Other

Supplementary materials for: Revealing subgroups that differ in common and distinctive variation in multi-block data: Clusterwise Sparse Simultaneous Component Analysis

Author(s) / Creator(s)

Yuan, Shuai
De Roover, Kim
Dufner, Michael
Denissen, Jaap
Van Deun, Katrijn

Abstract / Description

Social and behavioral studies more and more often yield multi-block data, which consist of novel blocks of data (e.g., data from wearable devices) and traditional blocks of data (e.g., survey data) collected from the same sample. Multi-block data offer researchers valuable insights into complex social mechanisms, where several influences act together. Yet such mechanisms are likely to differ among subgroups. Hence, fully revealing the composite mechanisms underlying multi-block data is challenging, since proper clustering analysis of such data requires methods that simultaneously detect the covariation of variables underlying all data blocks and the group differences therein. Additionally, the methods should be able to handle high-dimensional datasets, which might include many irrelevant variables. Here, we present Clusterwise Sparse Simultaneous Component Analysis (CSSCA), a method that groups the subjects that are driven by the same mechanisms and, at the same time, extracts cluster-specific components that model these mechanisms. By imposing structure constraints, CSSCA further distinguishes common mechanisms that underlie all data blocks from distinctive mechanisms that only underlie one or a few data blocks. In extensive simulations, CSSCA delivered convincing results in recovering the clusters and their associated component structures across various conditions. More importantly, CSSCA showed a clear advantage over existing methods when substantial cluster differences in the component structure were present. We demonstrated the usefulness of CSSCA in an application to data stemming from a study on personality.
Supplementary materials for: Yuan, S., De Roover, K., Dufner, M., Denissen, J. J. A., & Van Deun, K. (2019). Revealing Subgroups That Differ in Common and Distinctive Variation in Multi-Block Data: Clusterwise Sparse Simultaneous Component Analysis. Social Science Computer Review, 089443931988844. https://doi.org/10.1177/0894439319888449

Keyword(s)

clustering data integration high-dimensional data analysis

Persistent Identifier

Date of first publication

2019

Publisher

PsychArchives

Is referenced by

Citation

Yuan, S., De Roover, K., Dufner, M., Denissen, J., & Van Deun, K. (2019). Supplementary materials to: Revealing subgroups that differ in common and distinctive variation in multi-block data: Clusterwise Sparse Simultaneous Component Analysis. PsychArchives. https://doi.org/10.23668/psycharchives.2601
  • Author(s) / Creator(s)
    Yuan, Shuai
  • Author(s) / Creator(s)
    De Roover, Kim
  • Author(s) / Creator(s)
    Dufner, Michael
  • Author(s) / Creator(s)
    Denissen, Jaap
  • Author(s) / Creator(s)
    Van Deun, Katrijn
  • Accession date
    2019-09-26T08:54:48Z
  • Made available on
    2019-09-26T08:54:48Z
  • Date of first publication
    2019
  • Abstract / Description
    Social and behavioral studies more and more often yield multi-block data, which consist of novel blocks of data (e.g., data from wearable devices) and traditional blocks of data (e.g., survey data) collected from the same sample. Multi-block data offer researchers valuable insights into complex social mechanisms, where several influences act together. Yet such mechanisms are likely to differ among subgroups. Hence, fully revealing the composite mechanisms underlying multi-block data is challenging, since proper clustering analysis of such data requires methods that simultaneously detect the covariation of variables underlying all data blocks and the group differences therein. Additionally, the methods should be able to handle high-dimensional datasets, which might include many irrelevant variables. Here, we present Clusterwise Sparse Simultaneous Component Analysis (CSSCA), a method that groups the subjects that are driven by the same mechanisms and, at the same time, extracts cluster-specific components that model these mechanisms. By imposing structure constraints, CSSCA further distinguishes common mechanisms that underlie all data blocks from distinctive mechanisms that only underlie one or a few data blocks. In extensive simulations, CSSCA delivered convincing results in recovering the clusters and their associated component structures across various conditions. More importantly, CSSCA showed a clear advantage over existing methods when substantial cluster differences in the component structure were present. We demonstrated the usefulness of CSSCA in an application to data stemming from a study on personality.
    en
  • Abstract / Description
    Supplementary materials for: Yuan, S., De Roover, K., Dufner, M., Denissen, J. J. A., & Van Deun, K. (2019). Revealing Subgroups That Differ in Common and Distinctive Variation in Multi-Block Data: Clusterwise Sparse Simultaneous Component Analysis. Social Science Computer Review, 089443931988844. https://doi.org/10.1177/0894439319888449
    en
  • Publication status
    publishedVersion
    en
  • Publication status
    other
    en
  • Review status
    notReviewed
    en
  • Sponsorship
    This research was funded by a personal grant from The Netherlands Organization for Scientific Research [NWO-Research Talent 406.17.526] awarded to Shuai Yuan
    en
  • Table of contents
    Section 1 - Algorithms; Section 2 - Technical Minutiae of Algorithm 1; Section 3 - Data Generation Procedure
    en
  • Citation
    Yuan, S., De Roover, K., Dufner, M., Denissen, J., & Van Deun, K. (2019). Supplementary materials to: Revealing subgroups that differ in common and distinctive variation in multi-block data: Clusterwise Sparse Simultaneous Component Analysis. PsychArchives. https://doi.org/10.23668/psycharchives.2601
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/2223
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.2601
  • Language of content
    eng
    en
  • Publisher
    PsychArchives
    en
  • Is referenced by
    https://doi.org/10.1177/0894439319888449
  • Is related to
    https://doi.org/10.1177/0894439319888449
  • Keyword(s)
    clustering
    en
  • Keyword(s)
    data integration
    en
  • Keyword(s)
    high-dimensional data analysis
    en
  • Dewey Decimal Classification number(s)
    150
  • Title
    Supplementary materials for: Revealing subgroups that differ in common and distinctive variation in multi-block data: Clusterwise Sparse Simultaneous Component Analysis
    en
  • DRO type
    other
    en