2016/01/19 by Brad R. Fulton · 104 citations
Business, Management and Accounting · Economics, Econometrics and Finance · Mathematics · Psychology · Social Sciences · #Business #Customer Service Quality and Loyalty #Data collection #Data quality #Econometrics #Economic and Environmental Valuation #Economics #Marketing #Mathematics #Missing data #Non-response bias #Psychology #Representativeness heuristic #Response bias #Social psychology #Statistics #Survey Methodology and Nonresponse #Survey data collection #Survey methodology #Variance (accounting)
paper · doi:10.1177/0049124115626169
published in Sociological Methods & Research 47(2), 240-276 (SAGE Publishing)
openalex publication_date 2016/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25
Surveys provide a critical source of data for scholars, yet declining response rates are threatening the quality of data being collected. This threat is particularly acute among organizational studies that use key informants—the mean response rate for published studies is 34 percent. This article describes several response enhancing strategies and explains how they were implemented in a national study of organizations that achieved a 94 percent response rate. Data from this study are used to examine the relationship between survey response patterns and nonresponse bias by conducting nonresponse analyses on several important individual and organizational characteristics. The analyses indicate that nonresponse bias is associated with the mean/proportion and variance of these variables and their correlations with relevant organizational outcomes. After identifying the variables most susceptible to nonresponse bias, a final analysis calculates the minimum response rate those variables needed to ensure that they do not contain significant nonresponse bias. Heuristic versions of these analyses can be used by survey researchers during data collection (and by scholars retrospectively) to assess the representativeness of respondents and the degree of nonresponse bias variables contain. This study has implications for survey researchers, scholars who analyze survey data, and those who review their research.