Aiken
Psychology 532
Multivariate Analysis
Bibliography In Statistical Methods
Fall, 2002
BIBLI0-F02
This bibliography covers multivariate analysis, regression analysis (including
logistic regression) and some topics less familiar to psychologists: generalized
linear model, hierarchical linear models, correspondence analysis,
epidemiological methods, and analysis of categorical variables. The bibliography
is by no means complete but will get you started. I have included a few out-ofprint but wonderful books that are available in many university libraries. I
have provided a few notes on the majority of texts. I also include books that
address computer applications in these topics.
I. TEXT FOR MATHEMATICAL BACKGROUND, MATRIX ALGEBRA
Carroll, J. D., Green, P. E., & Chaturvedi, A. (1997). Mathematical Tools for
Applied Multivariate Analysis 2nd ed. New York: Academic Press.
This is a wonderful book.
II.
There is no other like it for social scientists.
MULTIPLE REGRESSION ANALYSIS
Aiken, L. S., & West, S. G. (1991) Multiple regression: testing and
interpreting interactions. Newbury Park, CA: Sage.
Treatment of interactions in ordinary least squares regression.
matrices. SPSS examples for all analyses.
No
Allison, P.D. (1998). Multiple Regression: A Primer. Thousand Oaks: Sage.
Birkes, D., & Dodge, Y. (1993). Alternative methods of regression.
New York: John Wiley (Wiley-Interscience).
Begins with ordinary least squares and then takes reader beyond with
readable chapters on robust regression (least absolute deviations
regression, M-regression), nonparametric regression, Bayesian regression,
ridge regression, with an introduction to other methods. This is a
very important book for new developments, and is very readable.
Cohen, J., & Cohen, P. C. (1983) (2nd Ed). Applied multiple
regression/correlation analysis for the behavioral sciences. Hillsdale, NJ.:
Lawrence Erlbaum.
Old time stuff, the basics, no matrices, everyone cites this.
Cohen, J., Cohen, P. C., West, S. G., & Aiken, L. S. (2003). (3rd Ed.) Applied
multiple regression/correlation analysis for the behavioral sciences. Mahwah,
NJ.: Lawrence Erlbaum.
All of Cohen and Cohen (1983) plus regression diagnostics, regression
graphics, assumptions (violations and fixes), interactions in the Aiken and West
(1991) manner, logistic, ordinal logistic and Poisson regression, multilevel
(hierarchical linear) models, longitudinal data analysis overview, and more. Has
accompanying CD with SPSS, SAS, SYSTAT syntax and data.
Cook, R. D. (1999). Applied Regression: Including Computing and Graphics. New
York: John Wiley.
Integrates new approaches, e.g. generalized linear models with traditional
models, and uses graphics as a unifying theme.
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Cook, R. D. (1998). Regression Graphics: Ideas for Studying Regressions through
Graphics. New York: John Wiley.
Graphical regression analysis including model selection through graphics.
Cook, R. D., & Weisberg, S. (1994). An introduction to regression graphics.
York: Wiley.
New
Presents modern graphical approaches to the display of data in the
context of multiple regression. Steve West used this text to teach
regression graphics in fall, 1995. It is accompanied by a wonderful
computer based graphics package, RCODE2. This program is on computers in
the Psychology statistics laboratory.
Draper, N. R., & Smith, H. (1998).
York: Wiley.
Applied regression analysis. 3rd Ed.
New
Classic, accessible, new edition.
Fox, J. (1997) Applied Regression Analysis, Linear Models, and Related Methods
Thousand Oaks : Sage. 1997
Has all the standard stuff, but has wonderful writing on logit and probit
models for dichotomous outcomes, and alternative regression models
nonlinear, nonparametric).
Graybill, F. A., & Iyer, H. K. (1994). Regression analysis: Concepts and
applications. Belmont, CA: Duxbury Press.
Basic book, but good chapter on nonlinear regression.
Hocking, R. R. (1996). Methods and applications of linear models.
Wiley.
New York:
Treats both regression and analysis of variance in a general matrix based
approach to the general linear model; pretty mathematical; includes some
work on missing data.
Kupper, L., & Muller, K. (1997). Applied regression analysis an multivariable
techniques. 3rd Ed. Belmont, CA: Wadwworth.
This is the next edition of the former standard, Kleinbaum, Kupper, and
Muller, 1988. Solid book in multiple regression. Introduction provided to
maximumum likelihood estimation, with application to Poisson and Logistic
regression. Has examples from the health field.
Mosteller, F., & Tukey, J. W. (1977). Data analysis and regression: a second
course in statistics. Reading, MA.: Addison-Wesley.
Important book in the tradition of Exploratory Data Analysis (EDA), a
"movement" in statistics begun by John Tukey. The notation is completely
idiosyncratic, and you should be familiar with John Tukey's companion text,
Exploratory data analysis, 1977. These books are really the precursors of
the modern approaches that fall under "generalized linear model," a very
data driven approach to data analysis, at the opposite end of the continuum
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from classical hypothesis testing approaches.
Neter, J., Kutner, M. H., Nachtsheim, C.J., & Wasserman, W. (1996). Applied
linear regression models. Chicago, IL.: Irwin, 3nd Edition.
Excellent coverage of the usual plus modern topics: diagnostics, time
series and autocorrelation, logistic regression, introduction to nonlinear
regression. New, 3rd edition of the classic Neter, Wasserman, & Kutner. You
will acquired the matrix algebra background in this course to make full use
of this book.
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and
Prediction. 2nd Ed. Fort Worth: Harcourt Brace.
Solid for the basics, easy reading.
1982 edition.
Has been updated from the original
Rawlings, J. O., Pantula, S. G., Dickey, D. A. (1998).
Analysis: A Research Tool. New York: Springer-Verlag.
Applied Regression
This is a basic book used in business and engineering. It is very
accessible to social scientists, up to date. I find it very useful.
Ryan, T. P. (1997). Modern regression methods.
New York: Wiley.
Matrix based text with wonderful verbal sections of good advice, straight
from the hip. I really like this book.
Weisberg, S. (1985). Applied linear regression. New York: John Wiley.
Standard stuff plus more: residuals and influence, data transformations,
missing data, weighted least squares, incomplete data, all
in matrix form, good for a second course in regression analysis.
REGRESSION COMPUTER APLICATIONS IN SAS, SPSS
Freund, R. J., & Littell, R. C. (1999). SAS system for regression. Cary, NC: SAS
Institute. (now handled by John Wiley).
These SAS books published by the company that produces SAS have very clear
examples, explanations of printout. They are written by people with a lot
of experience in the particular statistical topic. I find them an excellent
supplement to texts. This one includes nonparametric regression
(smoothing), logistic regression, nonlinear regression, regression
graphics,
SPSS, Inc. Staff. (1999) Regression Models 10.0. (Manual for SPSS Regression).
NJ: Prentice Hall.
This manual has very basic introductions to topics in regression analysis
including nonlinear regression, survival analysis, time series, with
documentation of the current SPSS Regression section of the Advanced SPSS
module.
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III. GENERALIZED LINEAR MODEL
(LOGISTIC REGRESSION AS SUBSET)
The term "generalized linear model" refers to linear equations relating
predictors to a criterion which allow two variants on familiar ordinary least
squares regression: (1) the distribution of errors may take on many forms other
than normal, e.g. binomial, poissson (2) the relationship of the observed
dependent variable to the dependent variable analyzed in the equation may be
other than and identity relationship, e.g. in logistic regression in which a
binary response is observed, but in which the logistic transformation of the
probability of response is analyzed. This is the new wave in linear models. Use
of these models is associated with several statistical packages.
GENERALIZED LINEAR MODEL
Aitkin, M., Anderson, D., Francis, B., & Hinde, J. (1990). Statistical
in GLIM. Oxford, England: Oxford University Press.
modeling
Devoted to a British computer package, GLIM, superseded by S+
Chambers, J. M., & Hastie, T. J. (1992). Statistical Models in S. Pacific
Grove, CA.: Wadsworth & Brooks/Cole Advanced Books and Software.
Devoted to early version of current advanced statistical package S+.
Farmier, L., & Tutz, G. (2001). (2nd Ed). Multivariate Statistical Modeling
Based on Generalized Linear Model. New York : Springer-Verlag
Mathematical presentation of generalized linear model.
Gill,
(2000). Generalized linear models: A Unified approach. Sage University
Paper Series on Quantitative Applications in the Social Sciences, 07-134.
This is one of the little green Sage books; should be a simplified
introduction. Start here, because the rest of the references are straight
math stat.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. (2nd Ed.)
London: Chapman Hall.
This is the classic, plenty of math.
LOGISTIC REGRESSION (See also Categorical Data section below)
Hosmer, D. W., & Lemeshow, S. (2000). (2nd Ed.). Applied logistic regression.
New York: Wiley.
The standard text for social scientists.
Jaccard, J.(2001). Interaction effects in logistic regression. Sage University
Papers Series on Quantitative Applications in the Social Sciences. 07-135.
Thousand Oaks: Sage.
This book completely demystifies interactions in logistic regression. It is
great reading.
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Kleinbaum, D. G. (1996).
Springer-Verlag.
Logistic Regression:
A Self-Learning Text.
New York:
Kleinbaum teaches people in public health and also physicians. His
programmed textbooks (this is one) in various topic, e.g. epidemiology, are
easy and fun.
(See Fox, 1997 in Regression Section above)
Long, J. S. (1997). Regression models for categorical and limited dependent
variables. Thousand Oaks, CA.: Sage. (L)
This is a fabulous book for regression with any dependent variables that
are non-normal, including logistic regression, Poisson regression,
censored data.
Menard, S. (2001). Applied logistic regression analysis. Sage University Paper
series on Quantitative Applications in the Social Sciences, 07-106, Thousand
Oaks, CA: Sage. 2nd Edition
One of the Sage "green books", Quantitative Applications in the Social
Sciences, #106, good for getting started. Very well written.
Pampel, F. C. (2000). Logistic regression: A primer. Sage University Paper
series on Quantitative Applications in the Social Sciences, 07-132. Thousand
Oaks, CA: Sage.
Another of the Sage "green books". Also explains probit analysis. Updates
the Menard book cited above. Menard and Pampel make a good introductory
pair of books.
LOGISTIC REGRESSION COMPUTER APLICATIONS IN SAS
Allison, P. D. (2001). Logistic regression using the SAS System:
application. Cary, NC: SAS Publishing. ISBN 1-58025-352-0.
Theory and
Will include logistic, ordinal logistic, Poisson regression.
Miron, et al. (1998). Logistic regression examples using the SAS system. Cary,
NC: SAS Institute, Inc.
These SAS books published by the company that produces SAS have very clear
examples, explanations of printout. They are written by people with a lot
of experience in the particular statistical topic. I find them an excellent
supplement to texts.
IV. MULTIVARIATE ANALYSIS
Berry, W. D. (1999). Understanding multivariate methods. Boulder, CO: Westview
Press.
A small primer like the Sage green books, in a political science series.
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Berry writes very clearly.
Cliff, N. (1987). Analyzing Multivariate Data. San Diego: Harcourt Brace.
Good verbal explanations. A bit of matrices. Comments and hints on
SPSS, BMDP, SAS, but not complete examples. (Group this with Stevens,
Tabachnick & Fidell). (Out of print and missing from the library).
Dillon, W. R., & Goldstein, M. (1984).
Applications. New York: Wiley.
Multivariate analysis:
Accessible, used in business schools.
here. Some matrices.
Methods and
I have found some useful information
Grimm, L. C., & Yarnold, P. R. (1995). Reading and understanding multivariate
statisics. Washington, D. C.: American Psychological Association.
Very basic descriptions, for people reading literature that use
multivariate analyses.
Grimm, L. C., & Yarnold, P. R. (2000). Uderstanding more multivariate statisics.
Washington, D. C.: American Psychological Association.
Includes cluster analysis, q factor analysis, canonical correlation,
repeated measures, survival analysis.Very basic descriptions, for people
reading literature that use multivariate analyses.
Hair, J. F. (1998).
Hall.
Multivariate data analysis.
Englewood Cliffs, NJ:
Prentice
Hand, D. J., & Taylor, C. C. Multivariate analysis of variance for repeated
measures: A practical approach for behavioral scientisits. London: Chapman &
Hall.
Johnson, R. A., & Wichern, D. W. (1998). (4th Edition).
statistical analysis, 3rd Ed. N.J.: Prentice Hall.
Applied multivariate
Math department choice for multivariate analysis. Lots of matrix algebra.
Come with a disk of data sets and SAS code, which is also included in the
book. Has a chapter on clustering and multidimensional scaling.
(5th edition expected November, 2001)
Khattree, R., & Naik, D. N. (1999).
software. New York: Wiley.
Applied multivariate statistics with SAS
Part text book, clearly written, matrix algebra, mostly focused on
MANOVA, repeated measures, including mixed models. Gives a brief
introduction to SAS IML, the SAS matrix language.
Harris, R. J. (2001). A primer of multivariate statistics. (3rd Ed.)
Mahwah, NJ: Lawrence Erlbaum.
This book is good on classic multivariate material, with math at
a level for which this course will prepare you. Has SPSS output as
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well, and a smattering of SAS. Best on multivariate analysis
of variance and related topics.
Marascuilo, L. A., & Levin, J. R. (1983). Multivariate statistics in the social
sciences. Monterey, CA.: Brooks/Cole.
An old favorite book of mine, balanced between math and application (out
of print).
Marcoulides, G. A., & Hershberger, S. L. (1997). Multivariate statistical method:
A first course. Mahwah, NJ: Lawrence Erlbaum.
Introductory text, hardly any math. Good verbal explanations. Lots of SAS
code and SAS output.
Morrison, D. F. (1990). Multivariate statistical methods. 3rd Edition.
This, in its first edition, 1967, was the first ever multivariate analysis
book for the social sciences. Good math, great presentation on the
multivariate normal distribution, only little examples, no packages,
all matrices.
Rencher, A. C. (1995) Methods of Multivariate Analysis. New York:
Wiley.
Some math, but accessible. It comes with a disk with SAS code for
multivariate analysis methods.
Rencher, A. C. (1998)
York Wiley.
Multivariate statistical inference and applications. New
Focus is on new techniques for examining the effect of individual variables
in set of variables.
Stevens, J. (2001). Applied multivariate statistics for the social sciences.
4th Ed. Mahwah, NJ.: Lawrence Erlbaum.
Very complete coverage of MANOVA from an applications standpoint with SPSS,
SAS.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. 4th
edition. Boston: Allyn & Bacon.
Almost no math, if this is possible, and great applications with the big
three packages: SAS, SPSS, SYSTAT; practical text. Covers classic
multivariate analysis plus logistic, survival, time series, multiway
frequency analysis. Website for related data sets replaces the SPSS and SAS
workbooks that accompanied the 3rd edition: www.abacon.com/tabachnick
Tabachnich, B. G. (1997). Using Multivariate Statistics: SAS Workshop.
Reading, PA: Addison Wesley.
SAS workbook that accompanies the Tabachnick & Fidell text (3rd edition).
With disk of data sets and code. Apparently not updated to the 4th
edition.
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Tabachnich, B. G. (1996). SPSS for Windows Workbook to accompany Using
Multivariate Statistics, third edition. New York: Harper Collins.
SPSS workbook that accompanies the Tabachnick & Fidell text (3rd Edition).
With disk of data sets and code.
Tatsuoka, M. M. (1988). Multivariate analysis: techniques for educational and
psychological research. New York: Macmillan. 2nd Edition.
Second edition of a classic for the social sciences. This is really fine on
the mathematics, elegant, quite complete, but light on application, no
examples from the packages, because Tatsuoka liked his students to use a
matrix language to write their own analysis routines. Tatsuoka died in
1996.
Wickens, T. D. (1995).
Lawrence Erlbaum.
The geometry of multivariate statistics.
Mahwah, NJ:
MULTIVARIATE COMPUTER APPLICATIONS IN SPSS, SAS
Khattree, R., & Naik, D. N. (1999).
software. New York: Wiley.
Applied multivariate statistics with SAS
Littell, Ra. C., Frend, R. J., & Spector, P.C. (1997). SAS System for Linear
Models (4th.). Cary, NC: SAS Publishing
Another of the SAS computing books. Mostly focused on ANOV, including
repeated measures, mixed models included.
Osterlind, S, J., & Tabachnick, B. G. (2001). SPSS for Windows Workbook. Boston:
Allyn & Bacon.
To accompany Tabachnick & Fidell (2001), Using Multivariate Statistics.
Boston: Allyn & Bacon. Gives a Website with data sets. Coverage of
topics in the Tabachnick & Fidell text.
Tabachnich, B. G. (1997). Using Multivariate Statistics: SAS Workshop.
Reading, PA: Addison Wesley.
SAS workbook that accompanies the Tabachnick & Fidell text (3rd edition).
With disk of data sets and code. Apparently not updated to the 4th
edition of Tabachnick & Fidell (2001), Using Multivariate Statistics.
V. FACTOR ANALYSIS
Not much new in the way of textbooks. Gorsuch and Mulaik talked about revising
their texts. Mulaik has retired.
Comrey, A. L., & Lee, H. B. (1992) A first course in factor analysis.
Hillsdale, NJ.: Lawrence Erlbaum.
Cureton, E. E., & D'Agostino, R. B. (1993). Factor Analysis: An applied
approach. Hillsdale, NJ: Lawrence Erlbaum.
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Gorsuch, R. L. (1983) Factor analysis. 2nd edition. Hillsdale, NJ.: Lawrence
Erlbaum.
(Under revision at present)
Mulaik, S. A., (1972). The foundations of factor analysis. New York:
McGraw Hill.
(Under revision at present)
McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ.:
Lawrence Erlbaum.
Comrey is the primer, read this first, though it is not modern; it will
prepare you for harder stuff. Gorsuch is at a moderate level, and Mulaik and
McDonald are tough. McDonald covers both exploratory and confirmatory factor
analysis, gives an introduction to item response theory (IRT) in relation to
factor analysis.
VI. CATEGORICAL MULTIVARIATE PROCEDURES
Agresti, A. (1990). Categorical data analysis. New York: Wiley.
This is the current complete reference, much math, matrices.
Agresti, A. (1996). An introduction to categorical data analysis.
Wiley.
New York:
Less technical that the 1990 book, with good introduction to categorical
response data, including count data, and accessible chapters on Poisson
regression (for count data) and logistic regression. Start here.
Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete
multivariate analysis: theory and practice. Cambridge, MA.: MIT Press.
This is the original, classic complete references, much math. Still in
print.
Collett, D. (1991, reprinted 1996).
and Hall.
Modelling binary data.
London: Chapman
Good reading about binary data, very oriented to analyis and
interpretation.
Fienberg, S. E. (1980). The analysis of cross-classified categorical data.
Cambridge, MA.: MIT Press. 2nd edition.
This is the book Fienberg, of Bishop, Fienberg, and Holland, wrote, to get
average people (read that social sciences, not math types) started in
modern categorical data analysis. Good for introduction to log linear
models, but not to logisitic regression.
Hagenaars, J. A. (1990). Categorical longitudinal data. Newbury Park: Sage.
Good, moderate level introduction to log linear and latent class models;
you need Fienberg first. Then panel modeling issues, including cohort
analysis.
Le-Chap, T. (1998) Applied Categorical Data Analysis. New York: Wiley.
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VII. RANDOM COEFFICIENT MODELS (HIERARCHICAL LINEAR MODELS, MULTILEVEL MODELS)
Hierarchical linear models are random coefficient regression models that treat
data that are hierarchically organized, e.g. clients within group therapy groups,
with therapy groups assigned to therapists, such that each therapist has several
groups. In hierarchical models, the effects of different levels of the hierarchy
are simultaneously estimated, e.g. therapist effects, group effects, and
individual client effects. These models have been developed and are being
applied in Education. They have just begun to be used in Psychology.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications
and data analysis methods (2nd ed.). Thousand Oaks: Sage.
Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon, R. (2001). HLM5:
Hierarchcal linear and nonlinear modeling (2nd ed). Chicago: Scientific
Software International.
Goldstein, H. (1995). Multilevel statistical methods.
Goldstein is in education.
Kreft, I., & De Leeuw, J. (1998).
New York: Halsted Press.
His accompanying software is
MLWin.
Introducing multilevel models. London: Sage.
This is a wonderful introduction to the topic of multilevel models, clear,
easy to read, talks about the issues. Great place to start (or see Snijders
& Bosker, two references below).
Longford, N. T. (1993). Random coefficient models. Oxford: Oxford University
Press.
Longford's associated software is VARCL, out of use.
Snijders, T. & Bosker, R. (1999). Multilevel Analysis: An introduction to basic
and advanced multilevel modeling. London, England: Sage (can also be obtained
from Sage at Thousand Oaks, CA).
Wonderful book, well written, clear; you can start here, or read this
right after Kreft & DeLeeuw.
MULTILEVEL (MIXED) COMPUTER APPLICATIONS IN SAS
Littell, R. C., Milliken, C. A., Stroup, W. W., & Wolfinger, R. D. (1999).
System for mixed models. Cary, NC: SAS Publishing.
SAS
As of SPSS 11, there is a mixed model procedure in SPSS.
There is specialized software for multilevel modeling (HLM, MLWin) described in
Snijders and Bosker (1999).
VIII. MISSING DATA
Allison, Paul D.(2001) Missing data. Sage University Papers Series on
Quantitative Applications in the Social Sciences. CA: Sage (number 136).
This book is a star, wonderful introduction to treatment of missing data
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with modern maximum likelihood and multiple imputation methods. Examples
with SAS code and lots of practical advice to get you started. A little
green Sage book.
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis wth missing data.
New York: Wiley.
This is the classic.
IX. A GOOD REFERENCE FOR INTRODUCTIONS TO IMPORTANT TOPICS IN DATA ANALYSIS
Fox, J., & Long, J. S. (Eds.) (1990). Modern methods of data analysis. Newbury
Park: Sage.
Contains chapters on transformations, regression diagnostics, robust
regression, bootstrapping, missing data, selection bias, all by
excellent authors.
X. ANALYSIS OF EPIDEMIOLOGICAL DATA
Selvin, S. (1991). Statistical Analysis of Epidemiological Data. Oxford:
Oxford University Press.
This book provides very clear introductions to the analysis of
epidemiological data, including chapters on the analysis of binary
outcomes (logistic model), life tables, survival.
(Note:
Kleinbaum also has a self-taught book in this area).
XI. SURVIVAL ANALYSIS
Hosmer, D.W., & Lemeshow, S.(1999). Applied Survival Analysis: Regression
Modeling of Time to Event Data. New York:Wiley.
SURVIVAL ANALYSIS COMPUTER APLICATION IN SAS
Allison, P. D. (1998). Survival analysis using the SAS system: a practical guide.
Cary, NC: SAS Publishing.
XII. REGRESSION ANALYSIS OF COUNT DATA
Cameron, A.C., & Trivedi, P. K. (1998). Regression Analysis of Count Data. New
York: Cambridge University Press.
(see also Long, 1997 in the logistic regression section--Long is terrific here)
XIII. REGRESSION ANALYSIS OF CENSORED DATA
Breen, R.(1996). Regression Models: Censored, Sample Selected, or Truncated Data.
Thousand Oaks:Sage
(see also Long, 1997 in the logistic regression section)
XIV. NONPARAMETRIC REGRESSION ANALYSIS
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Nonparametric regression analysis involves fitting regression functions that are
completely driven by the data themselves; no model such as a "linear regression
model" is imposed. The two volume set by Fox is a terrific introduction.
Fox, J. (2000). Nonparametric simple regression: Smoothing Scatterplots. Sage
University Series on Quantitative Applications in the Social Sciences, 07-130.
Thousand Oaks, CA: Sage.
Fox, J. (2000). Multiple and Generalized Nonparametric Regression. Sage
University Series on Quantitative Applications in the Social Sciences, 07-131.
Thousand Oaks, CA: Sage.
XV. HOMOGENEITY AND CORRESPONDENCE ANALYSIS
European statisticians at the University of Paris (particularly Benzecri), and at
University of Leiden (van de Geer, DeLeeuw) have developed nonparametric analogs
of multivariate analysis, the basis of which are categorical data. The approach
draws on multivariate analysis concepts and multidimensional scaling. In the
United States, most of the applications are in marketing, for scaling
stimuli (read that products); work is associated with J. Douglas Carroll at
Rutgers, School of Business. Multivariate analysis texts in Business typically
devote a chapter to correspondence analysis, and there are correspondence
analysis programs in both SPSS (SPSS Categories, a special program, not in the
standard package) and in SAS. The classic text is Gifi, and one needs great
courage and a lot of math, even to begin this text. Fortunately, there is a twovolume set (theory and applications) that is much more accessible, also listed
below.
Gifi, Albert. (1990). Nonlinear multivariate analysis. New York: John Wiley.
And so, who is Albert Gifi? He was the butler of Sir Francis Galton. The
book was actually written by approximately 20 statisticians at the
University of Leiden. The most notable in the U.S. is Jan deLeeuw, at
UCLA.
Van de Geer, J. P. (1993). Multivariate analysis of categorical data: Theory.
Newbury Park: Wiley.
Van de Geer, J. P. (1993). Multivariate analysis of categorical data:
Applications. Newbury Park: Wiley.
XVI. ANALYSIS OF VARIANCE
I give only two books here. The Maxwell and Delaney book is just wonderful and I
drew on it to prepare materials on repeated measures ANOVA/MANOVA. The second by
Tabacknick and Fidell is wonderful for analysis, with lots of computer syntax
(SPSS, SAS).
Maxwell, S. E., & Delaney, H. D. (1990). Designing experiments and analyzing
data: a model-comparison perspective. Pacific Grove, CA: Brooks/Cole.
Remarkable book for learning ANOVA in depth. The book is under revision.
Tabachnick, B. G., & Fidell, L. S. (2001). Computer-assisted research design and
analysis. Boston: Allyn and Bacon
Lots of ANOVA designs and wisdom. Lots of SPSS and SAS
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