Multidimensional Computerized Adaptive Testing Research Articles

Using Response Time in Multidimensional Computerized Adaptive Testing

AbstractIn multidimensional computerized adaptive testing (MCAT), item selection strategies are generally constructed based on responses, and they do not consider the response times required by items. This study constructed two new criteria (referred to as DT‐inc and DT) for MCAT item selection by utilizing information from response times. The new designs maximize the amount of information per unit time. Furthermore, these two new designs were extended to the DTS‐inc and DTS designs to efficiently estimate intentional abilities. Moreover, the EAP method for ability estimation was also equipped with response time. The performances of the response‐time‐based EAP (RT‐based EAP) and the new designs were evaluated in simulation and empirical studies. The results showed that the RT‐based EAP significantly improved the ability estimation precision compared with the EAP without using response time, and the new designs dramatically saved testing times for examinees with a small sacrifice of ability estimation precision and item pool usage.

Applying multidimensional computerized adaptive testing to the MSQOL-54: a simulation study

BackgroundThe Multiple Sclerosis Quality of Life-54 (MSQOL-54) is one of the most commonly-used MS-specific health-related quality of life (HRQOL) measures. It is a multidimensional, MS-specific HRQOL inventory, which includes the generic SF-36 core items, supplemented with 18 MS-targeted items. Availability of an adaptive short version providing immediate item scoring may improve instrument usability and validity. However, multidimensional computerized adaptive testing (MCAT) has not been previously applied to MSQOL-54 items. We thus aimed to apply MCAT to the MSQOL-54 and assess its performance.MethodsResponses from a large international sample of 3669 MS patients were assessed. We calibrated 52 (of the 54) items using bifactor graded response model (10 group factors and one general HRQOL factor). Then, eight simulations were run with different termination criteria: standard errors (SE) for the general factor and group factors set to different values, and change in factor estimates from one item to the next set at < 0.01 for both the general and the group factors. Performance of the MCAT was assessed by the number of administered items, root mean square difference (RMSD), and correlation.ResultsEight items were removed due to local dependency. The simulation with SE set to 0.32 (general factor), and no SE thresholds (group factors) provided satisfactory performance: the median number of administered items was 24, RMSD was 0.32, and correlation was 0.94.ConclusionsCompared to the full-length MSQOL-54, the simulated MCAT required fewer items without losing precision for the general HRQOL factor. Further work is needed to add/integrate/revise MSQOL-54 items in order to make the calibration and MCAT performance efficient also on group factors, so that the MCAT version may be used in clinical practice and research.

Online Calibration in Multidimensional Computerized Adaptive Testing with Polytomously Scored Items

AbstractOnline 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.

LASSO-Based Pattern Recognition for Replenished Items With Graded Responses in Multidimensional Computerized Adaptive Testing.

As a branch of statistical latent variable modeling, multidimensional item response theory (MIRT) plays an important role in psychometrics. Multidimensional graded response model (MGRM) is a key model for the development of multidimensional computerized adaptive testing (MCAT) with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and selection operator (LASSO), which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility.

The Optimal Design of Bifactor Multidimensional Computerized Adaptive Testing with Mixed-format Items.

Multidimensional computerized adaptive testing (MCAT) using mixed-format items holds great potential for the next-generation assessments. Two critical factors in the mixed-format test design (i.e., the order and proportion of polytomous items) and item selection were addressed in the context of mixed-format bifactor MCAT. For item selection, this article presents the derivation of the Fisher information matrix of the bifactor graded response model and the application of the bifactor dimension reduction method to simplify the computation of the mutual information (MI) item selection method. In a simulation study, different MCAT designs were compared with varying proportions of polytomous items (0.2-0.6, 1), different item-delivering formats (DPmix: delivering polytomous items at the final stage; RPmix: random delivering), three bifactor patterns (low, middle, and high), and two item selection methods (Bayesian D-optimality and MI). Simulation results suggested that a) the overall estimation precision increased with a higher bifactor pattern; b) the two item selection methods did not show substantial differences in estimation precision; and c) the RPmix format always led to more precise interim and final estimates than the DPmix format. The proportions of 0.3 and 0.4 were recommended for the RPmix and DPmix formats, respectively.

Multidimensional Computerized Adaptive Testing Simulations in R

Computerized Adaptive Testing (CAT) is a beneficial test technique that decreases the number of items that need to be administered by taking items in accordance with individuals' own ability levels. After the CAT applications were constructed based on the unidimensional Item Response Theory (IRT), Multidimensional CAT (MCAT) applications have gained momentum with the improvement of multidimensional IRT (MIRT) models in recent years. Researchers often benefit from simulation studies in order to design the final adaptive testing application and to test the effectiveness of adaptive testing applications they developed with different methods. Recently, R has become one of the most widely used programming languages in Monte Carlo Simulation studies since it is a free and open-source software. The aims of this study are to present the MCAT simulation process step by step in the R environment, to examine the effects of the conditions that researchers can handle during the simulation process according to two different dimensional models, and to examine the effect of treating multidimensional structures as unidimensional structures on simulation results. In this direction, datasets generated in accordance with within-item dimensionality and between-item dimensionality models, MCAT simulation studies were constructed with different customizations, and MCAT simulation results were compared with unidimensional CAT simulation results. All commands required for each simulation example were explained and results were shared for each condition.

A comparative analysis of multidimensional computerized adaptive testing for the DASH and QuickDASH scores in Dupuytren's disease.

The QuickDASH is a short-form version of the DASH questionnaire, the most widely used patient-reported outcome measure in hand surgery. Multidimensional computerized adaptive testing (MCAT) can produce shorter and more precise testing than static short forms, like QuickDASH. We used DASH responses from 507 patients with Dupuytren's disease to develop a MCAT. The algorithm was evaluated in a Monte Carlo simulation, where the standard error of measurement (SEm) of scores obtained from the 11-item QuickDASH was compared with scores obtained from an MCAT that could administer up to 11 items from the full 30-item DASH. The MCAT asked a mean of 8.51 items (SD 2.93) and 265/1000 simulated respondents needed to complete ≤five items. Median SEms were better for DASH MCAT: 0.299 (hand function) and 0.256 (sensory symptoms) versus 0.320 and 0.290, respectively, for QuickDASH. Our study showed that the DASH MCAT can produce more precise DASH measurement than the QuickDASH, from fewer items.

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing: A development and usability study.

Background:The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT.Methods:In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions.Results:We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97–0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents’ burdens using Rasch MCAT.Conclusions:The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients’ perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.

Improving test efficiency for a grid multidimensional computerized classification test by the application of a conditional latent-trait distribution to a sequential probability ratio test.

The measurement efficiency of a multidimensional computerized adaptive testing (MCAT) can be improved by taking the correlations between the dimensions into account during the item selection and latent-trait estimation procedures (Segall, 1996; Wang &amp; Chen, 2004). Although a multidimensional computerized classification test (MCCT), which was based on a multidimensional itemresponse model, was previously found more efficient than its unidimensional counterpart, the difference was negligible (Seitz &amp; Frey, 2013); the researchers had adopted a sequential probability ratio test (SPRT) as the termination criterion in this MCCT study. To make a classification decision on each dimension, which is called a grid classification (Wang et al., 2019), only items that loaded on that dimension were used to calculate the likelihood ratio, which squandered the available information of the correlations between the dimensions. The current study utilizes such useful information to improve the measurement efficiency of the MCCT by applying a conditional distribution of the latent-trait estimates and then including all the administered items to calculate the likelihood ratio in the SPRT. The performance of this newly proposed method was evaluated through a series of simulation studies. The results showed that the proposed method can sizably improve the measurement efficiency of an MCCT by saving 1% to 32% of the test length in comparison with the SPRT when the two test dimensions are at least moderately correlated. The findings and further applications of this study are discussed.

Improving the Delivery of Function-Directed Care During Acute Hospitalizations: Methods to Develop and Validate the Functional Assessment in Acute Care Multidimensional Computerized Adaptive Test (FAMCAT)

Objective:To (1) develop a patient-reported, multidomain functional assessment tool focused on medically ill patients in acute care settings; (2) characterize the measure's psychometric performance; and (3) establish clinically actionable score strata that link to easily implemented mobility preservation plans. Design:This article describes the approach that our team pursued to develop and characterize this tool, the Functional Assessment in Acute Care Multidimensional Computer Adaptive Test (FAMCAT). Development involved a multistep process that included (1) expanding and refining existing item banks to optimize their salience for hospitalized patients; (2) administering candidate items to a calibration cohort; (3) estimating multidimensional item response theory models; (4) calibrating the item banks; (5) evaluating potential multidimensional computerized adaptive testing (MCAT) enhancements; (6) parameterizing the MCAT; (7) administering it to patients in a validation cohort; and (8) estimating its predictive and psychometric characteristics. Setting:A large (2000-bed) Midwestern Medical Center. Participants:The overall sample included 4495 adults (2341 in a calibration cohort, 2154 in a validation cohort) who were admitted either to medical services with at least 1 chronic condition or to surgical/medical services if they required readmission after a hospitalization for surgery (N=4495). Intervention:Not applicable. Main Outcome Measures:Not applicable. Results:The FAMCAT is an instrument designed to permit the efficient, precise, low-burden, multidomain functional assessment of hospitalized patients. We tried to optimize the FAMCAT's efficiency and precision, as well as its ability to perform multiple assessments during a hospital stay, by applying cutting edge methods such as the adaptive measure of change (AMC), differential item functioning computerized adaptive testing, and integration of collateral test-taking information, particularly item response times. Evaluation of these candidate methods suggested that all may enhance MCAT performance, but none were integrated into initial MCAT parameterization. Conclusions:The FAMCAT has the potential to address a longstanding need for structured, frequent, and accurate functional assessment among patients hospitalized with medical diagnoses and complications of surgery.

Development of a Multidimensional Computerized Adaptive Test based on the Bifactor Model

The purpose of this study was to conduct a real-time multidimensional computerized adaptive test (MCAT) using data from a previous paper-pencil test (PPT) regarding the grammar and vocabulary dimensions of an end-of-term proficiency exam conducted on students in a preparatory class at a university. An item pool was established through four separate 50-item sets applied in four different semesters. The fit between unidimensional, multi-unidimensional and bifactor IRT models was compared during item calibration, with the bifactor model providing the best fit for all data sets. This was followed by a hybrid simulation for 36 conditions obtained using six item selection methods, two ability estimation methods and three termination rules. The statistics and graphs obtained indicate D-rule item selection, maximum a posteriori (MAP) ability estimation and standard error termination rule as the best algorithm for the real-time MCAT application. With the minimum number of items to be administered determined as 10, the real-time application conducted on 99 examinees yielded an average number of items of 13.4. The PPT format proficiency exam consists of 50 items, leading to the conclusion that the examinees participating in the real-time MCAT are administered an average of 74.4% fewer items than the PPT. Additionally, 86 of the examinees answered between 10-13 items. The item pool use rate is 30%. Lastly, the correlation between the PPT scores and general trait scores of 32 examinees was calculated as .77.

The Block Item Pocket Method for Reviewable Multidimensional Computerized Adaptive Testing.

Most computerized adaptive testing (CAT) programs do not allow item review due to a decrease in estimation precision and aberrant manipulation strategies. In this article, a block item pocket (BIP) method that combines the item pocket method with the successive block method to realize reviewable CAT was proposed. A worst-case but still reasonable answering strategy and the Wainer-like manipulation strategy were simulated to evaluate the estimation precision of reviewable unidimensional computerized adaptive testing (UCAT) and multidimensional computerized adaptive testing (MCAT) under a series of BIP settings. For both UCAT and MCAT, it was found that the estimation precision of the BIP method improved as the number of blocks increased or the item pocket size decreased under the reasonable strategy. The BIP method was more effective in handling the Wainer-like strategy. With the help of block design, the BIP method can still maintain acceptable estimation precision under slightly large total IP size conditions. These results suggested that the BIP method was a reliable solution for both reviewable UCAT and MCAT.

A LASSO-Based Method for Detecting Item-Trait Patterns of Replenished Items in Multidimensional Computerized Adaptive Testing.

Multidimensional computerized adaptive testing (MCAT) is one of the widely discussed topics in psychometrics. Within the context of item replenishment in MCAT, it is important to identify the item-trait pattern for each replenished item, which indicates the set of the latent traits that are measured by each replenished item in the item pool. We propose a pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) to detect the optimal item-trait patterns of the replenished items via an MCAT test. Simulation studies are conducted to investigate the performance of the proposed method in pattern recognition accuracy under different conditions across various latent trait correlation, item discrimination, test lengths, and item selection criteria in the test. Results show that the proposed method can accurately and efficiently identify the item-trait patterns of the replenished items in both the two-dimensional and three-dimensional item pools.

Multidimensional Test Assembly Using Mixed-Integer Linear Programming: An Application of Kullback-Leibler Information.

Many educational testing programs require different test forms with minimal or no item overlap. At the same time, the test forms should be parallel in terms of their statistical and content-related properties. A well-established method to assemble parallel test forms is to apply combinatorial optimization using mixed-integer linear programming (MILP). Using this approach, in the unidimensional case, Fisher information (FI) is commonly used as the statistical target to obtain parallelism. In the multidimensional case, however, FI is a multidimensional matrix, which complicates its use as a statistical target. Previous research addressing this problem focused on item selection criteria for multidimensional computerized adaptive testing (MCAT). Yet these selection criteria are not directly transferable to the assembly of linear parallel test forms. To bridge this gap the authors derive different statistical targets, based on either FI or the Kullback-Leibler (KL) divergence, that can be applied in MILP models to assemble multidimensional parallel test forms. Using simulated item pools and an item pool based on empirical items, the proposed statistical targets are compared and evaluated. Promising results with respect to the KL-based statistical targets are presented and discussed.

Application of Dimension Reduction to CAT Item Selection Under the Bifactor Model.

Multidimensional computerized adaptive testing (MCAT) based on the bifactor model is suitable for tests with multidimensional bifactor measurement structures. Several item selection methods that proved to be more advantageous than the maximum Fisher information method are not practical for bifactor MCAT due to time-consuming computations resulting from high dimensionality. To make them applicable in bifactor MCAT, dimension reduction is applied to four item selection methods, which are the posterior-weighted Fisher D-optimality (PDO) and three non-Fisher information-based methods-posterior expected Kullback-Leibler information (PKL), continuous entropy (CE), and mutual information (MI). They were compared with the Bayesian D-optimality (BDO) method in terms of estimation precision. When both the general and group factors are the measurement objectives, BDO, PDO, CE, and MI perform equally well and better than PKL. When the group factors represent nuisance dimensions, MI and CE perform the best in estimating the general factor, followed by the BDO, PDO, and PKL. How the bifactor pattern and test length affect estimation accuracy was also discussed.

Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models.

Current use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback-Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback-Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees' abilities, which shed light on item bank construction for non-compensatory items.

Investigating The Effect of Exposure-Control Strategies on Item Selection Methods in MCAT

This study aims to investigate the effect of different item exposure controlling strategies on item selection methods in the context of multidimensional computerized adaptive testing (MCAT). Additionally, this study aims to examine to what extend the restrictive threshold (RT) and the restrictive progressive (RPG) exposure methods suppress the item exposure rates and increase the exposure rates of underexposed items without losing psychometric precision in MCAT. For this purpose, the performance of four item selection methods with and without exposure controls are evaluated and compared so as to determine how results differ when item exposure controlling strategies are applied with Monte-Carlo simulation method. The four item selection methods employed in this study are D-optimality, Kullback–Leibler information (KLP), the minimized error variance of linear combination score with equal weight (V1), the composite score with optimized weight (V2). On the other hand, the maximum priority index (MPI) method proposed for unidimensional CAT and two other item exposure control methods, that are RT and RPG methods proposed for cognitive diagnostic CAT, are adopted. The results show that: (1) KLP, D-optimality, and V1 performed better in recovering domain scores, and all outperformed V2 with respect to precision; (2) although V1 and V2 offer improved item bank usage rates, KLP, D-optimality, V1, and V2 produced an unbalanced distribution of item exposure rates; (3) all exposure control strategies improve the exposure uniformity greatly and with very little loss in psychometric precision; (4) RPG and MPI perform similarly in exposure control, and outperformed RT exposure control method.

Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items.

Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases.

Measurement Efficiency for Fixed-Precision Multidimensional Computerized Adaptive Tests: Comparing Health Measurement and Educational Testing Using Example Banks.

It is currently not entirely clear to what degree the research on multidimensional computerized adaptive testing (CAT) conducted in the field of educational testing can be generalized to fields such as health assessment, where CAT design factors differ considerably from those typically used in educational testing. In this study, the impact of a number of important design factors on CAT performance is systematically evaluated, using realistic example item banks for two main scenarios: health assessment (polytomous items, small to medium item bank sizes, high discrimination parameters) and educational testing (dichotomous items, large item banks, small- to medium-sized discrimination parameters). Measurement efficiency is evaluated for both between-item multidimensional CATs and separate unidimensional CATs for each latent dimension. In this study, we focus on fixed-precision (variable-length) CATs because it is both feasible and desirable in health settings, but so far most research regarding CAT has focused on fixed-length testing. This study shows that the benefits associated with fixed-precision multidimensional CAT hold under a wide variety of circumstances.

Measuring Patient-Reported Outcomes Adaptively: Multidimensionality Matters!

As there is currently a marked increase in the use of both unidimensional (UCAT) and multidimensional computerized adaptive testing (MCAT) in psychological and health measurement, the main aim of the present study is to assess the incremental value of using MCAT rather than separate UCATs for each dimension. Simulations are based on empirical data that could be considered typical for health measurement: a large number of dimensions (4), strong correlations among dimensions (.77-.87), and polytomously scored response data. Both variable- (SE < .316, SE < .387) and fixed-length conditions (total test length of 12, 20, or 32 items) are studied. The item parameters and variance–covariance matrix Φ are estimated with the multidimensional graded response model (GRM). Outcome variables include computerized adaptive test (CAT) length, root mean square error (RMSE), and bias. Both simulated and empirical latent trait distributions are used to sample vectors of true scores. MCATs were generally more efficient (in terms of test length) and more accurate (in terms of RMSE) than their UCAT counterparts. Absolute average bias was highest for variable-length UCATs with termination rule SE < .387. Test length of variable-length MCATs was on average 20% to 25% shorter than test length across separate UCATs. This study showed that there are clear advantages of using MCAT rather than UCAT in a setting typical for health measurement.