Missing Data Techniques and Low Response Rates: The Role of Systematic Nonresponse Parameters

This chapter attempts to debunk two popular misconceptions (or legends) about missing data: Legend #1, low response rates will necessarily invalidate study results; and Legend #2, listwise and pairwise deletion are adequate default techniques, compared with state-of-theart (maximum likelihood) missing data techniques. After reviewing general missingness mechanisms (i.e., MCAR, MAR, MNAR), the relevance of response rates and missing data techniques is shown to depend critically on the magnitude of two systematic nonresponse parameters (or SNPs: labeled dmiss and fmiss2) . Response rates impact external validity only when these SNPs are large. Listwise and pairwise deletions are appropriate only when these SNPs are very small. I emphasize (a) the need to explicitly identify and empirically estimate SNPs, (b) the connection of SNPs to the theoretical model (and specific constructs) being studied, (c) the use of SNPs in sensitivity analysis to determine bias due to response rates, and (d) the use of SNPs to establish inferiority of listwise and pairwise deletion to maximum likelihood and multiple imputation approaches. Finally, key applications of missing data techniques are discussed, including longitudinal modeling, within-group agreement estimation, meta-analytic corrections, social network analysis, and moderated regression.