Journal of Educational Measurement最新文献

Item difficulty and dimensionality often correlate, implying that unidimensional IRT approximations to multidimensional data (i.e., reference composites) can take a curvilinear form in the multidimensional space. Although this issue has been previously discussed in the context of vertical scaling applications, we illustrate how such a phenomenon can also easily occur within individual tests. Measures of reading proficiency, for example, often use different task types within a single assessment, a feature that may not only lead to multidimensionality, but also an association between item difficulty and dimensionality. Using a latent regression strategy, we demonstrate through simulations and empirical analysis how associations between dimensionality and difficulty yield a nonlinear reference composite where the weights of the underlying dimensions change across the scale continuum according to the difficulties of the items associated with the dimensions. We further show how this form of curvilinearity produces systematic forms of misspecification in traditional unidimensional IRT models (e.g., 2PL) and can be better accommodated by models such as monotone-polynomial or asymmetric IRT models. Simulations and a real-data example from the Early Childhood Longitudinal Study—Kindergarten are provided for demonstration. Some implications for measurement modeling and for understanding the effects of 2PL misspecification on measurement metrics are discussed.

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.