Revealing the joint mechanisms in traditional data linked with big data

Niek de Schipper*, Katrijn Van Deun

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example, linking psychological questionnaire data with genetic risk scores. Revealing the variables that are linked throughout these traditional and novel types of data gives crucial insight into the complex interplay between the multiple factors that determine human behavior, for example, the concerted action of genes and environment in the emergence of depression. Little or no theory is available on the link between such traditional and novel types of data, the latter usually consisting of a huge number of variables. The challenge is to select – in an automated way – those variables that are linked throughout the different blocks, and this eludes currently available methods for data analysis. To fill the methodological gap, we here present a novel data integration method
Original languageEnglish
Pages (from-to)212-231
JournalZeitschrift für Psychologie
Volume226
Issue number4
DOIs
Publication statusPublished - 2019

Keywords

  • COMPONENTS
  • MODEL SELECTION
  • REGRESSION
  • REGULARIZATION
  • VARIABLE SELECTION
  • big data
  • component analysis
  • linked data
  • variable selection

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