Rhemtulla Mijke, Little Todd
Center for Research Methods and Data Analysis, University of Kansas.
J Cogn Dev. 2012;13(4). doi: 10.1080/15248372.2012.717340.
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 , missing data are used strategically to improve the validity of data collection in one of two ways. Multi-form 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.
数据收集可能是发展性研究中最耗时且成本最高的部分。本文介绍了一些长期以来被提出但很少使用的研究设计,这些设计有可能在将研究成本降至最低的同时,最大限度地提高数据质量(可靠性和有效性)。在[具体内容缺失]中,缺失数据被策略性地用于通过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负担的情况下增加对每个参与者评估的测量数量。双方法测量设计允许利用成本高昂的金标准测量的优势,通过更粗略、更便宜的测量获得更大的样本量。我们使用与认知发展研究相关的例子来解释每种方法。通过使用产生无偏结果的分析方法,有计划的缺失数据设计是管理成本、提高数据质量以及减少参与者疲劳和练习效应的有效方法。