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A clusterwise simultaneous component method for capturing within-cluster differences in component variances and correlations

  1. Kim De Roover1,*,
  2. Eva Ceulemans1,
  3. Marieke E. Timmerman2,
  4. Patrick Onghena1

Article first published online: 7 FEB 2012

DOI: 10.1111/j.2044-8317.2012.02040.x

Issue

British Journal of Mathematical and Statistical Psychology

British Journal of Mathematical and Statistical Psychology

Volume 66, Issue 1, pages 81–102, February 2013

Additional Information

How to Cite

De Roover, K., Ceulemans, E., Timmerman, M. E. and Onghena, P. (2013), A clusterwise simultaneous component method for capturing within-cluster differences in component variances and correlations. British Journal of Mathematical and Statistical Psychology, 66: 81–102. doi: 10.1111/j.2044-8317.2012.02040.x

Author Information

  1. 1

    Katholieke Universiteit Leuven, Belgium

  2. 2

    University of Groningen, The Netherlands

* Correspondence should be addressed to Kim De Roover, Department of Educational Sciences, Katholieke Universiteit Leuven, Andreas Vesaliusstraat 2, B-3000 Leuven, Belgium (e-mail: Kim.DeRoover@ppw.kuleuven.be).

Publication History

  1. Issue published online: 17 JAN 2013
  2. Article first published online: 7 FEB 2012
  3. Received 03 February 2011; revised version received 28 October 2011

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References

  • Bock, H. H. (1987). On the interface between cluster analysis, principal component analysis, and multidimensional scaling. In H. Bozdogan & A. K. Gupta (Eds.), Multivariate statistical modeling and data analysis (pp. 17–34). Dordrecht : Reidel.
  • Brusco, M. J., & Cradit, J. D. (2001). A variable selection heuristic for K-means clustering. Psychometrika , 66, 249–270.
  • Ceulemans, E., & Kiers, H. A. L. (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method. British Journal of Mathematical and Statistical Psychology , 59, 133–150.
    Direct Link:
  • De Roover, K., Ceulemans, E., Timmerman, M. E. (2011). How to perform multiblock component analysis in practice. Behavior Research Methods . doi:10.3758/s13428-011-0129-1
  • De Roover, K., Ceulemans, E., Timmerman, M. E., Vansteelandt, K., Stouten, J., & Onghena, P. (2011). Clusterwise SCA-ECP for analyzing structural differences in multivariate multiblock data. Psychological Methods . doi:10.1037/a0025385
  • de Soete, G., & Carrol, J. D. (1994). K-means clustering in a low-dimensional Euclidean space. In E. Diday, Y. Léchevallier, M. Schader, P. Bertrand, & B. Burtschy (Eds.) New approaches in classification and data analysis (pp. 212–219). Berlin : Springer.
  • Diaz-Loving, R. (1998). Contributions of Mexican ethnopsychology to the resolution of the etic-emic dilemma in personality. Journal of Cross-Cultural Psychology , 29, 104–118.
  • Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009). Testing measurement invariance in the target rotated multigroup exploratory factor model. Structural Equation Modeling , 16, 295–314.
  • Goldberg, L. R. (1990). An alternative ‘description of personality’: The Big-Five factor structure. Journal of Personality and Social Psychology , 59, 1216–1229.
  • Haggard, E. A. (1958). Intraclass correlation and the analysis of variance . New York : Dryden.
  • Harris, C. W., & Kaiser, H. F. (1964). Oblique factor analytic solutions by orthogonal transformations. Psychometrika , 29, 347–362.
  • Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification , 2, 193–218.
  • Jolliffe, I. T. (1986). Principal component analysis. New York : Springer.
  • Kaiser, H. F. (1958). The Varimax criterion for analytic rotation in factor analysis. Psychometrika , 23, 187–200.
  • Kendell, R., & Jablensky, A. (2003). Distinguishing between the validity and utility of psychiatric diagnosis. American Journal of Psychiatry , 160, 4–12.
  • Kendler, K. S. (1990). Toward a scientific psychiatric nosology: Strengths and limitations. Archives of General Psychiatry , 47, 969–973.
  • Kiers, H. A. L. (1990). SCA. A program for simultaneous components analysis of variables measured in two or more populations . Groningen : iec ProGAMMA.
  • Kiers, H. A. L., & ten Berge, J. M. F. (1994a). Hierarchical relations between methods for simultaneous components analysis and a technique for rotation to a simple simultaneous structure. British Journal of Mathematical and Statistical Psychology , 47, 109–126.
    Direct Link:
  • Kiers, H. A. L., & ten Berge, J. M. F. (1994b). The Harris-Kaiser independent cluster rotation as a method for rotation to simple component weights. Psychometrika , 59, 81–90.
  • Kirk, R. E. (1995). Experimental design: Procedures for the behavioral sciences (3rd ed.). Pacific Grove , CA : Brooks/Cole.
  • Kroonenberg, P. M. (2008). Applied multiway data analysis . Hoboken , NJ : Wiley.
  • Lawley, D. N., & Maxwell, A. E. (1962). Factor analysis as a statistical method. The Statistician , 12, 209–229.
  • McCrae, R. R., & Costa, P. T., Jr. (1997). Personality trait structure as a human universal. American Psychologist , 52, 509–516.
  • McLachlan, G. J., & Peel, D. (2000). Finite mixture models . New York : Wiley.
  • Mezzich, J. E., & Solomon, H. (1980). Taxonomy and behavioral science: Comparative performance of grouping methods . London : Academic Press.
  • Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine , 2, 559–572.
  • Skrondal, A. (2000). Design and analysis of Monte Carlo experiments: Attacking the conventional wisdom. Multivariate Behavioral Research , 35, 137–167.
  • Steinley, D. (2003). Local optima in K-means clustering: What you don't know may hurt you. Psychological Methods , 8, 294–304.
  • Timmerman, M. E. (2006). Multilevel component analysis. British Journal of Mathematical and Statistical Psychology , 59, 301–320.
    Direct Link:
  • Timmerman, M. E., Ceulemans, E., Kiers, H. A. L., & Vichi, M. (2010). Factorial and reduced K-means reconsidered. Computational Statistics & Data Analysis , 54, 1858–1871.
  • Timmerman, M. E., & Kiers, H. A. L. (2003). Four simultaneous component models of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika , 86, 105–122.
  • Tucker, L. R. (1951). A method for synthesis of factor analysis studies (Personnel Research Section Rep. No. 984). Washington , DC : Department of the Army.
  • Van Deun, K., Smilde, A. K., van der Werf, M. J., Kiers, H. A. L., & Van Mechelen, I. (2009). A structured overview of simultaneous component based data integration. BMC Bioinformatics , 10, 246.
  • Zachar, P., & Kendler, K. S. (2007). Psychiatric disorders: A conceptual taxonomy. American Journal of Psychiatry , 164, 557–565.
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