Publication

Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis

De Roover, K., Ceulemans, E., Timmerman, M. E., Nezlek, J. B. & Onghena, P., Oct-2013, In : Psychometrika. 78, 4, p. 648-668 21 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

De Roover, K., Ceulemans, E., Timmerman, M. E., Nezlek, J. B., & Onghena, P. (2013). Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis. Psychometrika, 78(4), 648-668. https://doi.org/10.1007/s11336-013-9318-4

Author

De Roover, Kim ; Ceulemans, Eva ; Timmerman, Marieke E. ; Nezlek, John B. ; Onghena, Patrick. / Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis. In: Psychometrika. 2013 ; Vol. 78, No. 4. pp. 648-668.

Harvard

De Roover, K, Ceulemans, E, Timmerman, ME, Nezlek, JB & Onghena, P 2013, 'Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis', Psychometrika, vol. 78, no. 4, pp. 648-668. https://doi.org/10.1007/s11336-013-9318-4

Standard

Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis. / De Roover, Kim; Ceulemans, Eva; Timmerman, Marieke E.; Nezlek, John B.; Onghena, Patrick.

In: Psychometrika, Vol. 78, No. 4, 10.2013, p. 648-668.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

De Roover K, Ceulemans E, Timmerman ME, Nezlek JB, Onghena P. Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis. Psychometrika. 2013 Oct;78(4):648-668. https://doi.org/10.1007/s11336-013-9318-4


BibTeX

@article{f6436a1ca7b44fb3ade614894900abeb,
title = "Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis",
abstract = "Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.",
keywords = "multigroup data, multilevel data, principal component analysis, simultaneous component analysis, clustering, dimensionality., PRIVATE SELF-CONSCIOUSNESS, LOCAL OPTIMA, BINARY DATA, PERSONALITY, SELECTION, EMOTIONS, ROTATION, RECOVERY, NUMBER",
author = "{De Roover}, Kim and Eva Ceulemans and Timmerman, {Marieke E.} and Nezlek, {John B.} and Patrick Onghena",
year = "2013",
month = "10",
doi = "10.1007/s11336-013-9318-4",
language = "English",
volume = "78",
pages = "648--668",
journal = "Psychometrika",
issn = "0033-3123",
publisher = "SPRINGER",
number = "4",

}

RIS

TY - JOUR

T1 - Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis

AU - De Roover, Kim

AU - Ceulemans, Eva

AU - Timmerman, Marieke E.

AU - Nezlek, John B.

AU - Onghena, Patrick

PY - 2013/10

Y1 - 2013/10

N2 - Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.

AB - Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.

KW - multigroup data

KW - multilevel data

KW - principal component analysis

KW - simultaneous component analysis

KW - clustering

KW - dimensionality.

KW - PRIVATE SELF-CONSCIOUSNESS

KW - LOCAL OPTIMA

KW - BINARY DATA

KW - PERSONALITY

KW - SELECTION

KW - EMOTIONS

KW - ROTATION

KW - RECOVERY

KW - NUMBER

U2 - 10.1007/s11336-013-9318-4

DO - 10.1007/s11336-013-9318-4

M3 - Article

C2 - 24092482

VL - 78

SP - 648

EP - 668

JO - Psychometrika

JF - Psychometrika

SN - 0033-3123

IS - 4

ER -

ID: 5967027