The Survey Data Quality Evaluation Model: A Framework for Improving Online Data Quality in Psychological Science

Introduction Online survey research promises easier access to human subjects. Despite concerns about online data quality, there has been minimal guidance aimed toward novice researchers. Statement of the Problem The uptake of data evaluation practices has not kept pace with the expansion of online survey administration. Increased exposure to and practice with data quality evaluation can help improve the quality and rigor of research at all levels. Literature Review We review the literature on the prevalence and implications of low-quality data in online survey administration. Although low-quality data negatively impact findings, audits indicate that approximately half of published articles involving online survey administration do not describe any data quality evaluation procedures. Teaching Implications In an effort to provide a comprehensive set of guidance and increase researcher buy-in to data quality, this paper describes the Survey Data Quality Evaluation Model (SDQEM), a set of procedures developed to help researchers identify and remove low-quality cases. We illustrate how researchers can use the SDQEM and how retaining low-quality data impacts results. Conclusion The SDQEM is intended to increase novice researchers’ use of data cleaning procedures when relying on online samples. The SDQEM is a necessary and accessible resource, especially for junior scholars.