Multidimensional achievement tests are recently gaining more importance in educational and psychological measurements. For example, multidimensional diagnostic tests can help students to determine which particular domain of knowledge they need to improve for better performance. To estimate the characteristics of candidate items (calibration) for…

In this paper, we develop a mixed stochastic approximation expectation-maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four-parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of…

The more frequent collection of response time data is leading to an increased need for an understanding of how such data can be included in measurement models. Models for response time have been advanced, but relatively limited large-scale empirical investigations have been conducted. We take advantage of a large data set from the adaptive NWEA…

There has been a growing research interest in the identification and management of disengaged test taking, which poses a validity threat that is particularly prevalent with low-stakes tests. This study investigated effort-moderated (E-M) scoring, in which item responses classified as rapid guesses are identified and excluded from scoring. Using…

The use of multistage adaptive testing (MST) has gradually increased in large-scale testing programs as MST achieves a balanced compromise between linear test design and item-level adaptive testing. MST works on the premise that each examinee gives their best effort when attempting the items, and their responses truly reflect what they know or can…

For assessment scales applied to different groups (e.g., students from different states; patients in different countries), multigroup differential item functioning (MG-DIF) needs to be evaluated in order to ensure that respondents with the same trait level but from different groups have equal response probabilities on a particular item. The…

Group differences in test scores are a key metric in education policy. Response time offers novel opportunities for understanding these differences, especially in low-stakes settings. Here, we describe how observed group differences in test accuracy can be attributed to group differences in latent response speed or group differences in latent…

The low-stakes character of international large-scale educational assessments implies that a participating student might at times provide unrelated answers as if s/he was not even reading the items and choosing a response option randomly throughout. Depending on the severity of this invalid response behavior, interpretations of the assessment…

This article presents the pseudo-equivalent group approach and discusses how it can enhance the quality of linking in the presence of nonequivalent groups. The pseudo-equivalent group approach allows to achieve pseudo-equivalence using propensity score reweighting techniques. We use it to perform linking to establish scale concordance between two…

In a signal detection theory (SDT) approach to multiple choice exams, examinees are viewed as choosing, for each item, the alternative that is perceived as being the most plausible, with perceived plausibility depending in part on whether or not an item is known. The SDT model is a process model and provides measures of item difficulty, item…

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…

Large-scale standardized tests are regularly used to measure student achievement overall and for student subgroups. These uses assume tests provide comparable measures of outcomes across student subgroups, but prior research suggests score comparisons across gender groups may be complicated by the type of test items used. This paper presents…

This study proposes explanatory cognitive diagnostic model (CDM) jointly incorporating responses and response times (RTs) with the inclusion of item covariates related to both item responses and RTs. The joint modeling of item responses and RTs intends to provide more information for cognitive diagnosis while item covariates can be used to predict…

Multilevel bifactor item response theory (IRT) models are commonly used to account for features of the data that are related to the sampling and measurement processes used to gather those data. These models conventionally make assumptions about the portions of the data structure that represent these features. Unfortunately, when data violate these…

Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we…