Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 212-231 |
| Journal | Zeitschrift für Psychologie |
| Volume | 226 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2019 |
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Keywords
- COMPONENTS
- MODEL SELECTION
- REGRESSION
- REGULARIZATION
- VARIABLE SELECTION
- big data
- component analysis
- linked data
- variable selection
Cite this
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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 journal › Article › Scientific › peer-review
TY - JOUR
T1 - Revealing the joint mechanisms in traditional data linked with big data
AU - de Schipper, Niek
AU - Van Deun, Katrijn
N1 - This research was funded by a personal grant from the Netherlands Organisation for Scientific Research [NWO-VIDI 452.16.012] awarded to Katrijn Van Deun. The funder did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2019
Y1 - 2019
N2 - 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
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
KW - big data
KW - component analysis
KW - linked data
KW - variable selection
U2 - 10.1027/2151-2604/a000341
DO - 10.1027/2151-2604/a000341
M3 - Article
VL - 226
SP - 212
EP - 231
JO - Zeitschrift für Psychologie
JF - Zeitschrift für Psychologie
SN - 2151-2604
IS - 4
ER -