Revealing the joint mechanisms in traditional data linked with big data

Research output: Contribution to journalArticleScientificpeer-review

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

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Depression
Psychological
Human Behavior
Surveys and Questionnaires
Data Integration
Genetic Risk
Questionnaire
Gene

Keywords

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

Cite this

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title = "Revealing the joint mechanisms in traditional data linked with big data",
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",
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author = "{de Schipper}, Niek and {Van Deun}, Katrijn",
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}

Revealing the joint mechanisms in traditional data linked with big data. / de Schipper, Niek; Van Deun, Katrijn.

In: Zeitschrift für Psychologie, Vol. 226, No. 4, 2019, p. 212-231.

Research output: Contribution to journalArticleScientificpeer-review

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AB - 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

KW - COMPONENTS

KW - MODEL SELECTION

KW - REGRESSION

KW - REGULARIZATION

KW - VARIABLE SELECTION

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KW - linked data

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