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Multivariate methods, fall 2010

Lecturer and instructors

Kimmo Vehkalahti
Maria Valaste
Tara Junes


6-10 cu.


Minor (6 cu) or Intermediate (8 cu) or Advanced (10 cu) studies in Statistics.

Extremely suitable for students of REMS as well as other students of Social Sciences, including Statistics.


for 6 cu:

  • basic concepts of statistics and probability (e.g. Introduction to Statistics and Second Course in Statistics)
  • basic skills of at least one suitable software, such as Survo, SPSS, R, or SAS

for 8-10 cu: as above, but also

  • basic concepts of matrix algebra and mathematical analysis
  • basic concepts of statistical inference and linear models


(info) Kurssilla käytetään joustavasti sekä suomea että englantia. (Both Finnish and English are used flexibly during the course.)


The objective of the course is to learn the basics of multivariate data analysis and multidimensional statistical modeling in practice. The focus will be mostly on applications in social sciences. In addition, some of the theory behind the methods will be covered to support the learning (8-10 cu).

At least the following multivariate methods will be considered:


Period II (weeks 44-50), in City Center Campus.


Completion of the course requires a few parts (not all of these):

  1. Exercises
    a shared workspace (BSCW) will be in heavy use
  2. Net poster
    for an idea, see old net posters (mostly in Finnish)
  3. Exams
    course exam Dec 15 or final exam Dec 17
  4. Practical report
    only as a substitute for Exercices and Net poster
  5. Theoretical report
    only for 10 cu


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Data sets

Some of these are in Finnish, some in English. Own data sets may be used as well. The actual data sets will be found in different forms on BSCW. Here you can see some general information only:


These books may be (at least partially) suitable material:

  • Chatfield, Christopher & Collins, Alexander J. (1980). Introduction to Multivariate Statistics. Chapman & Hall.
  • Everitt, Brian (2005). An R and S-PLUS Companion to Multivariate Analysis. Springer.
  • Everitt, Brian (2009). Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences. Chapman & Hall/CRC.
  • Flury, Bernard (1997). A First Course in Multivariate Statistics. Springer.
  • Hair Jr, Joseph F.; Anderson, Rolph E.; Tatham, Ronald L. & Black, William C. (1998). Multivariate Data Analysis. Fifth Edition, Prentice Hall.
  • Johnson, Richard A. & Wichern, Dean W. (2002). Applied Multivariate Statistical Analysis, Fifth Edition, Prentice Hall.
  • Krzanowski, W. J. (2000). Principles of Multivariate Analysis. Revised Edition, Oxford University Press.
  • Raykov, Tenko & Marcoulides, George A. (2008). An Introduction to Applied Multivariate Analysis. Routledge.
  • Seber, George A. F. (2004). Multivariate Observations. Reprint of First Edition (1984). Wiley.
  • Stevens, James P. (2002). Applied Multivariate Statistics for the Social Sciences. Fourth Edition, Lawrence Erlbaum Associates, Mahwah, New Jersey.
  • Tabachnick, Barbara G. & Fidell, Linda S. (1996). Using Multivariate Statistics. Third Edition, HarperCollins.

The following books are more focused on certain methods or techniques:

  • Cudeck, Robert & MacCallum, Robert C., eds. (2007). Factor Analysis at 100: Historical Developments and Future. Lawrence Erlbaum.
  • Greenacre, Michael (2007). Correspondence Analysis in Practice, Second Edition, Chapman & Hall/CRC.
  • Greenacre, Michael (2010). Biplots in Practice. BBVA Foundation, Madrid, Spain.
  • Greenacre, Michael & Blasius, Jörg, eds. (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC.
  • Gower, J. C. & Hand, D. J. (1996). Biplots. Chapman & Hall.
  • Heck, Ronald H. & Thomas, Scott L. (2009). An Introduction to Multilevel Modeling Techniques, Second Edition. Routledge.
  • Mulaik, Stanley A. (2009). Foundations of Factor Analysis, Second Edition. Chapman & Hall/CRC.
  • Seber, George A. F. (2008). A Matrix Handbook for Statisticians. Wiley.

Suomeksi (in Finnish):

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