relation between an individual's speed and ability in testing scenarios is essential to assure a fair assessment. Different approaches exist for estimating this relationship, that either rely on specific study designs or on specific assumptions. This paper aims to add to the toolbox of approaches for estimating this relationship. We propose the intraindividual speed-ability-relation (ISAR) model, which relies on nonstationarity of speed and ability over the course of the test. The ISAR model explicitly models intraindividual change in ability and speed within a test and assesses the intraindividual relation of speed and ability by evaluating the relationship of both latent change variables. Model estimation is good, when there are interindividual differences in speed and ability changes in the data. In empirical data from PISA, we found that the intraindividual relationship between speed and ability is not universally negative for all individuals and varies across different competence domains and countries. We discuss possible explanations for this relationship.

Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore, previous research has shown a complex relationship between response time and accuracy, necessitating a functional analysis to understand the patterns that manifest from this relationship. In this study, response time data are incorporated into an item response model for two purposes: (a) to examine how individuals' speed within an experimental condition affects their response accuracy on an item, and (b) to detect the differences in individuals' speed between conditions in the presence of within-condition effects. For these two purposes, by-variable smooth functions are employed to model differential and functional response time effects by experimental condition for each item. This model is illustrated using an empirical data set to describe the effect of individuals' speed on their reading comprehension ability in two experimental conditions of reading medium (paper vs. digital) by item. A simulation study showed that the recovery of parameters and by-variable smooth functions of response time was satisfactory, and that the type I error rate and power of the test for the by-variable smooth function of response time were acceptable in conditions similar to the empirical data set. In addition, the proposed method correctly identified the range of response time where between-condition differences in the effect of response time on the probability of a correct response were accurate.

In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical DCM (sequential HDCM), which combines a sequential DCM with the HDCM, and investigate classification accuracy of the model in the presence of hierarchies when multiple attempts are allowed in dynamic assessment. We investigated the model's impact on classification accuracy when hierarchical structures are correctly specified, misspecified, or overspecified. The results indicate that (1) a sequential HDCM accurately classified students as masters and nonmasters when the data had a hierarchical structure; (2) a sequential HDCM produced similar or slightly higher classification accuracy than nonhierarchical sequential LCDM when the data had hierarchical structures; and (3) the misspecification of the hierarchical structure of the data resulted in lower classification accuracy when the misspecified model had fewer attribute profiles than the true model. We discuss limitations and make recommendations on using the proposed model in practice. This study provides practitioners with information about the possibilities for psychometric modeling of dynamic classroom assessment data.

Research has shown that multiple-indicator multiple-cause (MIMIC) models can result in inflated Type I error rates in detecting differential item functioning (DIF) when the assumption of equal latent variance is violated. This study explains how the violation of the equal variance assumption adversely impacts the detection of nonuniform DIF and how it can be addressed through moderated nonlinear factor analysis (MNLFA) model via Bayesian estimation approach to overcome limitations from the restrictive assumption. The Bayesian MNLFA approach suggested in this study better control Type I errors by freely estimating latent factor variances across different groups. Our experimentation with simulated data demonstrates that the BMNFA models outperform the existing MIMIC models, in terms of Type I error control as well as parameter recovery. The results suggest that the MNLFA models have the potential to be a superior choice to the existing MIMIC models, especially in situations where the assumption of equal latent variance assumption is not likely to hold.