When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking

Self-report questionnaires are widely used in research and practice. In most applications, the vulnerability of these questionnaires to response biases like faking is ignored. However, especially in high-stakes situations such as personnel selection, measurement can be severely biased when test-takers engage in faking to present themselves more favorably. To separate faking-related variance from substantive trait variance, the Multidimensional Nominal Response Model (MNRM) has been used to reduce systematic bias in trait estimation by allowing for item-specific relations between response categories and social desirability. A critical but untested assumption of this approach is that perceptions of social desirability are homogeneous across test-takers. However, individuals may differ considerably in how they perceive the desirability of the item content. Here, we conducted simulation studies to investigate how violations of this assumption affect the MNRM’s ability to recover substantive trait person parameters. We implemented three distinct manipulations of heterogeneous desirability perceptions and examined their impact on person parameter recovery. Results showed that the MNRM is robust against violations of homogeneous social desirability perceptions as long as test-takers’ faking behavior is aligned with their perceived desirability of the item content. In contrast, when test-takers fake responses in ways that are inconsistent with item-wise desirability perceptions, parameter recovery seems to decline. Implications for practice and possible model extensions are discussed.

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Publication Details

Journal
Applied Psychological Measurement
Published
2026-06-19
DOI
https://doi.org/10.1177/01466216261460893
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
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article

When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking

Timo Seitz, Julius David Kleinbub
Applied Psychological Measurement
Psychometric Methodologies and Testing
article

When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking

Timo Seitz, Julius David Kleinbub
article en

Abstract

Self-report questionnaires are widely used in research and practice. In most applications, the vulnerability of these questionnaires to response biases like faking is ignored. However, especially in high-stakes situations such as personnel selection, measurement can be severely biased when test-takers engage in faking to present themselves more favorably. To separate faking-related variance from substantive trait variance, the Multidimensional Nominal Response Model (MNRM) has been used to reduce systematic bias in trait estimation by allowing for item-specific relations between response categories and social desirability. A critical but untested assumption of this approach is that perceptions of social desirability are homogeneous across test-takers. However, individuals may differ considerably in how they perceive the desirability of the item content. Here, we conducted simulation studies to investigate how violations of this assumption affect the MNRM’s ability to recover substantive trait person parameters. We implemented three distinct manipulations of heterogeneous desirability perceptions and examined their impact on person parameter recovery. Results showed that the MNRM is robust against violations of homogeneous social desirability perceptions as long as test-takers’ faking behavior is aligned with their perceived desirability of the item content. In contrast, when test-takers fake responses in ways that are inconsistent with item-wise desirability perceptions, parameter recovery seems to decline. Implications for practice and possible model extensions are discussed.

Applied Psychological Measurement
University of Mannheim (DE)
Reduced inequalities
Openalex Percentile: Top 5%
Psychometric Methodologies and Testing
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