SCA with rotation to distinguish common and distinctive information in linked data

Martijn Schouteden, K. Van Deun, Sven Pattyn, Iven Van Mechelen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Often data are collected that consist of different blocks that all contain information about the same entities (e.g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.
Keywords: Simultaneous component analysis, Multigroup factor analysis, Rotation, Common information, Distinctive information
Original languageEnglish
Pages (from-to)822-833
JournalBehavior Research Methods
Volume45
Issue number3
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Statistical Factor Analysis
Factor Analysis
Key Words
Emotion
Person
Entity
Cross-cultural Studies

Cite this

Schouteden, Martijn ; Van Deun, K. ; Pattyn, Sven ; Van Mechelen, Iven. / SCA with rotation to distinguish common and distinctive information in linked data. In: Behavior Research Methods. 2013 ; Vol. 45, No. 3. pp. 822-833.
@article{22367df5b3f444c5a2826ca4e31c3fc5,
title = "SCA with rotation to distinguish common and distinctive information in linked data",
abstract = "Often data are collected that consist of different blocks that all contain information about the same entities (e.g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.Keywords: Simultaneous component analysis, Multigroup factor analysis, Rotation, Common information, Distinctive information",
author = "Martijn Schouteden and {Van Deun}, K. and Sven Pattyn and {Van Mechelen}, Iven",
year = "2013",
doi = "10.3758/s13428-012-0295-9",
language = "English",
volume = "45",
pages = "822--833",
journal = "Behavior Research Methods",
issn = "1554-351X",
publisher = "Springer",
number = "3",

}

SCA with rotation to distinguish common and distinctive information in linked data. / Schouteden, Martijn; Van Deun, K.; Pattyn, Sven; Van Mechelen, Iven.

In: Behavior Research Methods, Vol. 45, No. 3, 2013, p. 822-833.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - SCA with rotation to distinguish common and distinctive information in linked data

AU - Schouteden, Martijn

AU - Van Deun, K.

AU - Pattyn, Sven

AU - Van Mechelen, Iven

PY - 2013

Y1 - 2013

N2 - Often data are collected that consist of different blocks that all contain information about the same entities (e.g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.Keywords: Simultaneous component analysis, Multigroup factor analysis, Rotation, Common information, Distinctive information

AB - Often data are collected that consist of different blocks that all contain information about the same entities (e.g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.Keywords: Simultaneous component analysis, Multigroup factor analysis, Rotation, Common information, Distinctive information

U2 - 10.3758/s13428-012-0295-9

DO - 10.3758/s13428-012-0295-9

M3 - Article

VL - 45

SP - 822

EP - 833

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-351X

IS - 3

ER -