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III. FROM SMALL TO BIG: METHODS FOR INCORPORATING LARGE SCALE DATA INTO DEVELOPMENTAL SCIENCE
Author(s) -
DavisKean Pamela E.,
Jager Justin
Publication year - 2017
Publication title -
monographs of the society for research in child development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.618
H-Index - 63
eISSN - 1540-5834
pISSN - 0037-976X
DOI - 10.1111/mono.12297
Subject(s) - developmental science , psychology , scale (ratio) , dominance (genetics) , data collection , data science , diversity (politics) , developmental psychology , computer science , sociology , social science , geography , biochemistry , chemistry , cartography , anthropology , gene
For decades, developmental science has been based primarily on relatively small‐scale data collections with children and families. Part of the reason for the dominance of this type of data collection is the complexity of collecting cognitive and social data on infants and small children. These small data sets are limited in both power to detect differences and the demographic diversity to generalize clearly and broadly. Thus, in this chapter we will discuss the value of using existing large‐scale data sets to tests the complex questions of child development and how to develop future large‐scale data sets that are both representative and can answer the important questions of developmental scientists.