Carey: Psyc 7291: Multivariate Statistics

Suggested Reading

Notes:

(1) Below is a list of references for specific topics of the course.

(2) Some of this list has been taken from prior classes so some of the textbooks may have come out in more recent editions.

(3) Many older texts on the mathematical background of classic multivariate statistics are out of print but you might be able to pick up used copies. The math has not changed over time, so these can be good bargains.

(4) New texts can be very expensive. It is best to examine a copy before you spend the $$$.

 

    General Texts:
  • Tabachnick, B.G. & Fidell,L.S. (2006). Using Multivariate Statistics, 5th Ed. New York: Allyn and Bacon. A hands-on, how to do it text. Very good practical guide but weak on theoretical background.
  • Grimm, L.G. & Yarnold, P.R., Eds. (1995). Reading and Understanding Multivariate Statistics. Washington, DC: American Psychological Association. Very readable, little math, geared toward nonstatisticians.
  • Grimm, L.G. & Yarnold, P.R., Eds. (2000). Reading and Understanding More Multivariate Statistics. Washington, DC: American Psychological Association. Update and expansion of the Grimm & Yarnold above.
  • Johnson, Dallas E. (1998). Applied multivariate methods for data analysis. Pacific Grove, CA: Duxbury Press. Good balance between practive and theory.
    Classic Multivariate Texts (many good for theoretical background)
  • Anderson, T.W. (2003) An Introduction to Multivariate Statistical Analysis, 3rd Ed. New York: Wiley-Interscience.
  • Bock, R.D. (1975), Multivariate statistical methods in behavioral research. NY: McGraw-Hill.
  • Cooley, W.W. and Lohnes, P.R. (1971), Multivariate data analysis. NY: John Wiley & Sons
  • Dillon, W. R. and Goldstein, M. (1984). Multivariate Analysis, Methods and Applications. New York: Wiley.
  • Everitt, B.S & Dunn, G. (1992) Applied multivariate data analysis. New York : Oxford University Press.
  • Jobson, J. D. Applied multivariate data analysis. New York : Springer Verlag, c1991
  • Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. Academic Press, London.
  • Morrison, D.F. (1990). Multivariate Statistical Methods, 3nd edition. NY: McGraw-Hill.
  • Rao, C.R. (1973). Linear statistical inference and its applications. NY: John Wiley & Sons
  • Tatsuoka. M.M. (1988). Multivariate Analysis. 2nd Ed. New York: Macmillian.
    GLIM (Generalized Linear Models), aka GEE (Generalized Estimating Equations):
  • Dobson, A. (1990)., An Introduction to Generalized Linear Models, London: Chapman and Hall.
  • Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models. London: Chapman and Hall.
  • McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd Ed. London: Chapman and Hall.
    Regression, Classic and Modern:
  • Cohen J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences, 2nd edition. Hillsdale NJ: Lawrence Erlbaum Associates.
  • Draper, N. R. and Smith, H. (1981). Applied Regression Analysis. (second edition). Wiley, New York.
  • Myers, R. H. (1986). Classical and Modern Regression with Applications. Boston: Duxbury.
    Factor Analysis:
  • Mulaik, S.A. (1972), The foundations of factor analysis. NY: McGraw-Hill
  • Harman, H.H. (1976), Modern factor analysis, 3rd edition. Chicago, University of Chicago Press.
  • Gorsuch,R.L. (1983). Factor Analysis, Second Edition. Hillsdale NJ: Lawrence Erlbaum Associates.
  • Lawley, D.N. and Maxwell, A.E. (1971). Factor analysis as a statistical method. NY: McMillan.
    Cluster Analysis :
  • Everitt, B.S.. Landau, B. & Leese, M. (2001). Cluster Analysis, 3nd edition. New York: Wiley.
    Hierarchical Linear Models (Multilevel Analysis) :
  • Raudenbush, S.W. & Bryk, A.S (2002) . Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd Ed. Thousand Oaks CA: Sage.
  • Kreft, I.G.G & DeLeeuw, J. (1998) Introducing Multilevel Modeling. Thousand Oaks CA: Sage.
  • Bosker, R.J. & Snijders (1999) Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks CA: Sage.
    Analysis of Categorical Variables:
  • Agresti, A. (1984). Analysis of ordinal categorical data. New York: Wiley.
  • Agresti, A. (1990). Categorical data analysis. New York: Wiley.
  • Freeman, D.H. (1987). Applied categorical data analysis. New York: M. Dekker.
    Others:
  • Hampel, F. R., Ronchetti, E.M., Rousseeuw, P.J. and Stahel, W.A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley, New York.
  • Hoaglin, D. C., Mosteller, F. and Tukey, J. W., (Eds.) (1983). Understanding Robust and Exploratory Data Analysis. Wiley, New York.
  • Johnson, N. L. and Kotz, S. (1970). Continuous Univariate Distributions, vols. 1,2. Houghton Mifflin, Boston.
  • Puri & Sen (1971), Nonparametric Methods in Multivariate Analysis, Wiley.
  • Bojanov, B.D. , Hakopian, H.A. & Sahakian, A.A. (1993). Spline functions and multivariate interpolations. Dordrecht ; Boston : Kluwer Academic Publishers.
  • Hand, D.J. & Taylor, C.C. (1987). Multivariate analysis of variance and repeated measures: A practical approach to behavioural scientists. London ; New York : Chapman and Hall.
  • Manly, B.F. (1999) Randomization, Bootstrap and Monte Carlo Methods in Biology, 2nd Ed. CRC Press