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Post‐Stratification Weighting in Organizational Surveys: A Cross‐Disciplinary Tutorial
Author(s) -
Kulas John T.,
Robinson David H.,
Smith Jeffrey A.,
Kellar Donald Z.
Publication year - 2018
Publication title -
human resource management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.888
H-Index - 94
eISSN - 1099-050X
pISSN - 0090-4848
DOI - 10.1002/hrm.21796
Subject(s) - misrepresentation , weighting , polling , computer science , population , sample (material) , current population survey , survey data collection , operations research , statistics , sociology , political science , engineering , mathematics , law , medicine , chemistry , demography , chromatography , radiology , operating system
Post‐stratification weighting is a technique used in public opinion polling to minimize discrepancies between population parameters and realized sample characteristics. The current paper provides a weighting tutorial to organizational surveyors who may otherwise be unfamiliar with the rationale behind the practice as well as “when and how to do” such weighting. The primary reasons to weight include: [1] reducing the effect of frame, sampling, and nonresponse bias in point estimates, and, relatedly, (2) correcting for aggregation error resulting from over‐ and underrepresentation of constituent groups. We briefly compare and contrast traditions within public opinion and organizational polling contexts and present a hybrid taxonomy of sampling procedures that organizational surveyors may find useful in situating their survey efforts within a methodological framework. Next, we extend the existing HRM literature focused on survey nonresponse to a broader lens concerned with population misrepresentation. It is from this broadened methodological framework that we introduce the practice of weighting as a remedial strategy for misrepresentation. We then provide sample weighting algorithms and standard error corrections that can be applied to organizational survey data and make our data and procedures available to individuals who may wish to use our examples as they learn “how to weight.” © 2018 Wiley Periodicals, Inc.