Web Survey Bibliography
Title Careless Response and Attrition as Sources of Bias in Online Survey Assessments of Personality Traits and Performance
Source Computers in Human Behaviour; 76, pp. 417-430
Year 2017
Database ScienceDirect
Access date 20.07.2017
Abstract The online survey is critical to organizations to efficiently collect large amounts of data about variables like performance. However, this data collection method comes with challenges. Careless responding (CR) and attrition in online surveys introduce measurement error, and can lead to several psychometric issues. Despite the common co-occurrence of CR and attrition, previous studies have looked at CR and attrition separately, which may miss their additive impact in online survey measurement. Additionally, research has focused on CR and attrition without attending to their relationships with other variables. The aims of our studies are to build the nomological network around CR and attrition, and to determine the effects of the co-occurrence of CR and attrition. We investigated (a) the extent to which personality traits relate to both CR and attrition, and (b) the extent to which CR and attrition affect estimates of the relationships between personality traits and performance. We found a relatively high base rate of carelessness (23%) and mid-study attrition (11%). Conscientiousness, extraversion, neuroticism, and agreeableness were related to survey attrition and carelessness resulting in significant differences between respondents who carefully completed the survey and those lost via attrition or careless response screening. A simulation study provided estimates of the extent of bias resulting from: 1) various amounts of attrition and carelessness, and 2) correlations between attrition and carelessness with personality traits. Although the magnitude of bias is modest in most cases, there was substantial bias (.15) in correlation estimates in some situations when screening for CR and attrition. Based on findings from the current studies and extant literature, we suggest ways researchers and practitioners can address CR and attrition to improve the accuracy of the measured relationships between variables like personality and performance, and enhance the defensibility of conclusions.
Access/Direct link Journal Homepage (abstract) / (full text)
Year of publication2017
Bibliographic typeJournal article
Web survey bibliography - Measurement (1822)
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- A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample; 2017; Kott, P. S., Farrelly, M., Kamyab, K.
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- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
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- Device Effects - How different screen sizes affect answers in online surveys; 2016; Fisher, B.; Bernet, F.
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- The use of online social networks as a promotional tool for self-administered internet surveys; 2016; de Rada, V. D.; Arino, L. V. C; Blasco, M. G
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- Examining Trends in the Presence of Survey Mode Effects ; 2016; Hisako Kitada, H.; Lesser, V. M.
- Using a Response Propensity Model to Allocate Non-c ontingent Incentives in a Web Panel ; 2016; Masterton, M.
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- Non-Observation Bias in an Address-Register-Based CATI/CAPI Mixed Mode Survey; 2016; Lipps, O.
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- Detecting careless respondents in web-based questionnaires: Which method to use?; 2016; Niesen, A. S. M.; Meijer, R. R.; Tendeiro, J. N.
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- The impact of visual design and response formats on data quality in a web survey of MOOC students; 2016; Maloshonok, N.; Terentev, E.
- An experiment comparing grids and item-by-item formats in web surveys completed through PCs and smartphones...; 2016; Revilla, M.; Toninelli, D.; Ochoa, C.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Eye-tracking Social Desirability Bias; 2016; Kaminska, O.; Foulsham, T.
- Evaluating Three Approaches to Statistically Adjust for Mode Effects; 2016; Kolenikov, S.; Kennedy, C.
- Distractions: The Incidence and Consequences of Interruptions for Survey Respondents ; 2016; Ansolabehere, S.; Schaffner, B. F.