Literature DB >> 29843531

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology.

Hansjörg Plieninger1, Daniel W Heck1.   

Abstract

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt's model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

Entities:  

Keywords:  Acquiescence; Bayesian hierarchical modeling; item response theory (IRT); multinomial processing tree models; response styles

Mesh:

Year:  2018        PMID: 29843531     DOI: 10.1080/00273171.2018.1469966

Source DB:  PubMed          Journal:  Multivariate Behav Res        ISSN: 0027-3171            Impact factor:   5.923


  8 in total

1.  Contextual Responses to Affirmative and/or Reversed-Worded Items.

Authors:  Ulf Böckenholt
Journal:  Psychometrika       Date:  2019-09-04       Impact factor: 2.500

2.  Measuring Response Style Stability Across Constructs With Item Response Trees.

Authors:  Allison J Ames
Journal:  Educ Psychol Meas       Date:  2021-06-02       Impact factor: 2.821

3.  A Novel Deep Framework for English Communication Based on Educational Psychology Perspective.

Authors:  Ying Wang; Liang Zheng
Journal:  Front Public Health       Date:  2022-06-21

4.  Seeing the Forest and the Trees: Comparison of Two IRTree Models to Investigate the Impact of Full Versus Endpoint-Only Response Option Labeling.

Authors:  Elisabeth M Spratto; Brian C Leventhal; Deborah L Bandalos
Journal:  Educ Psychol Meas       Date:  2020-05-02       Impact factor: 2.821

5.  Explaining Variability in Response Style Traits: A Covariate-Adjusted IRTree.

Authors:  Allison J Ames; Aaron J Myers
Journal:  Educ Psychol Meas       Date:  2020-11-04       Impact factor: 3.088

6.  Modeling Faking in the Multidimensional Forced-Choice Format: The Faking Mixture Model.

Authors:  Susanne Frick
Journal:  Psychometrika       Date:  2021-12-20       Impact factor: 2.290

7.  Modeling Wording Effects Does Not Help in Recovering Uncontaminated Person Scores: A Systematic Evaluation With Random Intercept Item Factor Analysis.

Authors:  María Dolores Nieto; Luis Eduardo Garrido; Agustín Martínez-Molina; Francisco José Abad
Journal:  Front Psychol       Date:  2021-06-02

8.  Validity of Three IRT Models for Measuring and Controlling Extreme and Midpoint Response Styles.

Authors:  Yingbin Zhang; Yehui Wang
Journal:  Front Psychol       Date:  2020-02-21
  8 in total

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