Data collection can be the most time- and cost-intensive part of developmental research. This article describes some long-proposed but little-used research designs that have the potential to maximize data quality (reliability and validity) while minimizing research cost. In planned missing data designs, missing data are used strategically to improve the validity of data collection in one of two ways. Multiform designs allow one to increase the number of measures assessed on each participant without increasing each participant's burden. Two-method measurement designs allow one to reap the benefits of a cost-intensive gold-standard measure, using a larger sample size made possible by a rougher, cheaper measure. We explain each method using examples relevant to cognitive development research. With the use of analysis methods that produce unbiased results, planned missing data designs are an efficient way to manage cost, improve data quality, and reduce participant fatigue and practice effects.

中文翻译：

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用于认知发展研究的计划缺失数据设计

数据收集可能是发展研究中最耗费时间和成本的部分。本文描述了一些长期提出但很少使用的研究设计，它们有可能最大限度地提高数据质量（可靠性和有效性），同时最大限度地降低研究成本。在计划的缺失数据设计中，缺失数据被战略性地用于以两种方式之一提高数据收集的有效性。多形式设计允许在不增加每个参与者的负担的情况下增加对每个参与者评估的措施数量。两种方法的测量设计允许人们获得成本密集型黄金标准测量的好处，使用更大的样本量，通过更粗略、更便宜的测量成为可能。我们使用与认知发展研究相关的例子来解释每种方法。