Best practice recommendations for data screening

Abstract

Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has the potential to identify different types of insufficient effort. An example dataset is provided to illustrate these differences and familiarize readers with the computation and implementation of the screening techniques. Researchers are encouraged to consider data screening when designing a survey, select screening techniques on the basis of theoretical considerations (or empirical considerations when pilot testing is an option), and report the results of an analysis both before and after employing data screening techniques. Copyright © 2014 John Wiley & Sons, Ltd.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 681 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.