PICTURES FROM RESEARCH POSTER SESSION MAY 3, 2004

COURSE GOALS
The purpose of this course is to help you acquire certain specific competencies. They are as follows:
*  To understand the conceptual improvements heralded by multilevel models, as compared to contextual analyses for nested data.
* To develop an appreciation of how multilevel models can be applied to various criminal justice and criminology matters.
* To prepare data through spss for contextual regressions, and to carry out and interpret contextual regressions
* To prepare data for multilevel analysis via HLM, and to carry out and interpret the results of those multilevel models.
* Ability to interpret tables of results from journal articles using multilevel models.
* Ability to prepare a journal length article, including all stages of paper preparation, based on empirical analyses you conduct.
* Ability to professionally present your work, either in a presentation session or a poster session. 
* While doing the above, to learn some substance about current empirical work on effects of crime and community characteristics on both reactions to crime and psychosocial indicators. (This is not a competency per se, but rather content.)
* To gain experience with secondary data processing and analysis.

 BASICS

The Focus

Statistical
Hierarchical Linear Models (HLM) or Multilevel models (MLM) represent a significant advance in social scientists' ability to understand how outcomes are affected by context, how individual and contextual factors interact, how outcomes change over time, and how to summarize results from a series of studies. We will be concentrating in this course largely on the first two uses of HLM. We will get to the other two uses of HLM - to investigate changes over time, and to summarize studies -- if and as time permits. I am optimistic. These models address a range of theoretical and methodological issues relevant to criminal justice, sociology, psychology, urban studies, education, and political science. The issues include multilevel analysis, aggregation issues, contextual analysis, and clustered samples. In simple, whenever the individual units of study (e.g., students) are nested within a higher level unit (e.g., schools), HLM is an appropriate, and some would argue the most appropriate form of analysis.

HLM refers both to an analytical technique, and a specific software program. There are other multilevel software programs available, like MLWin. In this course we are using HLM.

There are questions about whether MLMs are just a "fad" right now in criminal justice and criminology research. I don't think so, for the following reason. First, many problems in research and evaluation in criminal justice are most amenable to, and only amenable to, a multilevel approach. Offender or delinquency careers represent cases in point. In addition, the interaction between the lower level unit and the higher level unit (e.g., the officer and the police department) is fundamental to numerous theoretical and policy concerns, and MLMs provide a systematic way to approach these.

Here are some examples of "units nested within larger units" in criminal justice evaluation or research:

Level 1 units	Level 2 units
Residents	Different Neighborhoods
Police Officers	Different Precincts
Police Precincts	Different Police Departments
Cases Sentenced	Different Judges
Prisoners	Different Prisons
Sentenced Drug Offenders	Different Drug Courts
Juveniles	Different Treatment Programs
Decades	Neighborhoods

At the same time, I do NOT think MLMs are going to "solve" all or nearly all of our analytic problems. That would be asking too much. In fact I think many may become disenchanted with MLMs because the answers they provide may not be to our liking. We have many theory and policy ideas around the interaction between person and context. But MLMs may often find that these interactions are nonexistent or trivial.  Alternatively, I think MLMs can help "push" us in our theorizing, moving us to think in more detailed ways about processes.

In this class we use  examples from criminal justice and other disciplines. 

The goals of this course revolve around different sets of activities. These activities break into two clusters. The first is concerned with general, doctoral student skills: completing secondary analysis, and writing research papers. You will be asked, in the beginning of this course, to work with the PHMC 2002 Survey files. You will then check these files, and learn about them. These are exactly the steps you take whenever you begin a secondary data analysis project. In order to write your research paper,  you will be learning about research in an area. I am going to provide you with starter references in each of several  areas we can address with the PHMC data files. I expect that you will complete additional research on your own and find additional references to be incorporated into your work. The references provided are just starters. If you do not know how to conduct Social Science Citation Index Analyses to "grow" your reference list, then let me know, and we will schedule a session for folks.

The "area" we are working in is, generally, crime and other community factors and how they influence a range of psychosocial and/or health related outcomes, and what roles various social dynamics may play in some of these models. Sounds vague. Don't worry.

The second cluster is specific to course content: carrying out and interpreting contextual regressions; understanding the fundamentals of HLM; and carrying out and interpreting HLMs.  In addition, we will be reading a small number of articles using HLM, so that you become familiar with interpreting HLM tabular output.

We will be having some lab time. Not sure at this time exactly how much. There is a student version of HLM you can acquire. Encourage you to do so. Details below.

Topical

Our conceptual focus will be impacts of  Philadelphia neighborhood characteristics, including crime rates, on either adults or the elderly population. We will be using the Philadelphia Health Management Corporation (PHMC) 2002 household survey. This is an RDD household survey which was completed in summer 2002. It includes the five counties on the PA side of the metro area (Philadelphia, Montgomery, Bucks, Chester, Delaware), but we are going to be concentrating here on a set of Philadelphia neighborhoods used by PHMC. We are sticking with Philadelphia for three reasons. First, if you are in the Geography of Crime course, you will be learning about Philadelphia there as well. Second, we can provide you with neighborhood crime rates for these locations. Third, since these neighborhoods are nearby, I encourage you to go out and visit specific locations -- being sure of course to check with me beforehand about the safest way to do this.

Potential outcomes that might be affected by community crime rates, and are available in the PHMC data files include:

behavioral restriction
social capital (sense of community, social climate)
health status
stress
household gun possession
depression (for elderly file only) 
fear
days exercising

We also are interested in roles various social dynamics may play as predictors or mediators, as well as outcomes.

Assumptions

I assume you understand the basics of OLS multiple regression, including:

variance
covariance
correlation
scatterplots
R squared
adjusted R squared
F test of R squared
b weights
standard errors of b weights
beta weights
t tests of b weights
constant
residuals
predicted scores
residual diagnostics
tests for linear vs. curvilinear impacts
coefficient of alienation

In addition, I assume you know your way around SPSS for Windows. This includes being able to write syntax boxes, and save them, and diagnose what is happening with them.

If any of the above terms is unfamiliar to you, you have some remedial work to do!.

Readings and books

You will be reading books, articles, and handouts. The two required books have been ordered. Of course, although I officially recommend the College Bookstore for all your collegiate purchases, you also may find you can save money either through Amazon.com or through ssicentral.com's bookstore.

In addition to the books indicated below, there are handout note files prepared by the instructor (see handouts link), and additional works completed in the area. 

You also will be reading many articles in your interest area, once you have selected one.

REQUIRED BOOKS 

 Stephen W. Raudenbush, Anthony S. Bryk  (2002). Hierarchical Linear Models : Applications and Data Analysis Methods. SECOND EDITION Thousand Oaks: Sage. This book costs a lot but is is a crucial reference.

Stephen Raudenbush, Anthony Bryk, Yuk Cheong, Richard Congdon (2000). HLM 5: Hierarchical Linear and Nonlinear Modeling . Chicago: SSI Scientific Software International. This is the program manual.

J. J. Hox (1995) Applied Multilevel Analysis. Available on the web at http://www.rbtaylor.net/hlm_applied_hox.pdf [This is over a hundred pages. You want to save this to disk. I may be making reference to this material from week to week. I like the discussion about connecting across levels.  There is some useful, basic material here. Again, you will need to be careful because his Greek differs from the other Greek you will see in other places. Please do not print this all out - just print out pages as you need them.

RECOMMENDED BOOKS

These are recommended in that they provide additional detail; they are not necessarily oriented to those who are looking for more basic help.

Ita G. G. Kreft, Jan De Leeuw (1998). Introducing Multilevel Modeling (Introducing Statistical Methods) Thousand Oaks: Sage. [Chapter 2 is a nice review of contextual models. There are some nice graphical examples of varying slopes. The text uses a different program than we are using, so the programming examples are not that helpful.

Tom A. B. Snijders and Roel J. Bosker (1999). Multilevel analysis: An Introduction to basic and advanced multilevel modeling Thousand Oaks: Sage. [More "advanced" [confusing?] than the above book. Good discussion of sampling issues, however. Uses Greek alphabet differently than HLM program does. 

ARTICLES AND NOTE FILES

Articles. We are going to be reading a good number of articles and handouts. These will illuminate uses of HLM, or related and important conceptual questions. Some of these are on the web site, some on the CD I will hand out, some you will need to look up.
Handouts.  The handouts are a series of HLM lecture notes you get directly off the website. 

Class Structure

You will complete the assigned readings on a weekly basis and come to class prepared. To help you prepare there are questions to go along with the article readings. You want to write answers to those questions after you have read them. Some weeks I will announce you can receive extra credit by turning in written answers to questions - but you will not get notice beforehand. Your answers should be typed, double-spaced, with both your name and SSN and due date on the top of the page.

You will complete and write up a number of analytical assignments; the purpose of each is to conduct a specific analysis, and write it up. You will receive full credit for turning in each of these on time provided you make a serious, good faith attempt to complete the assignment.  In addition, some assignments ask you to turn in a draft of a portion of your final paper.  Each of these assignments will build toward a broader, final paper. These papers will, hopefully, represent building blocks that can later be put together to form a paper of publishable quality. In class I will tell you why it is important to produce something like this.

The range of paper selection topics will be sent around and discussed the second week of class.  There may be some latitude for students going outside the PHMC data file and working on a different data source. but you will need to talk to me beforehand

The weekly assignments are to be typed, double-spaced with the due date your name and SSN at the top. These are not going to be graded, but you will get feedback on how to improve. Be sure to spell-check and grammar-check your papers.

The weekly readings provide the needed conceptual background for carrying out the work assigned, and for understanding how MLMs are advancing scholarship in criminal justice and criminology. Thus it is important that you keep up with the readings. They provide not only examples that help us decode MLMs, they also provide very interesting background for the conceptual work.

You will notify me beforehand if it is absolutely essential for you to miss a class. Given the amount of ground we must cover, a missed class may create a significant burden for your learning curve. If you do miss a class it is completely your responsibility to get all handouts, assignments, and so on, that were distributed.

Datasets and conceptual background

Grades

Your grade at the end of the semester will be based on the following:
 

Extra credit - see above

GRADING POLICIES

1. Assignments are due on the date indicated. If you cannot get your paper in to me at class time, please send me an email explaining why, and let's be sure to have a followup chat. The weekly assignments must not only be credible but also handed in ON TIME in order for you to get full credit. Same applies to the final paper.

2.  If I encounter solid evidence of academic misconduct I reserve the right to fail you on the assignment in question, and/or to assign you a failing grade for the course. I will try to state as clearly as I can the ways in which it is acceptable for you to cooperate with one another and network, and the ways in which it is not acceptable.

3. You do have a right to submit any assignment for regrading. (In this course, this means the midterm, final, and paper.) You should state in writing the reason you think you deserve a higher grade, attach that to the original completed assignment, and return it to me. Your grade may go up, go down, or stay the same. I may consult with other faculty members as I deem fit.

GUIDELINES ON AVOIDING ACADEMIC MISCONDUCT

We will discuss in class the nature of academic misconduct, including plagiarism. You are responsible for understanding the different varieties of academic misconduct, and for understanding the Graduate School's policies as described below. If I encounter solid evidence of academic misconduct I will discuss the matter with you, and then deliver the consequence I deem appropriate. Possible consequences include: failure on the assignment in question (i.e., a 0); assigning a failing grade for the course; or attempting to have you expelled from Temple University. Should you wish to contest a decision I make on academic misconduct, I will inform you of the procedures to follow. The department and the college have fully specified grievance procedures for graduate students. 

The following materials are from the University's Graduate Bulletin statements on academic honesty of about two years ago  [http://isc.temple.edu/grad/Bulletin/Default.htm] Even though I can no longer find this in the current Graduate School Bulletin, it still applies.

Academic Honesty

Temple University believes strongly in academic honesty and integrity; therefore, any kind of academic dishonesty is prohibited. Essential to intellectual growth is the development of independent thought and of a respect for the thoughts of others. The prohibition against academic dishonesty is intended to foster this independence and respect. Primarily, the two types of academic dishonesty include the following: Plagiarism and Academic Cheating.

Plagiarism is the unacknowledged use of another person’s labor, ideas, words, or assistance. Normally, all work done for courses — papers, examinations, homework exercises, laboratory reports, oral presentations — is expected to be the individual effort of the student presenting the work. There are many forms of plagiarism: repeating another person’s sentence as your own, adopting a particularly apt phrase as your own, paraphrasing someone else’s argument as your own, or even presenting someone else’s line of thinking in the development of a thesis as though it were your own. All these forms of plagiarism are prohibited both by the traditional principles of academic honesty and by the regulations of Temple University. Our education and our research encourage us to explore and use the ideas of others, and as writers we will frequently want to use the ideas and even the words of others. It is perfectly acceptable to do so; but we must never submit someone else’s work as if it were our own, rather we must give appropriate credit to the originator.

Academic Cheating is, generally, the thwarting or breaking of the general rules of academic work or the specific rules of the individual courses. Some examples include: falsifying data; submitting, without the instructor’s approval, work in one course that was done for another; helping others to plagiarize; or cheating from one’s own or another’s work; or actually doing the work of another person.

The penalty for academic dishonesty can vary from a reprimand and receiving a failing grade for a particular assignment, to a failing grade in a course, suspension, or expulsion from the University. The penalty varies with the nature of the offense, the individual instructor, the department, and the school or college.

For more information about what constitutes Academic Dishonesty or about disciplinary and/or academic grievance procedures refer to the University’s Statement on Academic Honesty and the Student Code of Conduct or contact the Student Assistance Center, 215-204-8531.

Load
This course ends up being somewhat more demanding than some other graduate courses for some students. In short, for some of you, this may "feel" like a four credit or a six credit graduate course. Try to plan your weeks (and weekends) accordingly.

Software
For HLM we will use a specific program, put out by Scientific Software International. The main web page for SSI is SSICENTRAL.COM. This is a useful website, because you can look at the examples, and get help interpreting HLM output. I STRONGLY ENCOURAGE YOU TO GO THROUGH ALL THESE EXAMPLES ONCE WE ACTUALLY START WORKING ON HLM. Here is the link:

http://www.ssicentral.com/hlm/example.htm

The department has bought a site license for this program. This means that you need to do your work in the department on their computers if you want to have access to the FULL version of the program

There is, however, a STUDENT version of the file that you can download for FREE. Go to this address to learn more:

http://www.ssicentral.com/other/hlmstu.htm

There are, however, a number of restrictions. Most importantly, you can not run very complex models.

From that page, here are the restrictions they note:

The student edition can run all the analyses the full version can in terms of models selected, statistical options and output.

Restrictions are, however, placed on the data used and the size of the model selected. The following restrictions apply in this edition:

The student edition can run all the analyses the full version can in terms of models selected, statistical options and output. Restrictions are, however, placed on the data used and the size of the model selected. The following restrictions apply in this edition (ACCORDING TO THE WEBSITE):

• The DBMSCOPY utility used for the importation of data is not included. The student edition will only accept ASCII, SYSTAT, SPSS for Windows or SAS transport data files.
• For a level-3 model, the maximum number of observations that may be used at levels 1, 2 and 3 are approximately 7500, 1700 and 60 respectively. Note that the restriction applies to observations in the case of the level-2 file, for example, and not to actual number of level-2 units to be included in the analysis.
• For a level-2 model, the maximum number of observations at the two levels are 7200 at level-1 and 350 at level-2 of the hierarchy.
• No more than 5 effects may be included in any HLM equation at any level of the model, and the grand total of effects can not be 25 or higher.

When these limitations are exceeded, an appropriate error message will automatically be displayed.