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The use of snowball sampling for multi source organizational research: Some cause for concern
Author(s) -
Marcus Bernd,
Weigelt Oliver,
Hergert Jane,
Gurt Jochen,
Gelléri Petra
Publication year - 2016
Publication title -
personnel psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.076
H-Index - 142
eISSN - 1744-6570
pISSN - 0031-5826
DOI - 10.1111/peps.12169
Subject(s) - snowball sampling , psychology , sample (material) , sampling (signal processing) , applied psychology , social psychology , quality (philosophy) , variance (accounting) , inclusion (mineral) , statistics , computer science , philosophy , chemistry , mathematics , accounting , filter (signal processing) , chromatography , epistemology , business , computer vision
In snowball sampling for multisource studies, researchers ask target participants to recruit informants. Despite its widespread use, especially for recruiting informants for multisource research, virtually no published research has addressed possible biases snowball sampling may cause in findings of this type of research. Such potential biases were tested empirically in a multisource study with a sample of 1,058 employed students asked to collect online ratings from their supervisors and coworkers. Informant ratings were obtained for 358 target participants. Objective indicators were employed to identify informant ratings suspicious of being fabricated. Results indicated that target participants who report (a) better relationship quality with informants, (b) fewer organizational constraints, and (c) more favorable self‐evaluations on behaviors to be rated by informants were more likely to be included in multisource data unsuspicious of being fabricated. Inclusion of informant ratings suspicious of being fabricated led to inflated estimates of self‐other consensus and of variance accounted for in criterion measures, to deflated informant rating means, but also to a target sample less restricted in terms of relevant organizational variables. In sum, the present findings suggest that potential biases should be identified in future uses of snowball sampling. Some practical recommendations toward that end are derived.