This article explores considerations that emerge when using a planned missing data design (PMDD). It describes scenarios where a PMDD can be useful and reveals implications of a PMDD for questionnaire design in terms of the optimal number of questionnaire versions. It takes a confirmatory factor model to explore the PMDD performance. Findings suggest that increasing the number of versions to maximize uniformity of missing data yields no advantage over minimizing data collection cost and complexity. The paper makes recommendations concerning different missing data patterns that can be used. And finally, this article points out how certain fit statistics could be misleading with PMDD. The one fit statistics that consistently performed well with the PMDD is the SRMR.