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

(2013) BEHAVIOR RESEARCH METHODS. 45(3). p.822-833
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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
NUMBER, MODEL, DIMENSIONS, POPULATIONS, Multigroup factor analysis, AGE, COMPONENT ANALYSIS, CONSTRUCT-VALIDITY, ORTHOGONAL ROTATION, HIERARCHICAL RELATIONS, Rotation, Simultaneous component analysis, Common information, Distinctive information

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Citation

Please use this url to cite or link to this publication:

MLA
Schouteden, Martijn, Katrijn Van Deun, Sven Pattyn, et al. “SCA with Rotation to Distinguish Common and Distinctive Information in Linked Data.” BEHAVIOR RESEARCH METHODS 45.3 (2013): 822–833. Print.
APA
Schouteden, M., Van Deun, K., Pattyn, S., & Van Mechelen, I. (2013). SCA with rotation to distinguish common and distinctive information in linked data. BEHAVIOR RESEARCH METHODS, 45(3), 822–833.
Chicago author-date
Schouteden, Martijn, Katrijn Van Deun, Sven Pattyn, and Iven Van Mechelen. 2013. “SCA with Rotation to Distinguish Common and Distinctive Information in Linked Data.” Behavior Research Methods 45 (3): 822–833.
Chicago author-date (all authors)
Schouteden, Martijn, Katrijn Van Deun, Sven Pattyn, and Iven Van Mechelen. 2013. “SCA with Rotation to Distinguish Common and Distinctive Information in Linked Data.” Behavior Research Methods 45 (3): 822–833.
Vancouver
1.
Schouteden M, Van Deun K, Pattyn S, Van Mechelen I. SCA with rotation to distinguish common and distinctive information in linked data. BEHAVIOR RESEARCH METHODS. 2013;45(3):822–33.
IEEE
[1]
M. Schouteden, K. Van Deun, S. Pattyn, and I. Van Mechelen, “SCA with rotation to distinguish common and distinctive information in linked data,” BEHAVIOR RESEARCH METHODS, vol. 45, no. 3, pp. 822–833, 2013.
@article{5683161,
  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.},
  author       = {Schouteden, Martijn and Van Deun, Katrijn and Pattyn, Sven and Van Mechelen, Iven},
  issn         = {1554-351X},
  journal      = {BEHAVIOR RESEARCH METHODS},
  keywords     = {NUMBER,MODEL,DIMENSIONS,POPULATIONS,Multigroup factor analysis,AGE,COMPONENT ANALYSIS,CONSTRUCT-VALIDITY,ORTHOGONAL ROTATION,HIERARCHICAL RELATIONS,Rotation,Simultaneous component analysis,Common information,Distinctive information},
  language     = {eng},
  number       = {3},
  pages        = {822--833},
  title        = {SCA with rotation to distinguish common and distinctive information in linked data},
  url          = {http://dx.doi.org/10.3758/s13428-012-0295-9},
  volume       = {45},
  year         = {2013},
}

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