Systematic Review on the Effectiveness of Social Desirability Bias Reduction Methods in Survey Research

DOI

The dataset was used to inform a systematic review. This systematic review provides an overview and explanation of recently investigated reduction methods of social desirability bias and highlights those that synthesis show to be most effective. We included in our review/dataset only experimental studies (i.e., correlational studies are excluded). This review outlines experiment’s characteristics (e.g., behavioral/cognitive topics, sample information, example operationalizations), evaluates experimental quality, and provides future directives for SDB reduction research. The dataset contains Details Citation, Topic(s) of self-report, Theoretical framework, objective and expectations, Sample, Methods and outcomes. More specifically, Authors, title, publication year, journal of publication, doi., General topic (e.g., health) and specific topic (e.g., alcohol consumption), Method/theoretical framework used in the study; objective/goal of study. N participants; country; age; gender; sampling frame (i.e., from which population was the sample drawn); sample recruitment (e.g., probability/non-probability); incentives given (Yes/No/Unclear/Mixed), Design (number of groups/comparisons); mode of administration/study context (e.g., self-administered online survey, laboratory setting); main independent/dependent variables & operationalization; covariates, Statistical analyses carried out; primary outcomes; effect sizes mentioned (Yes/No); Primary outcome (e.g., experimental condition(s) reduced, increased or had no effect on SDB); implications for questionnaire design (i.e., method supported, mixed/unclear, unsupported).

Identifier
DOI https://doi.org/10.17026/SS/G8JC8E
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=e3bcce61f23263f278365be0340c8902ac467a9c49c8f5175d603706e928fe69
Provenance
Creator E. Zaal
Publisher DANS Data Station Social Sciences and Humanities
Publication Year 2026
OpenAccess true
Representation
Resource Type Data abstraction of previously published papers
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Life Sciences; Psychology; Social Sciences; Social and Behavioural Sciences; Soil Sciences