Journal of Educational Measurement最新文献

Online calibration is a key technology for item calibration in computerized adaptive testing (CAT) and has been widely used in various forms of CAT, including unidimensional CAT, multidimensional CAT (MCAT), CAT with polytomously scored items, and cognitive diagnostic CAT. However, as multidimensional and polytomous assessment data become more common, only a few published reports focus on online calibration in MCAT with polytomously scored items (P-MCAT). Therefore, standing on the shoulders of the existing online calibration methods/designs, this study proposes four new P-MCAT online calibration methods and two new P-MCAT online calibration designs and conducts two simulation studies to evaluate their performance under varying conditions (i.e., different calibration sample sizes and correlations between dimensions). Results show that all of the newly proposed methods can accurately recover item parameters, and the adaptive designs outperform the random design in most cases. In the end, this paper provides practical guidance based on simulation results.

Technical difficulties and other unforeseen events occasionally lead to incomplete data on educational tests, which necessitates the reporting of imputed scores to some examinees. While there exist several approaches for reporting imputed scores, there is a lack of any guidance on the reporting of the uncertainty of imputed scores. In this paper, several approaches are suggested for quantifying the uncertainty of imputed scores using measures that are similar in spirit to estimates of reliability and standard error of measurement. A simulation study is performed to examine the properties of the approaches. The approaches are then applied to data from a state test on which some examinees' scores had to be imputed following computer problems. Several recommendations are made for practice.

Back random responding (BRR) behavior is one of the commonly observed careless response behaviors. Accurately detecting BRR behavior can improve test validities. Yu and Cheng (2019) showed that the change point analysis (CPA) procedure based on weighted residual (CPA-WR) performed well in detecting BRR. Compared with the CPA procedure, the exponentially weighted moving average (EWMA) obtains more detailed information. This study equipped the weighted residual statistic with EWMA, and proposed the EWMA-WR method to detect BRR. To make the critical values adaptive to the ability levels, this study proposed the Monte Carlo simulation with ability stratification (MC-stratification) method for calculating critical values. Compared to the original Monte Carlo simulation (MC) method, the newly proposed MC-stratification method generated a larger number of satisfactory results. The performances of CPA-WR and EWMA-WR were evaluated under different conditions that varied in the test lengths, abnormal proportions, critical values and smoothing constants used in the EWMA-WR method. The results showed that EWMA-WR was more powerful than CPA-WR in detecting BRR. Moreover, an empirical study was conducted to illustrate the utility of EWMA-WR for detecting BRR.

Multiple group modeling is one of the methods to address the measurement noninvariance issue. Traditional studies on multiple group modeling have mainly focused on item responses. In computer-based assessments, joint modeling of response times and action counts with item responses helps estimate the latent speed and action levels in addition to latent ability. These two new data sources can also be used to further address the measurement noninvariance issue. One challenge, however, is to correctly model action counts which can be underdispersed, overdispersed, or equidispersed in real data sets. To address this, we adopted the Conway-Maxwell-Poisson distribution that accounts for different types of dispersion in action counts and incorporated it in the multiple group joint modeling of item responses, response times, and action counts. Bayesian Markov Chain Monte Carlo method was used for model parameter estimation. To illustrate an application of the proposed model, an empirical data analysis was conducted using the Programme for International Student Assessment (PISA) 2015 collaborative problem-solving items where potential measurement noninvariance issue existed between gender groups. Results indicated that Conway-Maxwell-Poisson model yielded better model fit than alternative count data models such as negative binomial and Poisson models. In addition, response times and action counts provided further information on performance differences between groups.

Several marginal effect size (ES) statistics suitable for quantifying the magnitude of differential item functioning (DIF) have been proposed in the area of item response theory; for instance, the Differential Functioning of Items and Tests (DFIT) statistics, signed and unsigned item difference in the sample statistics (SIDS, UIDS, NSIDS, and NUIDS), the standardized indices of impact, and the differential response functioning (DRF) statistics. However, the relationship between these proposed statistics has not been fully discussed, particularly with respect to population parameter definitions and recovery performance across independent samples. To address these issues, this article provides a unified presentation of competing DIF ES definitions and estimators, and evaluates the recovery efficacy of these competing estimators using a set of Monte Carlo simulation experiments. Statistical and inferential properties of the estimators are discussed, as well as future areas of research in this model-based area of bias quantification.

A test of item compromise is presented which combines the test takers' responses and response times (RTs) into a statistic defined as the number of correct responses on the item for test takers with RTs flagged as suspicious. The test has null and alternative distributions belonging to the well-known family of compound binomial distributions, is simple to calculate, and has results that are easy to interpret. It also demonstrated nearly perfect power for the detection of compromise with no more than 10 test takers with preknowledge of the more difficult and discriminating items in a set of empirical examples. For the easier and less discriminating items, the presence of some 20 test takers with preknowledge still sufficed. A test based on the reverse statistic of the total time by test takers with responses flagged as suspicious may seem a natural alternative but misses the property of a monotone likelihood ratio necessary to decide between a test that should be left or right sided.

This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe+ prior was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement.

The certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self-confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.