Evidence map›Paper›PMID 42245766›Full record

ArticleQuality & quantity2026

Unraveling honest responding: a systematic review on the effectiveness of social desirability bias reduction methods in survey research.

Emma Zaal, Yfke Ongena, Nina van der Velden, Dan Loughnan, John Hoeks

Abstract read
In one paragraph

Article in Quality & quantity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Emma ZaalFaculty of Arts, University of Groningen, Groningen, The Netherlands.ORCID 0000-0002-2105-6594
Yfke OngenaFaculty of Arts, University of Groningen, Groningen, The Netherlands.ORCID 0000-0001-6678-7860
Nina van der VeldenFaculty of Arts, University of Groningen, Groningen, The Netherlands.ORCID 0009-0004-7252-4281
Dan LoughnanFaculty of Social Sciences, Radboud University, Nijmegen, The Netherlands.ORCID 0009-0009-8963-0749
John HoeksFaculty of Arts, University of Groningen, Groningen, The Netherlands.ORCID 0000-0002-6874-1126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social Desirability Bias (SDB), the tendency of respondents to present themselves in socially acceptable terms, poses serious challenges for the validity of survey research. This study systematically reviewed the effectiveness of methods aimed at reducing SDB. Searches in Scopus and PsycINFO of publications from 2017 to 2021 identified a total of 121 experiments in 79 peer-reviewed papers. These experiments, conducted in over 20 Western countries, employed 13 SDB-reduction methods across more than 17 behavioral or cognitive topics. Based on 10 quality measures, the methodological quality of these experiments was typically high. There was considerable variability in the frequency of SDB-reduction methods being used and their effectiveness. The most common methods were list experiments, probability-based techniques (RRT/NRRT), face-saving strategies, survey mode, and proxy reporting. Overall, in 55% of experiments SDB was significantly reduced, with face-saving strategies demonstrating the highest efficacy. Future research should aim at replicating these findings in a wider range of (societally relevant) topics and more comprehensively testing promising less explored methods. This review highlights the importance of continuing to refine and test SDB reduction techniques to improve survey data quality. Supplementary Information: The online version contains supplementary material available at 10.1007/s11135-026-02664-7.

Indexed as

Face-savingList experimentRRT/NRRTSocial Desirability Bias (SDB)Survey methodologySystematic review

Identifiers

PMID42245766
PMCPMC13230293

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.