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Reference number = 00972

CLICK HERE  for online registration
 
 

LINKS TO EXERCISES AND FEEDBACK GENERALLY GO HERE.

 

WATCH FOR ANNOUNCEMENTS ABOUT : SCHEDULE CHANGES PAPER INFORMATION FEEDBACK ETC.

 

COURSE  OVERVIEW

REQUIRED  MATERIALS

ASSIGNMENTS

WEB-ASSISTED  INFO

COURSE  TOPICS

 

MY OFFICE: 307K Stone Building  850-644-8778  OFFICE HOURS: Exceptions to be announced 12:30-2:00 P.M. Tuesday & Thursday & by appointment slosh@garnet.acns.fsu.edu

FSU Stone Building  INSTRUCTOR: Professor Susan Carol Losh 006  Stone Building Thursdays 2:00-4:50 PM CLICK HERE  to find the Stone Building

PLEASE INFORM ME IMMEDIATELY IF YOU REQUIRE ANY ASSISTANCE WITH DISABILITIES!
 
 

The Multivariate Analysis of Non-numeric Data addresses models with categorical dependent variables. Analyses with these variables do not lend themselves to some of the more traditional statistics used in education, the behavioral or the social sciences. Part of our course centers around causality and testing causal models. Along the way we will encounter maximum likelihood estimators, Likelihood-Ratio Chi-Squares, and diverse fit estimates. This material requires familiarity with at least one course past the basic introductory statistics class (e.g., multiple regression, ANOVA, the General Linear Model or structural equation models).  We will use the SPSS statistical package.

By the end of the semester, you should be able to set up, test and interpret:

   --loglinear and logit models
   --logistic regression equations for both binary and multinomial dependent variables

• You should be more familar with other methods for incorporating non-numeric dependent variables such as probit analysis or hazard models.
• Your ability to read materials in this area should be considerably enhanced.

Alas! Simply reading materials does not confer peace of mind, let alone a healthy respect for everything that can go wrong. Until you try basic applications, these techniques are difficult to understand or evaluate. Once you work an exercise, the materials become much clearer. You will complete four short exercises designed to familiarize you with terminology, basic concepts, and computer techniques. Finally, you will analyze data and write a short paper. A handout will set the fundamental parameters for the analysis paper. Several datasets are available to you or (encouraged) you may use your own data.

It is insufficient to simply report results, results are interpreted. Did your original substantive hypotheses receive any support? Were they unequivocally rejected? Did they make sense in your causal system? We will address causal issues several times throughout the semester.
 
 

ELEMENTARY CAUSAL EXAMPLES 101: Did you know?  Looking across months of the year, ice cream consumption and criminal assaults rise and fall together.  Students with lower grades tend to smoke more cigarettes. The storks return to nest in Sweden just as the number of births rise during the year. Does eating ice cream cause criminals to assault others? Does smoking cigarettes literally stupify you? Do storks really bring babies? Stay tuned. Statistics never, ever "prove causality." However, certain patterns of results will be more consistent with hypotheses than others. And, you will discover, certain numeric patterns of results can be the same for "real" or substantive findings--and for fake or "spurious" findings.

MAIN TEXT: Alan Agresti, An Introduction to Categorical Data Analysis. (Wiley, 1996; ISBN = 0471113387)
 
 

NOTE: A more recent and expanded version of Agresti, which is more mathematical, can be found at: Alan Agresti, Categorical Data Analysis. SECOND edition, 2002. ISBN = 0471360937

Other readings from: Nigel Gilbert, Analyzing Tabular Data: Loglinear and Logistic Models (1993; ISBN = 1857280903) and

Online Course Guides

The Agresti book is ordered for Bill's and the FSU Bookstore. It is quite possible that you can find used copies online at Amazon and other sites for a good deal. I will make copies of the Gilbert chapters for you.

ALL MY COURSE LECTURES will be placed on the Internet and linked in with each course topic.

Course guides will be keyed to the readings. See the top of each Guide as it is posted.
The lecture urls have the general form of:

Please type in course urls EXACTLY. There is no "www" in these urls.

Some material in the guides will be covered during class. However, we will also use class time for instruction related to each exercise, demonstrations, presentations and feedback.

Here is information about  assignments, due dates, and course weights.

There will be four short equally weighted exercises.  While each exercise will focus on the immediately prior units, please be advised that this material is cumulative in nature.

Exercises have several purposes:

To familiarize you with terminology, basic operations, and associated computer programs.

To help practice your basic analysis and results interpretation skills. For example, logistic regression coefficients are often used and equally often misinterpreted.

To alert you to common problems that occur with different kinds of analyses and ways to solve these problems.

All four exercises put together will count a total of 40 percent toward your final grade.

Details on each assignment are posted to our course WEB site prior to the due date.

As exercises and exercise feedback sites are created and posted, watch the space at the top of the Guides for information and links.

An analytic paper will count 60 percent toward your final grade.

In the paper, you will analyze data using course material and interpret your findings. The general format of the paper will resemble a journal article.

You may use your own data, data from your major professor, or one of the many data sets I have available. There are many databases online.

I use plus and minus grading, throughout and for final grades.

If I think you are having trouble with the material, I will alert you immediately and I expect you will seek remedial help as quickly as possible. If you receive such an alert, please take it very seriously. Please do not tell me that you "really understand the material" and fail to seek help. I issue such alerts when the work makes it appear that the student DOES NOT understand the material.

I use plus and minus grading for final grades.

Our course is WEB assisted through the CourseInfo/Blackboard 6.0and WEB-MC  systems at FSU. You MUST be registered for edf6937-01 to access our Blackboard site. To access our course through Blackboard, here is what to do. Go online to:

(You will be forwarded to the new, more complicated url <http://campus.fsu.edu/webapps>. The above works and is easy to remember.) Enter your GARNET username (USERNAME ONLY!) and password to log in. For example, I would enter "slosh" ONLY and omit the "@garnet.acns.fsu.edu" part. Then click on:

If you DON'T have a garnet account, you need to get one NOW. Go to the Academic Computing and Network Services website (address below) and follow the links to register online for your garnet account at FSU.

You need a garnet or mailer account to log into BlackBoard.

Our course can also be accessed directly through the WEB-MC system. Go to:

(That's THIS WEB address.)

You can link to nearly all the course sites from this central location.
 
 

I will use WEB-assist for several course features:

• All lectures will be available online and linked with course topics. Objectives will be posted for each section of the course as the semester progresses.

• We have a communications section in Blackboard which allows course members to post questions and initiate discussion. This can be particularly useful immediately prior to exercises.

• Exercises will be posted to our course site. Generic feedback on assignments will be posted. Please read this feedback before you ask questions about your personal written work because these sites may answer many of your questions.

• I DO NOT ANSWER QUESTIONS ABOUT PERSONAL EXERCISES OR THE PAPER DURING CLASS. This includes class break. PLEASE DO NOT EVEN ASK. There is not enough time for me to thoroughly reread over your materials and ask you questions about it. This is not fair to you, to me, or to your classmates. However, I will be glad to make an appointment to discuss your exercise or paper with you or we can speak after class.

• Announcements on the main Blackboard page will alert you to any changes in the nature of exercises or to the schedule for the syllabus, course deadlines, or class meetings. Also watch the top of the sites for such announcements.

There may be some variations from this syllabus. Please check back weekly and watch Blackboard for any announcements.

DATES	TOPICS TO BE COVERED	ISSUES AND OBJECTIVES
January 12	Introduction: Issues in Modeling Causal issues in experimental, nonexperimental and observational data and their implications for models	Navigating our course web sites What are the fundamental analytic problems? What (to anticipate) are some solutions? Basic casual issues Review of types of data
January 19-26	Review General Linear Model; issues of basic terminology  Introduce terminology  (Odds-Ratios; MLEs; iteration; Chi-square as goodness of fit);  The basic two-way (2 X 2) cross-tabulation table recast	What are important aspects of the GLM? What are the building blocks and terms of multivariate analyses of non-numeric data?
February 2-9	Basic loglinear models.  A probabilistic model for table cell counts. Poisson and multinomial distributions. Begin with two way table, extend initially to three way (2 dichotomous, 1 not, then to multinomial). Frequency equations; transformed to log-linear equations.	What do loglinear models "look like"? What is a general cell frequency model? Show me an example!
February 9	EXERCISE 1 DUE	Basic terminology exercise
February 16	N-way tables Model construction and model testing in the loglinear model	Extending the model to "n dimensional" cross-tabulation tables How do I test my loglinear model? Which parameters can I drop?
February 23	Introduction to programs and analysis:  SPSS and “Model Selection” and the “General” program.  Examples and demonstrations (output interpretation)
March 2	EXERCISE 2 DUE	SPSS program 1 and writing a loglinear equation
March 2-9	The logit model.  Transformation from the loglinear to the logit model.  Transform equations to logit model.	What is a logit model? What are the advantages to a logit model? What's the relationship between a loglinear model and a logit model?
March 6-10	No class--Spring Break
March 16	Issues in testing logit models.  The SPSS “Logit” program and demonstrations
March 16	EXERCISE 3 DUE	SPSS Exercise: Nonhierarchical Loglinear models
March 23 - 30	The special case of logistic regression. Dichotomous and polytotomous dependent variables.  Poisson, binomial and multinomial regression More programs: The SPSS Multinomial logistic regression program	What's the relationship between the GCF and logit models and logistic regression? How do I handle dependent variables with more than two categories?
March 30	EXERCISE 4 DUE	Loglinear to logit transformation Running a logit model program
April 6	AERA & National Association for Research on Science Teaching  (NARST)	No class
April 13	First draft analytic paper Presentations	PRESENTATION INFO
April 13-20	Various extensions and special cases:  Model Fitting Ordinal response variables versus nominal models;  Quasi independence;  Structural versus sampling zeros	When Chi-square just isn't enough Many many measures of fit Are there advantages to an ordinal dependent variable? What about cells with no cases? What are some other extensions?
April 25 3 PM	Analytic paper due Presentations	Paper Guide

A LECTURE (AND ASSOCIATED MATERIALS) WILL BE LINKED WITH EACH TOPIC AS THE SEMESTER PROGRESSES.
 
 

READINGS AND ASSIGNMENTS

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There may be some minor changes as the semester progresses.
Your patience is appreciated.
Susan Carol Losh
January 4 2006