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Contact Information:
Dr. Keith A. Markus
kmarkus@aol.com
2122378784
Room 10.63.11
Psychology Department, John Jay College
Course Description:
Additional Reading:
Christensen, R. H. B. (2011). Analysis of
ordinal data with cumulative link models estimation with the
Rpackage ordinal. (http://finzi.psych.upenn.edu/R/library/ordinal/doc/clm_intro.pdf)
or (http://127.0.0.1:12516/library/ordinal/doc/clm_intro.pdf)
Gameroff, M. J. (2005). Using the Proportional
Odds Model for HealthRelated Outcomes: Why, When, and How with
Various SAS® Procedures, Paper 20530, In SAS Institute Inc.
2005. Proceedings of the Thirtieth Annual SAS Users Group
International Conference. Cary, NC: SAS Institute Inc. (http://www2.sas.com/proceedings/sugi30/20530.pdf)
Linzer, D. A. & Lewis, J. (2011a). poLCA:
An R Package for Polytomous Variable Latent Class Analysis.
Journal of Statistical Software, 42, 129. (http://www.jstatsoft.org/v42/i10/paper)
Linzer D. A. & Lewis, J. (2011b). poLCA:
Polytomous Variable Latent Class Analysis. R package version 1.3.
(http://userwww.service.emory.edu/~dlinzer/poLCA/poLCAmanual131.pdf)
Visser, I. (2007). depmix: An Rpackage for
fitting mixture models on mixed multivariate data with Markov
dependencies. (http://127.0.0.1:16899/library/depmix/doc/depmixintro.pdf).
R Installation:
1. Point your web browser to the Comprehensive R Archive
Network (CRAN).
2. From the sidebar menu on the left, near the top, click Mirrors
and select something geographically close (e.g., Pennsylvania).
The same page will reload from a closer server.
3. Select Windows (if that is your operating system), if you use
an Apple computer, your version of R differs somewhat and I am not
familiar with it.
4. Click base. Then download and run the newest version
installation file (currently R2.9.1win32.exe). Further
installation instructions are provided on the CRAN web page.
5. Once installation is compete, start R. You will see a window
with a '>' prompt. At the prompt you may type the following
command to test the installation.
> demo(graphics)
You will be prompted to hit Enter several times as you move
through the demo. A series of graphs should appear in a separate
window inside the R window if R has been installed correctly.
6. On the Packages menu in R, select Install Packages. You will
be prompted with a list of mirror sites that opens in a separate
window. Again, pick something close (e.g., USA PA or USA PA2).
7. Momentarily, you will be prompted with a list of packages in a
window similar to the mirror site window that you just used. Click
ordinal.
8. After some brief chugging, you should have a message in your
main R window indicating that the ordinal package installed
correctly.
9. You can test the installation by typing the following command
at the R prompt.
>library(ordinal)
>?ordinal
This should open a new window outside of the main R window with a
help file on the ordinal package. At the top, it should say
"Regression Models for Ordinal Data via Cumulative Link (Mixed)
Models".
Repeat steps 69 for the poLCA package and depmix package (which
is not the same as depmixS4). Use library(poLCA), ?poLCA,
library(depmix), and ?depmix to test the installation.
10. Return to the R console window where you type commands. At the
prompt, enter the following command. When prompted, choose not to
save the workspace image. This will close R.
>q()
Homework: You will need to run
examples using R and turn in printed output to demonstrate that
you have done this. As such, you need to have a PC capable
of running R, access to the Internet, and a printer.
Homework will generally involve small tasks. However, as
with any other new skill, give yourself plenty of extra time to
get confused, muck around by trial and error, and eventually
figure out what you did wrong.
Turn in homework assignments at the beginning of class on the
days noted on the schedule. The assignments may not make
sense to you until you cover the material to which they refer. The
specific assignments will appear on Blackboard.
Grading: Each of the four examination
modules is worth 20% of your total grade. That leaves
20% for the homework assignments. Letter
grades will be assigned as indicated below.




















Special Needs:
To request accommodations please contact the Office of the Vice
President for Student Affairs (Room 7301 Graduate Center; (212)
8177400). Information about accommodations can be found in the
Graduate Center Student Handbook 0506, pp. 5152).
Academic Honesty:
The Graduate Center of The City University of New York is
committed to the highest standards of academic honesty. Acts of
academic dishonesty include—but are not limited to—plagiarism, (in
drafts, outlines, and examinations, as well as final papers),
cheating, bribery, academic fraud, sabotage of research materials,
the sale of academic papers, and the falsification of records. An
individual who engages in these or related activities or who
knowingly aids another who engages in them is acting in an
academically dishonest manner and will be subject to disciplinary
action in accordance with the bylaws and procedures of The
Graduate Center and the Board of Trustees of The City University
of New York.
Each member of the academic community is expected to give full,
fair, and formal credit to any and all sources that have
contributed to the formulation of ideas, methods, interpretations,
and findings. The absence of such formal credit is an affirmation
representing that the work is fully the writer’s. The term
“sources” includes, but is not limited to, published or
unpublished materials, lectures and lecture notes, computer
programs, mathematical and other symbolic formulations, course
papers, examinations, theses, dissertations, and comments offered
in class or informal discussions, and includes electronic media.
The representation that such work of another person is the
writer’s own is plagiarism.
Care must be taken to document the source of any ideas or
arguments. If the actual words of a source are used, they must
appear within quotation marks. In cases that are unclear, the
writer must take due care to avoid plagiarism.
The source should be cited whenever:
(a) a text is quoted verbatim
(b) data gathered by another are presented in diagrams or tables
(c) the results of a study done by another are used
(d) the work or intellectual effort of another is paraphrased by
the writer
Because the intent to deceive is not a
necessary element in plagiarism, careful note taking and record
keeping are essential in order to avoid unintentional plagiarism.
For additional information, please consult
“Avoiding and Detecting Plagiarism,” available in the Office of
the Vice President for Student Affairs, the Provost’s Office, or
at
http://web.gc.cuny.edu/provost/pdf/AvoidingPlagiarism.pdf.
(From The Graduate Center Student Handbook 0506, pp. 3637)




Azen & Walker (A&W)
Chapter 1: Introduction and overview. Installing and using
R. 


A&W Chapter 2: Probability distributions. 


A&W Chapter 3: Proportions, estimation, and
goodnessoffit. 
Homework Assignment 1 (HA1 probability) 

A&W Chapter 4: Association
between two categorical variables. 


A&W Chapter 5: Association between three categorical variables.  Test Module 1 (weeks 14) 

A&W Chapter 6: Modeling and the generalized linear model.  HA2 (1way and 2way GOF) 

A&W Chapter 7: Loglinear models.  HA3 (CMH test) 

A&W Chapter 8:
Logistic regression with continuous predictors. 
Test Module 2 (weeks 57) 

A&W Chapter 9: Logistic regression with categorical predictors.  HA4 (loglinear) 

A&W Chapter 10: Logistic regression with multicategory outcomes.  
(No classes 4/11) 
Gameroff (2005), Christensen (2011): Proportional odds regression and other cumulative link function models for ordinal data.  HA5 (logistic) 

McCutcheon (M) Chapters 12, Linzer & Lewis (2011a): The logic of latent variables, Latent class analysis.  Test Module 3 (weeks 811) 

M Chapters 34, Linzer & Lewis (2011b): & Estimating latent categorical variables & Analyzing scale response patterns.  

M Chapters 46, Visser (2007): Comparing latent structures among groups & Conclusions.  HA6 (LCA) 
Finals
Week: W 5/23 (W 5/16 is a reading day) 
Test
Module 4 (weeks 1214) 