Spring 2008
Tue, Thu 10:00-11:20, 189 PLC
Lab:
Fri 10:00-12:00, 180 Straub
Instructor: Bertram
Malle
Straub
305, 346-0475
bfmalle@uoregon.edu
Office
hours: Thu 12 p.m. & by apptmt.
Teaching Assistants:
|
Amber Thalmeyer |
Laura Kaehler |
|
Straub 351, 346-4930 |
Straub 383, 346-3936. |
|
Tue 12-1 p.m., Wed
3-4 p.m. |
Tue
2:00-4:00 p.m. |
Overview. This course covers the basic multivariate techniques that are currently used in psychology and other social and life sciences. These include principal components and factor analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and log-linear analysis. Other techniques, such as structural equation modeling, hierarchical linear modeling, canonical correlation, or multidimensional scaling, are not covered in detail but are briefly introduced at the end of the term.
The
learning goal for students is to have a conceptual and statistical
understanding of each technique, be able to apply the correct technique to any
given data set, properly interpret the output of statistical computer packages
(primarily SPSS), and understand and critique scientific papers that use these
techniques.
Even
though much work will go into learning to conduct the various analyses, a
primary goal is to gain a conceptual understanding of each technique, which
refers to its function and capacity to answer scientific questions (not to
achieve p values). Each technique is
a tool that can immensely assist you in understanding your data and testing
your hypotheses; but to apply each tool correctly you need to understand both
your data and the nature of these tools.
The
level of understanding you will reach in this course lies one step deeper than
the level at which you will operate when using the techniques in years to come.
During this term you will learn to think about some mathematical underpinnings
of the techniques, and this step will give you a deeper understanding of the
workings of each technique. Even if you later forget the details of these
underpinnings, you will not forget the conceptual implications they have. All
mathematics introduced in this course is meant to serve your conceptual
understanding.
Because
most or all techniques will be new to you, we need to create maximal
familiarity through redundancy. You are expected to read the relevant textbook
chapters before each lecture. The lectures themselves will introduce the
material from a conceptual and statistical perspective, with directions to
computer analysis. The lab on Friday will briefly review the lectures and then
introduce relevant SPSS commands to analyze actual data using the statistical
techniques. The lab will also discuss the current homework and review past
weekÕs homework. In the homeworks, you will run analyses on provided data sets
and write up the results, culminating in a one-page summary that resembles the
report in an empirical journal article. Finally, the exams will ask you to show
your command over the conceptual underpinnings of each technique.
Grading. Your grade will be based on 9 homeworks (100 pts each) and
two take-home exams (300 points each), for a total of 1500 points. Tentative
grade cut-offs are 1350 (A) and 1200 (B). As mentioned, the homeworks consist
of data retrieval, analysis, interpretation and write-up of results, all done
electronically. They will be graded by the teaching assistants using explicit
criteria that I provide. The teaching assistants have taken this course
previously and performed at a superior level. If anyone has concerns about
their work being graded by a fellow graduate student, please approach me. The
exams (web-posted
May 3 and June
5) consist
of five to six questions on the functional character of techniques and your
understanding of central concepts.
I grade the first exam; TAs help with a few questions on the final exam,
again following an explicit grading scheme.
Readings: Because there is no
all-around good, complete, and affordable textbook of multivariate statistics,
I have selected readings from various textbooks and other sources for this
class (all available on Blackboard).
You are advised to at least skim the relevant chapters before class to
familiarize yourself with the terminology. After class you should read the chapter in detail to deepen
you understanding, which will speed up your homework and prepare you well for
the exams.
For
those of you who would like to own a source book, I recommend Tabachnick and
Fidell (T & F), Multivariate
Statistics (2006, 5th ed.). You
are not required to buy T & F, but if you do, you must still read the
other required chapters, because
for some topics T & F just donÕt provide the background you need.
Resources: Extensive lecture handouts are posted on the Blackboard page
and on a course web page (www.uoregon.edu/~bfmalle/613.html).
Each lecture is also audio recorded and made available on Blackboard. Before each lecture, last yearÕs
material can be inspected; after ThursdayÕs lecture, the weekÕs material will
be updated where appropriate. The Blackboard page also contains the
electronic reading library and additional resource material.
I
assume that you have reliable access to SPSS statistical software and that you
can without a problem exchange electronic documents with your TAs.
Homework: Each Wednesday, a homework
assignment will be posted on the web and is due the following Thursday, 10
a.m. Given the long time to
complete the homework, extension will be given only in extraordinary
circumstances and only before the due
date. Points will be subtracted
for lateness.
Analyses
relevant to the homework will be practiced during lab hours on Friday
(10:00am-12:00 in Straub 180). Typically the assignment contains a data set and
several instructions to analyze it. Your homework is complete if you run all
required analyses, edit the output files down to the essential information
(there will typically be a page limit), and annotate the output to demonstrate
your understanding of what SPSS was doing. Finally, you write up a one-page summary of the results as
you would for an empirical journal article. The annotated outputs and the written results must be sent
to your TA via email by Thursday, 10 a.m.
For the written results, you can take Tabachnick & FidellÕs result
summaries or two sample summaries on the course web page as models (http://darkwing.uoregon.edu/~bfmalle/613/Summaries.html).
Be sure to write clearly, concisely, and meaningfully.
For
all of your homeworks I strongly recommend that you have the SPSS Syntax command reference document
handy (available in the SPSS Help pull-down menu and on Blackboard). Studying
it can save you many hours of SPSS frustration. If you are stuck with an SPSS problem for more than 10
minutes, contact me or your TAs to help you solve it.
The
readings in this class are selected chapters from various statistics textbooks,
SPSS handbooks, and a few journal articles. In addition, I have listed the
relevant Tabachnick & Fidell (2006) chapters where available. In some
cases, a Tabachnick & Fidell chapter is required reading. Required readings are printed in blue font below.
All
reading materials are available as pdf files on Blackboard. If you need to
check out paper documents for copying, please contact me.
Textbook authors cited below:
Cliff, N. (1987). Analyzing
multivariate data. Harcourt Brace Jovanovich: San Diego.
Dillon, W. R., &
Goldstein, M. (1984). Multivariate
Analysis: Methods and Applications. Wiley: New York.
Tabachnick, B. G., &
Fidell, L. S. (2006). Using multivariate statistics (5th ed.).
New York: HarperCollins.
L
1: Tu Apr 1 The
multivariate approach: Introduction and overview
È Dillon & Goldstein (1984). Overview of
multivariate techniques (pp. 19-22)
È Tabachnick & Fidell (2006). ch. 1 + ch.
2.
Optional
SPSS Syntax command reference
L 2: Thu Apr 3
Data screening and exploratory data analysis
È Tabachnick & Fidell (2006). ch. 4
È Cohen, J. (1990). Things I have learned (so
far). American Psychologist, 45,
1304-1312.
Optional
SPSS EXAMINE complete chapter
SPSS EXAMINE syntax reference
Fri Apr 4 Lab 1:
Exploratory data analysis
L
3: Tue Apr 8 Matrix algebra (Introduction)
È [very basic, slow pace, for beginners] Cliff,
N. (1987). Elements of matrix algebra for statistical applications (ch. 1),
Vectors (ch. 3).
È [faster pace, for intermediates] Dillon
& Goldstein (1984). Vector and matrix operations and selected statistical
concepts (pp. 521-539).
Optional
Tabachnick & Fidell (2006). Appendix A
L 4: Thu Apr 10
Matrix algebrafor statistics
È [faster pace] Dillon & Goldstein (1984). Statistical
concepts and vector and matrix operations (pp. 6-18).
È [slower pace, thorough] Cliff, N. (1987).
Statistical formulas in matrix form (ch. 2), Variances and covariances of
linear combinations (ch. 4), The inverse (ch. 5).
Optional
Handout on determinants and inverses [advanced]
Fri Apr 11 Lab 2: Matrix
concepts
L
5: Tue Apr 15 Test Theory and Item Analysis
È http://en.wikipedia.org/wiki/Classical_test_theory
È SPSS Base System UserÕs Guide. ch. 26
(Procedure RELIABILITY)
Optional
[very
thorough explanation of reliability] Nunnally, J. C. (1967). Theory of measurement error. In J. C. Nunnally, Psychometric theory
(ch. 6, pp. 172-205). New York:
McGraw-Hill.
Messick, S. (1995). Validity of psychological assessment:
Validation of inferences from personsÕ responses and performances as scientific
inquiry into score meaning. American
Psychologist, 50, 741-749.
L 6: Thu Apr 17
Principal components analysis (PCA)
È Dillon & Goldstein (1984). Principal
components analysis (ch. 2, pp. 23-39, 47-52)
È SPSS Base System UserÕs Guide. ch. 21
(Procedure FACTOR)
Optional
Cliff (1987). Components and principal components of
variables (ch. 13)
Tabachnick & Fidell (2006). ch. 13
Fri Apr 18 Lab 3: PCA
L
7: Tue Apr 22 PCA extensions
L 8: Thu Apr 24
Factor analysis (FA)
È Dillon & Goldstein (1984). Factor Analysis
(ch. 3)
È (review from Apr 14) SPSS Base System UserÕs
Guide. ch. 21 (Procedure FACTOR)
Optional
Cliff (1987). The common factor model (ch. 15)[ challenging but
thorough.]
Gorsuch, R. L. (1997). Exploratory factor analysis: Its role
in item analysis. Journal of personality
assessment, 68, 532-560.]
Russell, D. W. (2002). In search of underlying dimensions:
The use (and abuse) of factor analysis in Personality and Social Psychology
Bulletin. Personality and Social
Psychology Bulletin, 28,, 1629-1646.
Fri Apr 25 Lab 4: Factor
Analysis
L
9: Tue Apr 29 (M)ANOVA preludes: Interactions, orthogonality
È Rosnow, R. L., & Rosenthal, R. (1989).
Statistical procedures and the justification of knowledge in psychological
science. American Psychologist, 44, 1276-1284.
È Rosnow, R. L., & Rosenthal, R. (1989).
Definition and interpretation of interaction effects. Psychological Bulletin, 105, 143-146. [make sure you read this--it will
prevent you from making one of the most wide-spread statistical errors in
psychology!]
L 10: Thu May 1
Significance testing and meta-analysis
È Schmidt, F. (1996). Statistical significance
testing and cumulative knowledge in psychology: Implications for training of
researchers. Psychological Methods, 1, 115-129.
È DeCoster, J. (2004). Meta-analysis. In Kempf-Leonard, K. (Ed.), The
Encyclopedia of Social Measurement.
San Diego, CA: Academic Press.
Optional
Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118, 183-192.
Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychological Methods, 1, 170-177.
Midterm take-home exam posted Thu May 1
Fri May
2 Lab 5: Meta-analysis
L
11: Tue May 6 General multivariate analysis of variance (Manova)
È Tabachnick & Fidell (2006). ch. 9
È SPSS Advanced Statistics UserÕs Guide. ch. 3
(Procedure MANOVA)
È SPSS Manual: Appendix B–Categorical
variable coding schemes.
Optional
SPSS
Keywords: Interpreting MANOVA parameter estimates.
Midterm take-home exam due Thu May 8, 4:00 p.m.
L 12: Thu May 8
Discriminant function analysis (DFA)
Dillon
& Goldstein? È Tabachnick & Fidell (2006). ch. 11
È SPSS Advanced Statistics UserÕs Guide. ch. 1
(Procedure DISCRIMINANT)
Optional
Tabachnick & Fidell (2006). ch. 8
Fri May
9 Lab 6: General
Manova and DFA
L
13: Tue May 13 Univariate vs. multivariate repeated measures analysis of variance
È SPSS Advanced Statistics UserÕs Guide. ch. 4
(More on Procedure MANOVA)
L 14: Thu May
15 Multifactor multivariate repeated measures analysis of variance (profile
analysis)
È Tabachnick & Fidell (2006). ch. 10 (pp.
441-483; pp. 503-505)
Fri May
16 Lab 7:
Repeated-measure analysis
L
15: Tue May 20 (M)Anova expansions (doubly multivariate designs; simple
effects)
È SPSS handout on simple effects
È SPSS handout on doubly multivariate analysis
Optional
Tabachnick
& Fidell (2006). ch. 3
L 16: Thu May
22 Logistic Regression
È Tabachnick & Fidell (2006). ch. 12
È SPSS Advanced Statistics UserÕs Guide. ch. 2
(LOGISTIC REGRESSION)
Fri May
23 Lab 8: Logistic
Regression
L
17: Tue May 27 From c2
to log-linear analysis
Other?È
Tabachnick & Fidell (2006). ch. 7
L 18: Thu May
29 Log-linear and logit analysis
È SPSS Advanced Statistics UserÕs Guide. ch. 5
(Procedure HILOGLINEAR) and ch. 6 (Procedure LOGLINEAR)
Fri
5/30 Lab
9: Log-linear analysis
L
19: Tue 6/3 Putting it all together: The multivariate tool box
Optional
Tabachnick & Fidell (2006). ch. 15, 2
Final take-home exam posted Tue Jun 3
L 20: Thu 6/5
Luxury tools: Canonical correlation, covariance structure analysis;
cluster
analysis; multidimensional scaling.
Optional
Tabachnick & Fidell (2006). ch. 14
Fri 6/6 No lab