Conditional Dependence across Slow and Fast Item Responses: With a Latent Space Item Response Modeling Approach.

There recently have been many studies examining conditional dependence between response accuracy and response times in cognitive tests. While most previous research has focused on revealing a general pattern of conditional dependence for all respondents and items, it is plausible that the pattern may vary across respondents and items. In this paper, we attend to its potential heterogeneity and examine the item and person specificities involved in the conditional dependence between item responses and response times. To this end, we use a latent space item response theory (LSIRT) approach with an interaction map that visualizes conditional dependence in response data in the form of item-respondent interactions. We incorporate response time information into the interaction map by applying LSIRT models to slow and fast item responses. Through empirical illustrations with three cognitive test datasets, we confirm the presence and patterns of conditional dependence between item responses and response times, a result consistent with previous studies. Our results further illustrate the heterogeneity in the conditional dependence across respondents, which provides insights into understanding individuals' underlying item-solving processes in cognitive tests. Some practical implications of the results and the use of interaction maps in cognitive tests are discussed.

A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.

Traditional measurement models assume that all item responses correlate with each other only through their underlying latent variables. This conditional independence assumption has been extended in joint models of responses and response times (RTs), implying that an item has the same item characteristics fors all respondents regardless of levels of latent ability/trait and speed. However, previous studies have shown that this assumption is violated in various types of tests and questionnaires and there are substantial interactions between respondents and items that cannot be captured by person- and item-effect parameters in psychometric models with the conditional independence assumption. To study the existence and potential cognitive sources of conditional dependence and utilize it to extract diagnostic information for respondents and items, we propose a diffusion item response theory model integrated with the latent space of variations in information processing rate of within-individual measurement processes. Respondents and items are mapped onto the latent space, and their distances represent conditional dependence and unexplained interactions. We provide three empirical applications to illustrate (1) how to use an estimated latent space to inform conditional dependence and its relation to person and item measures, (2) how to derive diagnostic feedback personalized for respondents, and (3) how to validate estimated results with an external measure. We also provide a simulation study to support that the proposed approach can accurately recover its parameters and detect conditional dependence underlying data.

Diagnosing a 12-Item Dataset of Raven Matrices: With Dexter.

We analyze a 12-item version of Raven's Standard Progressive Matrices test, traditionally scored with the sum score. We discuss some important differences between assessment in practice and psychometric modelling. We demonstrate some advanced diagnostic tools in the freely available R package, dexter. We find that the first item in the test functions badly-at a guess, because the subjects were not given exercise items before the live test.

How much power and speed is measured in this test?

An old issue in psychological assessment is to what extent power and speed each are measured by a given intelligence test. Starting from accuracy and response time data, an approach based on posterior time limits (cut-offs of recorded response time) leads to three kinds of recoded data: time data (whether or not the response precedes the cut-off), time-accuracy data (whether or not a response is correct and precedes the cut-off), and accuracy data (as time-accuracy data, but coded as missing when not preceding the time cut-off). Each type of data can be modeled as binary responses. Speed and power are investigated through the effect of posterior time limits on two main aspects: (a) the latent variable that is measured: whether it is more power-related or more speed-related; (b) how well the latent variable (of whatever kind) is measured through the item(s). As empirical data, we use responses and response times for a verbal analogies test. The main findings are that, independent of the posterior time limit, basically the same latent speed trait was measured through the time data, and basically the same latent power trait was measured through the accuracy data, while for the time-accuracy data the nature of the latent trait moved from power to speed when the posterior time limit was reduced. It was also found that a reduction of the posterior time limit had no negative effect on the reliability of the latent trait measures (of whatever kind).

Parameter estimation of multiple item response profile model.

Multiple item response profile (MIRP) models are models with crossed fixed and random effects. At least one between-person factor is crossed with at least one within-person factor, and the persons nested within the levels of the between-person factor are crossed with the items within levels of the within-person factor. Maximum likelihood estimation (MLE) of models for binary data with crossed random effects is challenging. This is because the marginal likelihood does not have a closed form, so that MLE requires numerical or Monte Carlo integration. In addition, the multidimensional structure of MIRPs makes the estimation complex. In this paper, three different estimation methods to meet these challenges are described: the Laplace approximation to the integrand; hierarchical Bayesian analysis, a simulation-based method; and an alternating imputation posterior with adaptive quadrature as the approximation to the integral. In addition, this paper discusses the advantages and disadvantages of these three estimation methods for MIRPs. The three algorithms are compared in a real data application and a simulation study was also done to compare their behaviour.

Spider phobics more easily see a spider in morphed schematic pictures.

BACKGROUND: Individuals with social phobia are more likely to misinterpret ambiguous social situations as more threatening, i.e. they show an interpretive bias. This study investigated whether such a bias also exists in specific phobia. METHODS: Individuals with spider phobia or social phobia, spider aficionados and non-phobic controls saw morphed stimuli that gradually transformed from a schematic picture of a flower into a schematic picture of a spider by shifting the outlines of the petals until they turned into spider legs. Participants' task was to decide whether each stimulus was more similar to a spider, a flower or to neither object while EEG was recorded. RESULTS: An interpretive bias was found in spider phobia on a behavioral level: with the first opening of the petals of the flower anchor, spider phobics rated the stimuli as more unpleasant and arousing than the control groups and showed an elevated latent trait to classify a stimulus as a spider and a response-time advantage for spider-like stimuli. No cortical correlates on the level of ERPs of this interpretive bias could be identified. However, consistent with previous studies, social and spider phobic persons exhibited generally enhanced visual P1 amplitudes indicative of hypervigilance in phobia. CONCLUSION: Results suggest an interpretive bias and generalization of phobia-specific responses in specific phobia. Similar effects have been observed in other anxiety disorders, such as social phobia and posttraumatic stress disorder.