CJ 8305 / (formerly 605) / CRN 35098


An Intellectual Virtues Approach

R. B. Taylor

Main Course Page:

Instructor Home Page:


The instructor espouses the following mission statements for this course. These statements are a work in progress and may be modified

(1) To promote dialogue and reflection about what it means for you, the student, to grow as a scholar who works simultaneously on strengthening your intellectual virtues, and on using the content of this course to advance your preferred areas of social science inquiry.

(2) To promote dialogue and reflection about what it means to be an effective teacher in the context of a course like this with demanding technical material.

(3) To help you grow your ability to plan and run mixed models, to decode the details and implications of results, and to grasp the specifics and significance of mixed model results presented in the social science literature.

All users : See LEGAL link below.  Your use of these pages explicitly implies you have read and understood the legal conditions stipulated.


2/8/2018 - changed specific weekly readings for some weeks

1/21 - IV self assignment part 1 due 1/22 added to event pages

Submodel summaries605_sp18_model_summaries.pdf [not active yet]






Steps on getting my project into focus

Submodel summaries

Self-reflection assignment part 1

PowerPoint uploads post-class






Instructor R. B. Taylor (GH 536-7)
Time and Place MONDAY 3:00 - 5:30 (+/-) GLADFELTER CLASSROOM & 5TH FLOOR LAB - plan for an extra hour of lab time most weeks
Office Hours [MAY CHANGE] Tuesday 10:30-12:30 and by appointment

TEL: 215.204.7169 (v). You also can ring 1-7918 and ask Ms. Clark if we need to chat and the phone is not being picked up; ask her to leave a message for me
EMAIL: tuclasses at
The syllabi powers require I include my Temple email on the syllabus. Here it is:
ralph.taylor at But please do not use it because it is likely to get hopefessly lost in the 14,000 emails that I have not yet deleted.

Canvas and Website Updates Here:

The Big Picture

Why this Family of Models?

Why are we spending sooooo much time on this one family of statistical models?

You may say: There are lots of other statistical models I would like to learn about. I have heard about things like principal components analysis, factor analysis, time series models, latent variable models, growth curve models, multidimensional scaling, seemingly unrelated regression, spline regression, ridge regression, cluster analysis, chi square automatic interaction detection, boosted regression, doubly boosted regression, machine learning, spatial error regression, and Full Bayesian models. Why can't we talk about that stuff?

My answer is this, paraphrasing Eric Baumer (2012). Two fundamental questions in both criminology and criminal justice are how outcomes of interest, or how dynamics of interest, are conditioned by either space or time. Some scholars have argued (Taylor 2015), that for some areas of criminology like community criminology, there are foundational questions about space and time, or both, that the field has not yet figured out but really should try to answer. .MIMUHIE models provide us with tools to address these foundational questions.

More broadly, these models provide a flexible tool kit. They are widely applicable to a range of theoretical, policy, and evaluation questions in criminal justice, criminology, and a number of other social science disciplines.

Further, these models are currently widely used in criminal justice and criminology. If you are in a "top ten" doctoral program in criminal justice or criminology you need to know your way around these analyses.

Competencies and Promises

This course helps you grow your social science competencies in three specific ways.

(1) You will grow a deeper understanding of how multilevel/mixed models advance scientific understanding. You achieve this goal by closely reading, rereading, and discussing research articles using these models; by engaging with texts explaining how these models work and how they fit into important theory, practice, and policy issues in criminal justice and criminology; and by talking and thinking hard, in class and outside of class, about all this.

If you want to meet this goal, you will adopt an open minded approach in the following way . Although some of the empirical articles you read will be in topic areas in which you have an interest, others will be outside of your interest areas. Being open minded means approaching all these materials, regardless of how they do or do not line up with your preferred topics, with the mindset that there is something important going on here and that it could be important for you to learn about these isues in depth.

MIHIMU models provide you an alternate approach to any topic area.

How will you know if you have met this goal? (a) If you can frame questions in a topic area whose accurate, least misleading answer can only be derived by using a MIHIMU model; and (b) you can readily and correctly "decode" results presented in an article using MIHIMU models, including evaluating whether the interpretation the authors provide is correct.

(2) You will gain expertise "running" these models, by working on a dataset of interest to you/your advisor, and by working on a class example data set. More specifically, you will understand the connections between how you structure a data set, the questions you pose to the data set, the program you write to run the MIMUHIE models, and the results you create.

How will you know if you have met this goal? (a) If you can write programs to create each of the four types of sub models, and the full model. (b) If you can explain how a particular way to organize your data does or does not facilitate asking a particular question. (c) If you can explain how a particular type of sub model best addresses a particular question, and write the corresponding programs.

(3) You will become more proficient at interpreting multilevel/mixed models by interpreting output generated by you or others.

How will you know if you have met this goal? (a) If you can interpret pretty much all the things that you see in a printout for each of the MIMUHIE sub models presented, and for the full model. This means being able to describe in specific terms what each output feature is telling you about the data examined.

How You Will Get There

To achieve these goals you must do work. The corresponding activities for the above goals are as follows

(1) You will read assigned and self-selected articles on a regular basis. You will take notes, and write short reflections. For the assigned articles, you will come to class prepared to answer questions. For the latter, you will come to class prepared to present. You also may read from textbooks, online sources, and other places, about these models.

And, yes, you can spend time on Youtube. Enter terms like "introduction to multilevel modeling" or "mixed effects models"+Stata. Please let me and your classmates know which links you find helpful. We can put up a set of links if you want. Just be aware, some of the videos are "legacy" models, that is, they describe somewhat older commands that may have been superceded in later Stata versions. Also be aware, the sound of Chuck Huber's voice may not make you feel peaceul and relaxed. Then again, it might.

But, please avoid Wikipedia. That should be one of your graduate school mantras.

(2) You will run programs, modify data sets, and interpret results, on a routine basis. You will learn how to document what you have done, so it is replicable. Remember Professor Ward's mantra: "If it's not replicable, it's not scientific." You will learn how to create an organized workflow. In short, you will spend a lot of time getting to know the Stata software, and specifically how its MIMUHIE models work, and document what you have done

(3) You will acquire this competency by thinking about, and discussing the articles we read; by pondering the multilevel results you obtain; and by working through example results presented in class.

Most importantly, you are going to draft an empirical research paper that uses MIMUHIE models.

How The Instructor Will Help You Get There

The instructor does several things to assist you.

(1) He provides conceptual background on the different varieties of models you are running, and how these models fit into the broader theoretical contexts in criminal justice and criminology.

(2) He provides detailed reviews of program syntax and program outputs.

(3) He guides discussion of assigned research articles.

(4) He responds to student presentations of selected research.

(5) He answers questions to the best of his ability.

How Will My Learning Be Assessed?

(1) There will be a final exam. In it you will be asked to interpret tables from published journal articles, and interpret output from MIMUHIE models.

(2) You will complete written assignments which will be turned in and graded.

(3) You will complete work in class for which you will receive credit.

(4) You will participate in class by asking questions, answering questions, and presenting materials.

(5) You will be in class. EACH class you miss will affect your letter grade by one letter grade (e.g., A to A-) unless it is an excusable absence.

How and Why Will the Course Embody an Intellectual Virtues Approach?

The course incorporates a meta-theme: teaching intellectual virtues. I do this for two reasons.

Mastering the materials presented in this course is about more than just mastering content. It's about growing your way into your own personal style as an independent scholar who is tough, careful, and open minded when confronted with challenging questions and data, or points of view with which you disagree. I intend to use this course to help you reflect on and develop your own intellectual virtues. As you become an autonomous social scientist developing your own perspective on important theories, policies and practices in criminology, criminal justice, and related disciplines, developing these intellectual virtues will make you a more thoughtful, open minded, careful, and perhaps even courageous contributor to the field.

Of course the flip side of intellectual virtues are intellectual weaknesses. All scholars have them. Even (especially?) the person writing these words. To grow your intellectual virtues you also need to reflect on your intellectual weaknesses. This course provides opportunities to engage in such reflection as you tackle challenges and grow your awareness about how you respond to those challenges.

The second reason is pedagogical transparency. I find the intellectual virtues approach requires me to explain lucidly why I am doing what I am doing in the classroom and when I construct and review work assignments you complete. It prompts me to consider, and then share with you, not just learning goals associated with different topics and assignments, but also broader lessons learned. We can widen the lens.

What Might I Do with What I Learn in This Course?

The short answer is use the tools (conceptual, analytic, statistical) learned here to

a) more intelligently read research or policy articles

b) write papers that you might present or publish,

c) use some of these tools for a dissertation, and,

d) more broadly, become a more capable and thoughtful scholar.

Several times past iterations of this course have resulted in publications based either on research conducted during the course, or later research done collaboratively between students formerly in this course and the instructor. It also has led to numerous students doing dissertations using these techniques.

Those include:

Askey, A. P., Taylor, R. B., Groff, E. R., & Fingerhut, A. (2017 Online First). Fast Food Restaurants and Convenience Stores: Using Sales Volume to Explain Crime Patterns in Seattle. Crime & Delinquency, Online First June 23, 2017 : DOI: 10.1177/0011128717714792.

Blasko, B. L., Roman, C. G., & Taylor, R. B. (2015). Local gangs and residents' perceptions of unsupervised teen groups: Implications for the incivilities thesis and neighborhood effects. Journal of Criminal Justice, 43(1), 20-28. doi:

Garcia, R. M., Taylor, R. B., & Lawton, B. A. (2007). Impacts of violent crime and neighborhood structure on trusting your neighbors. Justice Quarterly, 24(4), 679-704.

Haberman, C. P., Groff, E. R., & Taylor, R. B. (2013). The Variable Impacts of Public Housing Community Proximity on Nearby Street Robberies. Journal of Research in Crime and Delinquency, 50(2), 163-188. doi: 10.1177/0022427811426335.

Link, N. W., Kelly, J. M., Pitts, J. R., Waltman-Spreha, K., & Taylor, R. B. (2017). Reversing Broken Windows: Evidence of Lagged, Multilevel Impacts of Risk Perceptions on Perceptions of Incivility. Crime & Delinquency, 63(6), 659-682.

McCord, E. S., Ratcliffe, J. H., Garcia, R. M., & Taylor, R. B. (2007). Nonresidential crime attractors and generators elevate perceived neighborhood crime and incivilities. Journal of Research in Crime and Delinquency, 44(3), 295-320.

Robinson, J., Lawton, B., Taylor, R. B., & Perkins, D. D. (2003). Longitudinal Impacts of Incivilities: A Multilevel Analysis of Reactions to Crime and Block Satisfaction. Journal of Quantitative Criminology, 19(237-274).

Sorg, E. T., & Taylor, R. B. (2011) Community-level impacts of temperature on urban street robbery. Journal of Criminal Justice, 39(6), 463-470.

Taylor, R. B., Kelly, C., E., & Salvatore, C. (2010). Where concerned citizens perceive police as more responsive to troublesome teen groups: Theoretical implicaitons for political economy, incivilities and policing. Policing & Society, 20(2), 143-171. (The 2nd and 3rd authors approached me, seeking additional HLM experience. I developed the topic, they completed analyses and drafted paper portions.)

Wang, K., & Taylor, R. B. (2006). Simulated walks through dangerous alleys: Impacts of features and progress on fear. Journal of Environmental Psychology, 26, 269-283. (Done as part of a communities and crime course, after taking HLM course.)

Wyant, B. R. (2008). Multilevel impacts of perceived incivilities and perceptions of crime risk on fear of crime. Journal of Research in Crime and Delinquency, 45(1), 39-64.

Further, numerous students have gone on to use MIMUHIEs in their dissertations.

Various Model Names

The models used in this course are variously called

* Hierarchical models (e.;g., Hierarchical Linear Models (HLM), Hierarchical Linear Generalized Models (HLGM)

* Multilevel models (MLM)

* Mixed effects models or more simply mixed models.

* Fixed effects models

* Random effects models

* Generalized estimating equations (GEE not to be confused with "Gee Whiz!")

Despite all these different names, this is basically just one family of models, although different models do different things.

What Do These Models Do?

Generally speaking these models serve the following purposes:

1) For spatially or organizatinally nested data, they separate the contributions of context and the individual unit of analysis to an outcome;

2) For spatially or organizatinally nested data, they specify how context and the individual unit of analysis may interact to affect an outcome;

3) For longitudinal data they separate the contributions to an outcome of time passing and the individual unit of analysis;

4) For longitudinal data they specify how time and the individual unit of analysis m ay interact to affect an outcome.

These models also can be used to conduct meta-analyses, although we will not pursue that topic in this course.

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
Offenses by year Offenders

Note, the nesting can be more complex. You can have

* participants within programs within regions

* students within schools within school districts or

* juveniles over time in different neighborhoods

and so on.

You will note with the last two examples in the box that time is nested.

This is a repeated observation setup.

You will be learning how observations can be nested within the same individual, or the same unit. This means that MIMUHIEs can analyze much of the same data analyzed by repeated measures ANOVA, or even time series. In some cases, depending on the circumstances, MIMUHIEs do a better job of it. MIMUHIEs have become a powerful analytic tool for life course criminology, and there is an avid ongoing debate about trajectories vs. MIMUHIE approaches to change.

At the same time, I do NOT think MIMUHIEs are going to "solve" all or nearly all of our analytic problems. That would be asking too much. We have many theory and policy ideas around the interaction between person and context. But MIMUHIEs may often find that  

Alternatively, I think MIMUHIEs can help "push" us in our theorizing, moving us to think in more detailed ways about processes. For a great example see: Wilcox, P., K.C. Land, and S.A. Hunt. 2003. Criminal circumstance: A Dynamic multicontextual criminal opportunity . New York: Aldine deGruyter.

For questions about whether our modeling capabilities have outrun our theorizing, see: Taylor, R. B. (2010). Communities, crime and reactions to crime multilevel models: Accomplishments and meta-challenges. Journal of Quantitative Criminology, 26(4), 455-466.

In the long run, my guess is that MIMUHIEs will become like SEMs and other general purpose multivariate techniques: very useful in a wide range of situations, but also easily mis-applied.



I assume you understand the basics of OLS multiple regression.

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

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


In past years, this course used a program that ONLY did MIMUHIEs, Raudenbush's and Bryk's Hierarchical Linear Models program. Using that program is no longer tenable for two reasons: it has not been updated, and thus has fallen behind in the options it provides. Further, it can ONLY do MIMUHIEs. Those of you who are going to go on to work in agencies with limited budgets may get money for an all around stat package, but are unlikely to get money for a program that can only do MIMUHIEs.

This iteration of the course we are using mixed models in Stata. This is the third iteration of the course using Stata. Version 15 of Stata is on the lab computers. If it is not on your office computers, you need to request that it be put on. You also might want to spend money buying a student license for Stata/IC in lieu of a textbook.

Why not use SPSS? It also does mixed models. Because SPSS offers fewer options, and is not an extensible programming environment. Users write add-ons to Stata which powerfully extend what it can do. These provide important additional capabilities.

If you intend to become a serious quantitative analyst, you need to AT LEAST learn Stata. Hopefully, you also will go on to learn SAS or R. The latter two are the most preferred general social science statistical platforms.

You need to spend time on your own learning your way around Stata.

Ultimately, this course might move to R. We'll see.....


Readings and Tutorial Links

Free (and strongly recommended) Book!

Here is a free book. Get it. Strongly recommend you read the entire volume, at your own pace, in the first couple of weeks of the semester.

Luke, D. A. (2004). Multilevel Modeling. Thousand Oaks: Sage.
This is available through the TU Library database front end. The database is Sage Research Methods Online.The book is in three sections. You click on the blue bar at the bottom to get different sections to come up. Print each section to PDF and you are there. There is NO difference between the free and paper version.

Many students have found Luke helpful, even though his examples are from political science.

No Required Books

Each week under the "sequence of topics" page you will see pages from suggested readings. You may find these helpul. Those books that appear under suggested are:

Hox, J., J. (2010). Multilevel analysis (2nd Edition). East Sussex: Routledge.
Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata Volume I: Continuous Responses. Third Edition. (ISBN: 9781597181037). College Station, TX: Stata Press.
Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata Volume II: Categorical responses, counts and survival. Third edition (ISBN 9781597181044). College Station, TX.: Stata Press.
(page numbers across the two volumes are sequential, so I do not list which volume in the weekly sets.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.
Snijder, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd Edition). Los Angeles, CA: Sage.
Twisk, J. W. R. (2013). Applied Longitudinal Data Analysis for Epidemiology (Second ed.). Cambridge (UK): Cambridge University Press

Additional Comments on Books Under Suggested

All of these books, except for Twisk, are available on a limited basis, and pricey, so you may need to go to used book collators like ABEbooks

Hox, Joop, J. 2010. Multilevel analysis (2nd Edition). East Sussex: Routledge.

I see a lot of things I like. (a) The writing is clear. (b) He jumps in right away with conceptual and statistical issues about aggregation and micro-macro connections, which are key parts of the conceptual background. (c) He presents a model building strategy (his chapter 4) which is clear. (d) He gives a lot of attention to power analysis issues, which probably will not matter to you right now, but may in fugure. But (e) most importantly, he frames the entire enterprise as an extension of multiple regression.

Snijders, Tom A.B. and Roel Bosker, J. 2012. Multilevel analysis: An Introduction to basic and advanced multilevel modeling (2nd edition). Los Angeles: Sage.

Tom Snijders is one of the smartest social science statisticians in Europe (think Richard Berk for the Netherlands).

The advantages of this book are (a) the writing and examples are clear, (b) you can sometimes find things in this book that are not elsewhere, and (c) sometimes he helps you work through some simple math in figuring things out. The disadvantages of it are (d) it is still pretty mathematically "dense" in many places (equation scare sets in), and (e) it is not written specifically with reference to Stata software.

Additional Books You May Wish to Consider on Stata in Future

With so much available online about Stata (see below), you do not necessarily need an additional text to help you get oriented to Stata. But if you find yourself getting committed to Stata after this course is done as an analytic and data management environment, you may want to look into these. These are available at the Stata bookstore.

But, if you are interested here are a few ideas.

Kohler, Ulrich and Frauke Kreuter. 2012. Data Analysis Using Stata (3rd edition). College Station, TX: Stata Press.

Reasons to like this book: it emphasizes using do files from the beginning, it has useful chapters on working with do files (2), how Stata works (3 - the grammar of Stata), recoding data (5), and reading and writing data (11)

Acock, Alan C. 2012. A Gentle Introduction to Stata (Revised 3rd edition). College Station, TX: Stata Press.

Reasons to like this book: clearly written, has cross references to many useful Stata add-ons, and shows in mousing through the menus creates specific commands for do files.

Mitchell, Michael N. 2010. Data Management Using Stata: A Practical Handbook. College Station, TX: Stata Press.

Reasons to like this book: has lots about data checking and data recoding. The steps he goes through are what you should do any time you download an archived data set.


You will read assigned articles which use MIMUHIEs. You also will be finding and reporting on other articles using MIMUHIEs.

The assigned articles will be on CANVAS. The week they are assigned is indicated on the sequence of topics page.

For class each week you will want to read each assigned article, and try and answer the questions posted for the reading. {NOTE: the question page is evolving, so don't worry if you do not see questions yet for articles later in the semester.] You should expect to be able to discuss the reading when you come to class, or, at the least, say specifically where you are confused.

Most (but not all) of the articles are substantive and conceptual.

Stata Video Tutorials

Stata has posted a large number of video tutorials on Youtube. The list of tutorials is found here:

If you are new to Stata I suggest you spend time, sitting at a computer with Stata, going through the following tutorials:

Stata basics: all tutorials

Data management: tutorials for copy/paste data; Import Excel

Descriptive statistics: the first two (descriptives, tables) tutorials

UCLA Stata Tutorials and Related Items

IDRE at UCLA has a lot of Stata help. For the Starter Kit go to:

Under the heading "Getting familiar with Stata" you will see a link "Class notes with movies"

If you click here it takes you to a list under "class notes." NOTE - you have the option of viewing/printing out the class notes, or watching a movie of the same material.

You are STRONGLY encouraged to work you way through the first four of these if you are new to Stata:

* entering data
* exploring data
* modifying data
* managing data

"Working through" means following along doing the exercises.

Note - below the list of class notes there is a place where you can download all the data files used.

Back on the

page you also will see a link to "annotated output." Click on this and it takes you

You will find annotated output for:

* summarize
* correlation
* regression

You are strongly encouraged to print out and read carefully through the regression example, so you can see how this differs from what you have learned about regression in SPSS.

You can always get to any Stata help file if you go here

Click on "Statistics" then click on "Mixed models"

Stata as installed on campus at TU provides you access to help files.

But this link is nice because it means when you are away from the Stata program you can still work your way through Stata help files.

Rodriguez at Princeton

If you are looking for some refresher material on regression using Stata, at the top of the above page there is a link to the logs for Rodriguez' general linear model (regression course)

and from there you can link (see tab at top) to his lecture notes

Centre for Multilevel Modeling, University of Bristol

For over two decades, this centre in the UK has been at the forefront of conceptual and programming development in MIMUHIEs. They have developed their own versy sophisticated MIMUHIE. The program is called MLwiN.

They also have an online course you can work your way through if you want. It is free, but you need to register. The registration takes some time, and the Centre will not sign you up immediately.

Click on "Table of Contents" to see what is here.

Class Structure

You are doing two types of reading on a weekly basis. You will be reading about how to run and interpret MIMUHIEs in Stata. You will be reading under "suggested readings" or you will be exploring resources on your own. You also will be reading ASSIGNED articles that apply MIMUHIEs.

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 conceptual article readings. You want to write answers to some of those questions after you have read the articles. If it turns out that discussion in class is lagging because a significant number of students are not doing the readings, and not answering the questions beforehand, I will revise the class grading structure to reduce the weight of other components and add a required number of written answers that get graded.

You will be working with two types of data in this class: the dataset you and your advisor select, and a dataset that I provide to illustrate examples.

Once we get rolling you will be presenting regularly on what you are finding with your dataset.

Writing Resources

You will be writing up different assignments based on your data set. You might be able to use these different assignments later on as the components of a complete research paper.

Here are some suggested resources.

For guidelines on writing an empirical research paper go to:

The link below has some pages from: Payne, L. V. (1969). The Lively Art of Writing. New York: New American Library.

This should help you structure your writing.

For more on the ecology of writing and how to write sections of a social science journal article, many students found this helpful:

Silvia, P. J. (2007). How to Write a Lot. Washington, DC: American Psychological Association.

For an excerpt from this book CLICK HERE

For some serious help on writing mechanics you may any of the following helpful. These are dirt cheap on .

You need to learn how to be a serious critic of your own writing style and mechanics. These books can help. Mechanics cover everything from spelling and basic grammar to how you organize your paper and stylistic issues.

Warriner, John E., & Griffith, Francis (1969). English Grammar and Composition: Complete Course (Revised edition with supplement). New York: Harcourt Brace Jovanovich. [Be sure you get "complete course"]

Hodges, John C. & Whitten, Mary E. (1977). Harbrace College Handbook. (8th Edition) New York: Harcourt Brace Jovanovich.

Strunk, W., Jr., & White, E. B. (1979). The Elements of Style (Third ed.). New York: MacMillan.

Provost, G. (1994). 100 Ways to Improve Your Writing. New York: Signet.

To quote Stuart Little, "A mis-spelled word is an abomination." So is a misused word. CLICK HERE


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

10% Class participation. To get this you need to answer questions. There is cold calling in this class. Ask about my "take a pass" policy.
15% In-class presentation of your final project. Figure you are doing a 10 minute presentation with PPT slides. Scoring rubric for this will be distributed later.

Interim products and tests. This is a grab bag of different things. There will be some in-class quizzes. These are listed on the events page, although the listing and dates may change. (Check the page frequently; I will notify you if I shift things.) I may ask you (as a group or groups or individually) to come to class with something written, and I may then ask you to present it in class. This might be an interpretation of a printout, an interpretation of an article from a journal article, or something else. These will come up on a week-to-week basis, so you will have at least a week to prepare. Although the quizzes may be scored, the other items will probably be on a pass/fail basis.

30% You will turn in a number of short, written portions of a research paper based on the data set on which you are working. Each of these will be graded simply on pass/fail, i.e., full credit / no credit. If your submission receives no credit you come and talk with me, and submit a revised version. Each portion can be revised once. This portion of your grade is based on the fraction of written portions, whether draft or revised, that pass. There will probably be somewhere between 2 and 6 paper portions.
30% Final in-class examination.

NOTE. You are expected to be present at EVERY class for the ENTIRE class period with your CELL PHONE OFF. EACH CLASS YOU MISS COSTS YOU A LETTER GRADE.

If the class you miss happens because of something COMPLETELY unexpected, and is DOCUMENTABLE IN WRITING, you "get back" that lost letter grade (A- goes back to A) if you spend an hour with me going over what happened in class during my office hours the following day.

If you are too sick to come to class, but not sick enough to go to the doctor or ER to get documentation, then you must webex in to class, and participate in the class discussion to get credit for being present.


Grading Policies

1. Assignments are due on the date indicated. If you cannot get your assignment to me at class time, please send me an email explaining why, and let's be sure to have a follow-up chat. The assignments that I do ask you to hand in must not only be credible but also handed in ON TIME in order for you to get full credit.

2.  If I encounter solid evidence of academic misconduct (see below) 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 graded assignments for regrading. 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 it may stay the same. All submissions for regrading must be received no later than May 15, 2016.

Avoiding Academic Misconduct

CLICK HERE to see College Policy circa 1983 - I think this gives you the most detail. STRONGLY RECOMMENDED.

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. 

Academic Honesty

The section immediately below is from the University's Graduate Bulletin policies and procedure page []

Academic honesty and integrity constitute the root of the educational process at Temple University.  Intellectual growth relies on the development of independent thought and respect for the thoughts of others.  To foster this independence and respect, plagiarism and academic cheating are prohibited.

Plagiarism is the unacknowledged use of another individual's ideas, words, labor, or assistance.  All coursework submitted by a student, including papers, examinations, laboratory reports, and oral presentations, is expected to be the individual effort of the student presenting the work.  When it is not, that assistance must be reported to the instructor.  If the work involves the consultation of other resources such as journals, books, or other media, those resources must be cited in the appropriate style.  All other borrowed material, such as suggestions for organization, ideas, or actual language, must also be cited.  Failure to cite any borrowed material, including information from the internet, constitutes plagiarism.

Academic cheating results when the general rules of academic work or the specific rules of individual courses are broken.  It includes falsifying data; submitting, without the instructor's approval, work in one course that was done for another; helping others to plagiarize or cheat from one's own or another's work; or undertaking 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 the course, to suspension or expulsion from the University. The penalty varies with the nature of the offense.  Students who believe that they have been unfairly accused may appeal through their school/college's academic grievance procedure and, ultimately, to the Graduate Board if academic dismissal has occurred.


This class meets 2-1/2 hours a week. Outside of class, you can expect an average workload of approximately 6-12 hours per week. The course WILL PROBABLY require MORE hours early in the semester as you become familiar with Stata. Previous students reported spending 7-8 hours a week prepping for class.

Additional university, college, or professor policies and procedures


1. Turn off cell phones, PDAs, pagers, and i-Whatevers before you come to class.

2. If by chance you forget to turn it off, and your phone or pager rings, I expect you to turn it off immediately.

3. TEXTING IN CLASS OR CHECKING YOUR PHONE FOR TEXT OR EMAIL OR MISSED CALLS IS STRICTLY PROHIBITED. If there is an urgent message you are awaiting, alert me at the beginning of class.Yes, we do have a break every class. You can check all of your messages during break. After break - everything needs to be off again.

4. Because we are in a computer classroom for part of some classes, I expect you ONLY to be taking notes or looking at relevant program pages. I do not expect you to be websurfing, browsing, checking email and such.

Some background, if you want it. Scientific research has documented the costs of using your cell phone, It creates a condition of inattentional blindness . This is not good. See: Hyman, Ira E., S. Matthew Boss, Breanne M. Wise, Kira E. McKenzie, and Jenna M. Caggiano. 2010. "Did you see the unicycling clown? Inattentional blindness while walking and talking on a cell phone." Applied Cognitive Psychology 24 (5):597-607. If you want to read a recent and general review about portable media use see: Levine, Laura E., Bradley M. Waite, and Laura L. Bowman. 2012. "Mobile Media Use, Multitasking and Distractibility." IGI Global.

Academic Freedom

Statement on Academic Freedom: Freedom to teach and freedom to learn are inseparable facets of academic freedom. The University has adopted a policy on Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) which can be accessed through the following link:

The policy encourages students to first discuss their concerns with their instructor.  If a student is uncomfortable doing so, or if discussions with the instructor do not resolve the student’s concerns, an informal complaint can be made to the Student Ombudsperson for the student’s school or college.  Unresolved complaints may be referred to the dean for handling in accordance with the school or college’s established grievance procedure. Final appeals will be determined by the Provost.

Academic Rights and Responsibilities

Temple University students who believe that instructors are introducing extraneous material into class discussions or that their grades are being affected by their opinions or views that are unrelated to a course’s subject matter can file a complaint under the University’s policy on academic rights and responsibilities. 

Academic Honesty 

Temple's policy is as follows.

"Temple University believes strongly in academic honesty and integrity. Plagiarism and academic cheating are, therefore, prohibited. Essential to intellectual growth is the development of independent thought and a respect for the thoughts of others. The prohibition against plagiarism and cheating is intended to foster this independence and respect.

"Plagiarism is the unacknowledged use of another person's labor, another person's ideas, another person's words, another person's 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. Any assistance must be reported to the instructor. If the work has entailed consulting other resources -- journals, books, or other media -- these resources must be cited in a manner appropriate to the course. It is the instructor's responsibility to indicate the appropriate manner of citation. Everything used from other sources -- suggestions for organization of ideas, ideas themselves, or actual language -- must be cited. Failure to cite borrowed material constitutes plagiarism. Undocumented use of materials from the World Wide Web is plagiarism.

"Academic cheating is, generally, the thwarting or breaking of the general rules of academic work or the specific rules of the individual courses. It includes falsifying data; submitting, without the instructor's approval, work in one course which was done for another; helping others to plagiarize or cheat from one's own or another's work; or actually doing the work of another person."

Students must assume that all graded assignments, quizzes, and tests are to be completed individually unless otherwise noted in writing in this syllabus.  I reserve the right to refer any cases of suspected plagiarism or cheating to the University Disciplinary Committee; I also reserve the right to assign a grade of "F" for the given paper, quiz or test.

I strongly recommend you review a CLA policy on academic honesty from the mid-1980s as needed. Note that getting an F in the course is a possibility if there is misconduct.

The three areas where issues about academic honesty are most likely to arise are in taking quizzes and exams, properly footnoting and citing in your papers. We will talk about each of these matters in class.


You are expected to be present at EVERY class for the ENTIRE class period with your CELL PHONE OFF. EACH CLASS YOU MISS COSTS YOU A LETTER GRADE.

If the class you miss happens because of something COMPLETELY unexpected, and is DOCUMENTABLE IN WRITING, you "get back" that lost letter grade (A- goes back to A) if you spend an hour with me going over what happened in class during my office hours the following day.

If you are too sick to come to class, but not sick enough to go to the doctor or ER to get documentation, then you must webex in to class, and participate in the class discussion to get credit for being present.


During class, INCLUDING IN THE LAB PORTION, your cell phones are OFF and AWAY. Save checking your cell phone for the class break.

IF BECAUSE OF SOME TYPE OF URGENT SITUATION you need to have your cell phone on for a SPECIFIC class, NOTIFY THE INSTRUCTOR BEFORE the class begins, and leave class to take the call.

The instructor may ask you to leave the class for that day if he sees or even suspects cell phone use or that you are paying attention to your cell phone.

Computers in class

If you are using your computer in class the only things that should be up on your sreen are RELEVANT windows. If you are going to irrelevant windows like checking email or twitter that will be treated like using or paying attention to your cell phone.

Controversial Subject Matter

In this class we will be discussing subject material that some students may consider controversial. Some students may find some of the readings and/or some of the comments in class challenging. Our purpose in this class is to explore the subject matter deeply and to consider multiple perspectives and arguments. Students are expected to listen to the instructor and to one another respectfully, but of course are free to disagree respectfully with views expressed in class, or in readings. We will develop listening and speaking norms in class.

Disability Statement

This course is open to all students who meet the academic requirements for participation.  Any student who has a need for accommodation based on the impact of a disability should contact the instructor privately to discuss the specific situation as soon as possible.  If you have a documented disability, please bring the instructor the required form from Disability Resources and Services (215-204-1280 in 100 Ritter Annex) so that the instructor can coordinate reasonable accommodations for students with documented disabilities.

In fairness to all students, the instructor can only accommodate those students who might need extra time for taking exams or completing assignments, or special test taking arrangements, if those students are registered with the Office of Disability Resources and Services.


I will not respond to more than one email/student/workday. If you have sent me multiple emails in one day, I will respond to the latest one that I see when I look at my email.

During the semester sometimes things get busy. Although I may respond more quickly, do not expect an email reply in less than two working days (48 hours) during the semester. This does not count weekends or the spring break. You should expect that I will probably not be responding to emails on weekends and during break.

I expect all your emails to me and the teaching assistant to be professional. Professional emails have a subject heading that is informative and specific, a proper salutation, a clear statement of the matter at hand, and a closing. For some hints/tips, see: If you want to learn more, find a book “Send: The Essential Guide to Email for Office and Home.” To learn more about this book CLICK HERE.

Please use the tuclasses at address for all class email.

Late assignments

If you have an excuse for a late assignment I will take this into account only if
a) you notify me beforehand about the problem and
b) I find your excuse for the delay to be a valid one and
c) I have something in writing. (See Absences)


There is a final exam in this course: Monday May 7th , 3:00 - 5:30, in the regular classroom. There will be no makeups or early exams unless something dire happens unexpectedly in your life.

There will be some in-class quizzes. I will let you know these are coming. There will be no makesup for these unless something dire happens unexpectedly in your life.

Office Hours

If we need to chat, and you are unable due to completing obligations to meet during stated office hours, notify me and a different meetng time will be arranged. Please note that office hours are for all students. You do NOT need to set an appointment


You have the right to submit any written assignment for regrading. If you wish to submit an assignment for regrading proceed as follows:  Prepare a written statement explaining why the assignment should be regraded. This applies to written assignments, essay exams, and multiple choice exam questions where you think there was more than one correct answer.  On a cover sheet print your name, TUID number, name of the assignment or test, date of the assignment or test, and the date you submitted the assignment for regrading. Staple the cover sheet to your written rationale and the original assignment. Give this to me in class or leave in my mailbox in the departmente office. (see mailbox) I will review your request for regrading. I will consult with other faculty if I deem that appropriate. As a result of your request for regrading the grade on your original assignment may stay the same, or it may go up, or it may go down. All submissions for regrading must be received no later than Tuesday May 8, 2018.

Religious Holidays

If you will be observing any religious holidays this semester which will prevent you from attending a regularly scheduled class or interfere with fulfilling any course requirement, you will be permitted to make up the class and/or course requirement if you make arrangements by informing the instructor (via e-mail) in advance of the dates of your religious holidays. You are also responsible for reminding the instructor of the reason for your absence or late work at the time of the holiday.

Snow Cancellation

This hardly ever happens! Haha! But seriously folks, the emergency closing number is Philadelphia - 101. Notice is also posted on TU Portal. If there is no official closing, assume that class will be held and that you are expected to attend. In the unlikely event that Temple is open but I cannot get to campus I will email everyone by noon. If I am unable to attend a class due to snow, I will hold a makeup class during the study days at the end of the semester.

So keep your study days free!

Special Services
Students who may require special services should notify the instructor at the earliest opportunity, and I will put you into contact with the Office of Disability Resources and Services at Temple ( -  215.204.1280). You may require special services if you are sight or hearing impaired, or if you wish to register for gaining extra time for taking exams.

Student conduct
You are expected to be familiar with and abide by the Temple code of student conduct. It is available online at: :