Henk A.L. Kiers
If you need one or more papers, just send me an email, preferably indicating the number(s) of the publication(s)
Professor in Methods for Data Analysis
Department of Psychology
University of Groningen
Grote Kruisstraat 2/1
9712 TS Groningen
Telephone: +31 (0)50 363 6193
- Currently no regular teaching tasks
- International (ad hoc) courses on Optimization of matrix functions and Multiway Data Analysis
International memberships and editorial work
- Past President of the International Federation of Classification Societies (IFCS)
- Member of the International Statistical Institute (ISI)
- Associate editor of Psychometrika
- Member of editorial board of Journal of Classification
- Kiers, H.A.L., & van Mechelen, I. (2001).Three-way component analysis: Principles and illustrative application.
Psychological Methods, 6 , 84-110.
- Kiers, H.A.L. (2002) Setting up Alternating Least Squares and Iterative Majorization Algorithms for Solving Various Matrix Optimization Problems,
Computational Statistics and Data Analysis,41,157-170.
- Kiers, H.A.L. (2004) Bootstrap Confidence Intervals for Three-way Methods.Journal of Chemometrics,18,22-36.
(view powerpoint slides)
- Sigrist, M., Keller.C., & Kiers, H.A.L. (2005) A new look at the psychometric paradigm of perception of hazards. Risk Analysis, 25, 211-222.
- Kiers, H.A.L., Vicari, D., & Vichi, M. (2005) Simultaneous Classification and Multidimensional Scaling with External Information. Psychometrika, 70, 433-460.
- Kiers, H.A.L. (2006) Properties of and algorithms for fitting three-way component models with offset terms, Psychometrika, 71, 231-257. (download Addendum to this article)
- Kiers, H.A.L., & Smilde, A.K. (2007) A comparison of various methods for Multivariate Regression with highly collinear variables. Statistical Methods and Applications, 16, 193-228.
- Smilde, A.K. Kiers, H.A.L., Bijlsma, S., Rubingh, C.M., & van Erk, M.J. (2009). Matrix correlations for high-dimensional data: the modified RV-coefficient. Bioinformatics, 25, 401–405.
- Stuive, I., Kiers, H.A.L., Timmerman, M.E., & Ten Berge, J.M.F. (2009) Comparison of Methods for adjusting incorrect assignments of items to subtests: Oblique Multiple Group method versus Confirmatory Common Factor method. Educational and Psychological Measurement, 69, 948-965.
- Ceulemans, E., Timmerman, M.E., & Kiers, H.A.L. (2011) The CHULL procedure for selecting among multilevel component solutions, Chemometrics and Intelligent Laboratory Systems, 106, 12-20.
- Timmerman, M.E., Kiers, H.A.L., and Ceulemans, E. (2016) Searching components with simple structure in simultaneous component analysis: Blockwise Simplimax rotation. Chemometrics and Intelligent Laboratory Systems, xx<\i>, xxx-xxx.
Downloadable Book: Kiers, H.A.L. (1989) Three-way methods for the analysis of qualitative and quantitative
two-way data, Leiden: DSWO Press.
Other Downloadable Books:
Ten Berge, J.M.F. (1993, 2005) Least Squares Optimization in Multivariate Analysis.
Zegers, F.E. (1986) A General Family of Association Coefficients.
Research Report on methods for comparisons of Means
Report on common methods for comparison of Means (e.g., two-groups t-procedure, ANOVA):
Comparing the Student's t and the ANOVA contrast procedure with five alternatives.
pdf version full paper
Master thesis by Wobbe Zijlstra, University of Groningen.
(Abstract: pdf version of abstract)
(documentation in Dutch)
Program for SIMPLIMAX Oblique simple structure rotation
This program can be downloaded here: file: Simplimax.exe (use right mouse click, and 'save target as' command).
A description of the program can be found here: Simplimax.doc.
The program is a PASCAL based executable program (written for MS-DOS)
Main reference: Kiers, H.A.L. (1994). SIMPLIMAX: Oblique rotation to an optimal target with simple structure.
Psychometrika, 59, 567-579.
Program Files for Greatest Lower Bound and MRFA: Minimum Rank Factor Analysis
This program is a PASCAL based executable program (written for MS-DOS).
For Minimum Rank Factor Analysis as well as computation of the greatest lower bound, a windows based program, FACTOR, is available at
If for some reason one still wishes to use the old mrfa program, it is available here: file: MRFA2.exe (use right mouse click, and 'save target as' command).
A description of the method mrfa can be found here: mrfa.doc.
An example data file can be found here: lord.dat.
Matlab program files for several applications
Three-mode component analysis (Tucker3): Threeway m-files.zip (use right mouse click, and 'save target as' command)
Use: unzip zipped file; run "tucker3.m"
Main reference: Kiers, H.A.L., & van Mechelen, I. (2001).Three-way component analysis: Principles and illustrative application.
Psychological Methods, 6 , 84-110.
INDCLUS (Individual Differences Cluster Analysis: indclus.zip (use right mouse click, and 'save target as' command)
Use: unzip zipped file; run "indclus.m"; documention in Word file indclus.doc
Main reference: Kiers, H.A.L. (1997). A modification of the SINDCLUS algorithm for fitting the
ADCLUS and INDCLUS models. Journal of Classification, 14, 297-310.
Multi Trait Multi Method Componenent Analysis : mtmm-ca.zip (use right mouse click, and 'save target as' command)
Use: unzip zipped file; run "mtmm.m"; program is interactive; description of method can be found in article, also in zip-file
Main reference: Kiers, H.A.L., Takane, Y., & Ten Berge, J.M.F. (1996)
The analysis of Multitrait-Multimethod matrices via constrained components analysis. Psychometrika, 61, 601-628.
Orthogonal Congruence Rotation: Orthog Congr Rotation.zip (use right mouse click, and 'save target as' command)
Use: unzip zipped file; run "OrthCongRot.m"; program is interactive; description of method can be found in article, also in zip-file
Main reference: Kiers, H.A.L., & Groenen, P. (1996) A monotonically convergent algorithm for orthogonal congruence rotation.Psychometrika, 61, 375-389.
Zero correlated constrained Parafac: Zero Correlated Parafac.zip (use right mouse click, and 'save target as' command)
Use: unzip zipped file; you will get three matlab script files containing parafac routines; cpfunc.m gives the ordinary Parafac algorithm, and has the zero correlations constraint as an option.
To see how to use this function, first type (in Matlab) "help cpfunc".
The m-files Parafac "UncorrColumns Alg 1'.m" and "Parafac UncorrColumns Alg 2'.m" use the two fast algorithms described in the article referred to below.
In addition you will find some auxiliary files needed to run these script files.
Main reference: Kiers,H.A.L. & Harshman, R.A. (resubmitted). An Efficient Algorithm for Parafac with Uncorrelated Mode-A Components Applied to Large IxJxK Data Sets with I>>JK.
Update: July 2016