Joint Testlet Cognitive Diagnosis Modeling for Paired Local Item Dependence in Response Times and Response Accuracy.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhan P
PROVIDER: S-EPMC5996944 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Computer-based testing (CBT) is becoming increasingly popular in assessing test-takers' latent abilities and making inferences regarding their cognitive processes. In addition to collecting item responses, an important benefit of using CBT is that response times (RTs) can also be recorded and used in subsequent analyses. To better understand the structural relations between multidimensional cognitive attributes and the working speed of test-takers, this research proposes a joint-modeling approach that integrates compensatory multidimensional latent traits and response speediness using item responses and RTs. The joint model is cast as a multilevel model in which the structural relation between working speed and accuracy are connected through their variance-covariance structures. The feasibility of this modeling approach is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme. The results indicate that integrating RTs increased model parameter recovery and precision. In addition, Program of International Student Assessment (PISA) 2015 mathematics standard unit items are analyzed to further evaluate the feasibility of the approach to recover model parameters.
| S-EPMC6745633 | biostudies-literature
Project description:The most common process variable available for analysis due to tests presented in a computerized form is response time. Psychometric models have been developed for joint modeling of response accuracy and response time in which response time is an additional source of information about ability and about the underlying response processes. While traditional models assume conditional independence between response time and accuracy given ability and speed latent variables (van der Linden, 2007), recently multiple studies (De Boeck and Partchev, 2012; Meng et al., 2015; Bolsinova et al., 2017a,b) have shown that violations of conditional independence are not rare and that there is more to learn from the conditional dependence between response time and accuracy. When it comes to conditional dependence between time and accuracy, authors typically focus on positive conditional dependence (i.e., relatively slow responses are more often correct) and negative conditional dependence (i.e., relatively fast responses are more often correct), which implies monotone conditional dependence. Moreover, most existing models specify the relationship to be linear. However, this assumption of monotone and linear conditional dependence does not necessarily hold in practice, and assuming linearity might distort the conclusions about the relationship between time and accuracy. In this paper we develop methods for exploring nonlinear conditional dependence between response time and accuracy. Three different approaches are proposed: (1) A joint model for quadratic conditional dependence is developed as an extension of the response moderation models for time and accuracy (Bolsinova et al., 2017b); (2) A joint model for multiple-category conditional dependence is developed as an extension of the fast-slow model of Partchev and De Boeck (2012); (3) An indicator-level nonparametric moderation method (Bolsinova and Molenaar, in press) is used with residual log-response time as a predictor for the item intercept and item slope. Furthermore, we propose using nonparametric moderation to evaluate the viability of the assumption of linearity of conditional dependence by performing posterior predictive checks for the linear conditional dependence model. The developed methods are illustrated using data from an educational test in which, for the majority of the items, conditional dependence is shown to be nonlinear.
| S-EPMC6137682 | biostudies-literature
Project description:Research related to the fit evaluation at the item level involving cognitive diagnosis models (CDMs) has been scarce. According to the parsimony principle, balancing goodness of fit against model complexity is necessary. General CDMs require a larger sample size to be estimated reliably, and can lead to worse attribute classification accuracy than the appropriate reduced models when the sample size is small and the item quality is poor, which is typically the case in many empirical applications. The main purpose of this study was to systematically examine the statistical properties of four inferential item-fit statistics: S-X2 , the likelihood ratio (LR) test, the Wald (W) test, and the Lagrange multiplier (LM) test. To evaluate the performance of the statistics, a comprehensive set of factors, namely, sample size, correlational structure, test length, item quality, and generating model, is systematically manipulated using Monte Carlo methods. Results show that the S-X2 statistic has unacceptable power. Type I error and power comparisons favor LR and W tests over the LM test. However, all the statistics are highly affected by the item quality. With a few exceptions, their performance is only acceptable when the item quality is high. In some cases, this effect can be ameliorated by an increase in sample size and test length. This implies that using the above statistics to assess item fit in practical settings when the item quality is low remains a challenge.
| S-EPMC5978477 | biostudies-other
Project description:The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur. Even if the primary interest is to provide diagnostic classification of respondents, misspecification of missing data mechanism may lead to biased conclusions. This paper proposes a joint cognitive diagnosis modeling of item responses and item-level missing data mechanism. A Bayesian Markov chain Monte Carlo (MCMC) method is developed for model parameter estimation. Our simulation studies examine the parameter recovery under different missing data mechanisms. The parameters could be recovered well with correct use of missing data mechanism for model fit, and missing that is not at random is less sensitive to incorrect use. The Program for International Student Assessment (PISA) 2015 computer-based mathematics data are applied to demonstrate the practical value of the proposed method.
| S-EPMC7733994 | biostudies-literature
Project description:Composite assessments aim to combine different aspects of a disease in a single score and are utilized in a variety of therapeutic areas. The data arising from these evaluations are inherently discrete with distinct statistical properties. This tutorial presents the framework of the item response theory (IRT) for the analysis of this data type in a pharmacometric context. The article considers both conceptual (terms and assumptions) and practical questions (modeling software, data requirements, and model building).
| S-EPMC5915608 | biostudies-literature
Project description:Responses to survey data are determined not only by item characteristics and respondents' trait standings but also by response styles. Recently, methods for modeling response style with personality and attitudinal data have turned toward the use of anchoring vignettes, which provide fixed rating targets. Although existing research is promising, a few outstanding questions remain. First, it is not known how many vignettes and vignette ratings are necessary to identify response style parameters. Second, the comparative accuracy of these models is largely unexplored. Third, it remains unclear whether correcting for response style improves criterion validity. Both simulated data and data observed from a population-representative sample responding to a measure of personality pathology (the Personality Inventory for DSM-5 [PID-5]) were modeled using an array of response style models. In simulations, most models estimating response styles outperformed the graded response model (GRM), and in observed data, all response style models were superior to the GRM. Correcting for response style had a small, but in some cases significant, effect on the prediction of self-reported social dysfunction.
| S-EPMC6297915 | biostudies-literature
Project description:Currently, there are two predominant approaches in adaptive testing. One, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT), is based on cognitive diagnosis models, and the other, the traditional CAT, is based on item response theory. The present study evaluates the performance of two item selection rules (ISRs) originally developed in the CD-CAT framework, the double Kullback-Leibler information (DKL) and the generalized deterministic inputs, noisy "and" gate model discrimination index (GDI), in the context of traditional CAT. The accuracy and test security associated with these two ISRs are compared to those of the point Fisher information and weighted KL using a simulation study. The impact of the trait level estimation method is also investigated. The results show that the new ISRs, particularly DKL, could be used to improve the accuracy of CAT. Better accuracy for DKL is achieved at the expense of higher item overlap rate. Differences among the item selection rules become smaller as the test gets longer. The two CD-CAT ISRs select different types of items: items with the highest possible a parameter with DKL, and items with the lowest possible c parameter with GDI. Regarding the trait level estimator, expected a posteriori method is generally better in the first stages of the CAT, and converges with the maximum likelihood method when a medium to large number of items are involved. The use of DKL can be recommended in low-stakes settings where test security is less of a concern.
| S-EPMC6953845 | biostudies-literature
Project description:Item-level data from composite scales can be analyzed with pharmacometric item response theory (IRT) models to improve the quantification of disease severity compared with the use of total composite scores. However, regular IRT models assume unidimensionality, which is violated in the scale measuring iatrogenic withdrawal in children because some items are also affected by pain, undersedation, or delirium. Here, we compare regular IRT modelling of pediatric iatrogenic withdrawal symptom data with two new analysis approaches in which the latent variable is guided towards the condition of interest using numerical withdrawal severity scored by nurses as a "supervising variable:" supervised IRT (sIRT) and supervised multi-dimensional (smIRT) modelling. In this example, in which the items scores are affected by multiple conditions, regular IRT modeling is worse to quantify disease severity than the total composite score, whereas improved performance compared with the composite score is observed for the sIRT and smIRT models.
| S-EPMC6930857 | biostudies-literature
Project description:Some large-scale testing requires examinees to select and answer a fixed number of items from given items (e.g., select one out of the three items). Usually, they are constructed-response items that are marked by human raters. In this examinee-selected item (ESI) design, some examinees may benefit more than others from choosing easier items to answer, and so the missing data induced by the design become missing not at random (MNAR). Although item response theory (IRT) models have recently been developed to account for MNAR data in the ESI design, they do not consider the rater effect; thus, their utility is seriously restricted. In this study, two methods are developed: the first one is a new IRT model to account for both MNAR data and rater severity simultaneously, and the second one adapts conditional maximum likelihood estimation and pairwise estimation methods to the ESI design with the rater effect. A series of simulations was then conducted to compare their performance with those of conventional IRT models that ignored MNAR data or rater severity. The results indicated a good parameter recovery for the new model. The conditional maximum likelihood estimation and pairwise estimation methods were applicable when the Rasch models fit the data, but the conventional IRT models yielded biased parameter estimates. An empirical example was given to illustrate these new initiatives.
| S-EPMC6696873 | biostudies-literature
Project description:Chicks trained to identify a target item in a sagittally-oriented series of identical items show a higher accuracy for the target on the left, rather than that on the right, at test when the series was rotated by 90°. Such bias seems to be due to a right hemispheric dominance in visuospatial tasks. Up to now, the bias was highlighted by looking at accuracy, the measure mostly used in non-human studies to detect spatial numerical association, SNA. In the present study, processing by each hemisphere was assessed by scoring three variables: accuracy, response times and direction of approach. Domestic chicks were tested under monocular vision conditions, as in the avian brain input to each eye is mostly processed by the contralateral hemisphere. Four-day-old chicks learnt to peck at the 4th element in a sagittal series of 10 identical elements. At test, when facing a series oriented fronto-parallel, birds confined their responses to the visible hemifield, with high accuracy for the 4th element. The first element in the series was also highly selected, suggesting an anchoring strategy to start the proto-counting at one end of the series. In the left monocular condition, chicks approached the series starting from the left, and in the right monocular condition, they started from the right. Both hemispheres appear to exploit the same strategy, scanning the series from the most lateral element in the clear hemifield. Remarkably, there was no effect in the response times: equal latency was scored for correct or incorrect and for left vs. right responses. Overall, these data indicate that the measures implying a direction of choice, accuracy and direction of approach, and not velocity, i.e., response times, can highlight SNA in this paradigm. We discuss the relevance of the selected measures to unveil SNA.
| S-EPMC8483340 | biostudies-literature