Demographic Comparisons for Various Types of Low-Cognitive-Effort Survey Responses

• In Event: Data Consideration in Survey Research

Abstract

The quality of self-reported data is a fundamental concern in survey research. As surveys become more popular in the field of education, it is crucial we investigate the quality of the data collected. The purpose of this paper is to inform researchers how intentions of quota sampling may be affected depending on the relationships of several demographic subgroups with various low cognitive effort (LCE) indicators. Chi-square analyses examined relationships between gender, age, race, education and income variables with five LCE indicators (speeding, straight-lining, semantic synonyms, personal reliability, and expected order of responses). Results suggest that it may be necessary to oversample certain subgroups (e.g., 30- to 44-year-olds) due to certain subgroups exhibiting more LCE (e.g., speeding) than others.

Authors

• Jennifer Reimers, University of Arkansas at Fayetteville

• Ronna C. Turner, University of Arkansas

• Wen-Juo Lo, University of Arkansas

• Kristen Jozkowski, Indiana University

• Brandon Lee Crawford, University of Arkansas