The Graded Response Differential Discrimination Model Accounting for Extreme Response Style

Lubbe, Dirk; Schuster, Christof

doi:10.1080/00273171.2017.1350561

Cited by 7 publications

(19 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it can be by assuming that items are categorized at a common threshold of 0 in Equation 11. This fixes the origin of Y j and provides a plausible interpretation (see Lubbe & Schuster, 2017): negative values of Y j lead to denial while positive values lead to item endorsement.…”

Section: Item Calibrationmentioning

confidence: 95%

“…At the opposite extreme, as the PDD increases, the probability of responding in different categories becomes progressively more undifferentiated. This way of working contrasts with that of MM for graded responses (Lubbe & Schuster, 2017). As in the linear case, the person slope parameter in the MM modifies the expected response, so that low slope values imply that the response is more likely to lie in the middle categories whereas with large values it is more likely to be in the outer categories (see Lubbe & Schuster, 2017, for details).…”

Section: The Dtgrm and Dtbrmmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive IRT Approach for Modeling Binary, Graded, and Continuous Responses With Error in Persons and Items

Ferrando

2018

Applied Psychological Measurement

View full text Add to dashboard Cite

Dual item response theory (IRT) models in which items and individuals have different amounts of measurement error have been proposed in the literature. Any developments in these models, however, are feasible only for continuous responses. This article discusses a comprehensive dual modeling approach, based on underlying latent response variables, from which specific models for continuous, graded, and binary responses are obtained. Procedures for (a) calibrating the items, (b) scoring individuals, (c) assessing model appropriateness, and (d) assessing measurement precision are discussed for all the resulting models. Simulation results suggest that the proposal is quite feasible. A practical illustration is given with an empirical example in the personality domain.

show abstract

Section: Item Calibrationmentioning

confidence: 95%

Section: The Dtgrm and Dtbrmmentioning

confidence: 99%

A Comprehensive IRT Approach for Modeling Binary, Graded, and Continuous Responses With Error in Persons and Items

Ferrando

2018

Applied Psychological Measurement

View full text Add to dashboard Cite

show abstract

“…To illustrate the unique features of the STM, we compare it to (a) the GRM for measuring person reliability (PRM) of Ferrando (2009) and (b) the graded response differential discrimination model (GRDDM) of Lubbe and Schuster (2017). Similar to the STM, they are both modified versions of the GRM using an additional scaling factor.…”

Section: Other Scaling Factor Modelsmentioning

confidence: 99%

“…The use of a responder-specific scaling factors is not novel and has been used in other forms to model idiosyncratic response behavior. Ferrando (2009) proposed a model for ordered categorical items with responder-specific residual scales, and Lubbe and Schuster (2017) proposed a model that assumes responder-specific factor loadings. These models, although conceptually similar, achieve different purposes when compared to a model with heterogeneous thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we demonstrate why we consider this new approach of modeling scaled thresholds as superior by contrasting it to other previously proposed model specifications. Third, we compare the present model to other types of scaling factor models, specifically those of Ferrando (2009) and Lubbe and Schuster (2017), and point out structural differences. Fourth, we demonstrate the small sample parameter recovery of our model using Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Scaled Threshold Model for Measuring Extreme Response Style

Lubbe

Schuster

2019

Journal of Educational and Behavioral Statistics

Self Cite

View full text Add to dashboard Cite

Extreme response style is the tendency of individuals to prefer the extreme categories of a rating scale irrespective of item content. It has been shown repeatedly that individual response style differences affect the reliability and validity of item responses and should, therefore, be considered carefully. To account for extreme response style (ERS) in ordered categorical item responses, it has been proposed to model responder-specific sets of category thresholds in connection with established polytomous item response models. An elegant approach to achieve this is to introduce a responder-specific scaling factor that modifies intervals between thresholds. By individually expanding or contracting intervals between thresholds, preferences for selecting either the outer or inner response categories can be modeled. However, for a responder-specific scaling factor to appropriately account for ERS, there are two important aspects that have not been considered previously and which, if ignored, will lead to questionable model properties. Specifically, the centering of threshold parameters and the type of category probability logit need to be considered carefully. In the present article, a scaled threshold model is proposed, which accounts for these considerations. Instructions on model fitting are given together with SAS PROC NLMIXED program code, and the model’s application and interpretation is demonstrated using simulation studies and two empirical examples.

show abstract

Different approaches to modeling response styles in divide-by-total item response theory models (part 1): A model integration.

Henninger¹,

Meiser

2020

Psychological Methods

View full text Add to dashboard Cite

A large variety of item response theory (IRT) modeling approaches aim at measuring and correcting for response styles in rating data. Here, we integrate response style models of the divide-by-total model family into one superordinate framework that parameterizes response styles as person-specific shifts in threshold parameters. This superordinate framework allows us to structure and compare existing approaches to modeling response styles and therewith makes model-implied restrictions explicit. With a simulation study, we show how the new framework allows us to assess consequences of violations of model assumptions and to compare response style estimates across different model parameterizations. The integrative framework of divide-by-total modeling approaches facilitates the correction for and examination of response styles. In addition to providing a superordinate framework for psychometric research, it gives guidance to applied researchers for model selection and specification in psychological assessment.

show abstract

The Graded Response Differential Discrimination Model Accounting for Extreme Response Style

Cited by 7 publications

References 43 publications

A Comprehensive IRT Approach for Modeling Binary, Graded, and Continuous Responses With Error in Persons and Items

A Comprehensive IRT Approach for Modeling Binary, Graded, and Continuous Responses With Error in Persons and Items

A Scaled Threshold Model for Measuring Extreme Response Style

Different approaches to modeling response styles in divide-by-total item response theory models (part 1): A model integration.

Contact Info

Product

Resources

About