An Overview of Models for Response Times and Processes in Cognitive Tests

Boeck, Paul De; Jeon, Minjeong

doi:10.3389/fpsyg.2019.00102

Cited by 151 publications

(177 citation statements)

References 88 publications

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“…This suggests that although the variation of time effects is small relative to the ability variation, these models change the interpretation of ability itself, as other research has shown [De Boeck, & Jeon, 2019;Bolsinova, et. al, 2017].…”

Section: Conclusion and Discussionmentioning

confidence: 87%

“…However, to process such data, it is necessary to have a fitting statistical model which utilizes this information [De Boeck, & Jeon, 2019]. This means that items become described by more and more parameters, which more precisely describe the probability of a correct response in terms of information processing.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…al., 2014]. Although research in computational behavioral science seeks to incorporate these data into modelling Item Response theory, only models for the simultaneous modelling of response times and response accuracy remain well-developed [De Boeck, & Jeon, 2019]. Therefore, one of the ways to investigate cross-national comparability in terms of method bias is to study information from response times.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Cross-National Comparability of Testing Using Response Times

Federiakin

2020

SSRN Journal

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The cross-national comparability of testing results is one of the main concerns associated with international assessment. One way to approach this concern is the theoretical framework of crossnational comparability. One such framework, proposed by Van de Vijver and Tanzer [1997], describes the bias in item meaning across countries and the bias in terms of test administration procedures and respondent behavior across countries. To study how items function in terms of respondent behavior and in terms of their psychological meaning, we introduce the concept of timerelated differential item functioning (DIF). This concept is similar to traditional DIF but describes the incomparable time parameters of items across countries rather than item difficulty parameters. We discuss time-related DIF, referencing dual processing cognitive theory and illustrate how it limits the interpretability of a certain type of time-related DIF. We also demonstrate the analysis with real data, and discuss the difference introduced by response time information in parameter interpretation and modelling results. JEL Classification: Z0.

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Section: Conclusion and Discussionmentioning

confidence: 87%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the Cross-National Comparability of Testing Using Response Times

Federiakin

2020

SSRN Journal

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“…In this section, we begin with discussions of statistical and psychological approaches for understanding interplay between speed and accuracy. Other accounts aim to be relatively comprehensive [7,8], we focus on specific features of both approaches that are relevant given our data and findings.…”

Section: Interplay Of Speed and Accuracymentioning

confidence: 99%

“…Figure 5 (Panel B) contains results from stratified models wherein log(t) is first residualized based on a simple version of Eqn 5 including only sequence number and item/person fixed effects. 7 Note that these residuals are similar to the z pi in [26]. We then use the residuals, denoted ∆ pi , to predict accuracy in a simplified version of Eqn 6.…”

Section: Ability-based Heterogeneity In Response Accelerationmentioning

confidence: 99%

Variation in Respondent Speed and its Implications: Evidence from an Adaptive Testing Scenario

Domingue¹,

Kanopka²,

Stenhaug³

et al. 2020

Preprint

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As our ability to collect data about respondents increases, approaches for incorporating ancillary data features such as response time are of heightened interest. Models for response time have been advanced, but relatively limited large-scale empirical investigations have been conducted. We take advantage of a unique and massive dataset—data from computer adaptive administrations of the NWEA MAP Growth assessment in two states consisting of roughly 1/4 billion item responses—containing both item responses plus response times to shed light on emergent features of response time behavior. We focus on two behaviors in particular. The first, response acceleration, is a reduction in response time for responses that occur relatively late on the assessment. We further note that such reductions are heterogeneous as a function of estimated ability (lower ability estimates are associated with larger increases in acceleration) and that reductions in response time on later items lead to reductions in accuracy relative to expectation. We also document variation in interplay between speed and accuracy. In some cases, additional time spent on an item is associated with an increase in accuracy; in other cases, the opposite is true. This finding has potential connections to the nascent literature on different within-person response processes. We argue that our approach may be useful in other settings and that the behaviors observed here should be of interest in other data.

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A latent space accumulator model for response time: Applications to cognitive assessment data

Jin,

Yun,

Kim

et al. 2023

Stat

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Response time has attracted increased interest in educational and psychological assessment for, for example, measuring test takers' processing speed, improving the measurement accuracy of ability and understanding aberrant response behaviour. Most models for response time analysis are based on a parametric assumption about the response time distribution. The Cox proportional hazard model has been utilized for response time analysis for the advantages of not requiring a distributional assumption of response time and enabling meaningful interpretations with respect to response processes. In this paper, we present a new version of the proportional hazard model, called a latent space accumulator model, for cognitive assessment data based on accumulators for two competing response outcomes, such as correct versus incorrect responses. The proposed model extends a previous accumulator model by capturing dependencies between respondents and test items across accumulators in the form of distances in a two‐dimensional Euclidean space. A fully Bayesian approach is developed to estimate the proposed model. The utilities of the proposed model are illustrated with two real data examples.

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An Overview of Models for Response Times and Processes in Cognitive Tests

Cited by 151 publications

References 88 publications

Investigating the Cross-National Comparability of Testing Using Response Times

Investigating the Cross-National Comparability of Testing Using Response Times

Variation in Respondent Speed and its Implications: Evidence from an Adaptive Testing Scenario

A latent space accumulator model for response time: Applications to cognitive assessment data

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