Literature DB >> 28475252

III. FROM SMALL TO BIG: METHODS FOR INCORPORATING LARGE SCALE DATA INTO DEVELOPMENTAL SCIENCE.

Pamela E Davis-Kean, Justin Jager.   

Abstract

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.
© 2017 The Society for Research in Child Development, Inc.

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Year:  2017        PMID: 28475252     DOI: 10.1111/mono.12297

Source DB:  PubMed          Journal:  Monogr Soc Res Child Dev        ISSN: 0037-976X


  2 in total

Review 1.  Building theories of consistency and variability in children's language development: A large-scale data approach.

Authors:  Angeline Sin Mei Tsui; Virginia A Marchman; Michael C Frank
Journal:  Adv Child Dev Behav       Date:  2021-06-14

2.  Evaluation of a Longitudinal Family Stress Model in a Population-Based Cohort.

Authors:  Arianna M Gard; Vonnie C McLoyd; Colter Mitchell; Luke W Hyde
Journal:  Soc Dev       Date:  2020-03-13
  2 in total

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