TY - JOUR
T1 - Planned Missing Data Designs for Research in Cognitive Development
AU - Rhemtulla, Mijke
AU - Little, Todd D.
N1 - Funding Information:
This work was supported by a Banting postdoctoral fellowship from the Social Sciences and Humanities Research Council of Canada to M. Rhemtulla and a National Science Foundation grant (NSF0066969; T. D. Little & W. Wu, co-principal investigators).
PY - 2012/11
Y1 - 2012/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84866462331&partnerID=8YFLogxK
U2 - 10.1080/15248372.2012.717340
DO - 10.1080/15248372.2012.717340
M3 - Article
AN - SCOPUS:84866462331
VL - 13
SP - 425
EP - 438
JO - Journal of Cognition and Development
JF - Journal of Cognition and Development
SN - 1524-8372
IS - 4
ER -