Criminal Justice 405
Introductory Graduate Statistics for Criminal Justice:
Simple and Multiple Regression in Criminal Justice
all materials (c) 1999-2001 by R. B. Taylor unless indicated otherwise
on the web at:

Fall 2001


Resources for stats review


Student email list (NOW available)


Memos  (latest: 12/28/01)


Labs and assignments

Grades and Grading Policies 

Special online readings Classroom Expectations

Data sets


Your inner mathematician


Want to try the final exam? (adobe pdf)






Topic and Reading Sequence


Click here to download the US ecological data file that gets used in the examples discussed in the note files. This is an SPSS Data file. The file is named usdanu12.sav. You should print out and look at the frequencies for that file.



R. B. Taylor


One Hour Lab

Monday: 6 - 8:30 (may be adjusted forward)

To be scheduled


509 Gladfelter

Office Hours

Mondays from 4-5:30

Thursday 3:30-5:00 (starting 9/13)

for updates/cancellations see:


5th Floor Gladfelter; available when building open; swipe ID card. I will need your name and ssn to get you on swipe system. SEE LAB GUIDELINES


215.204.7169 (v); 610.446.9023 (fax). You also can ring 1-1376 and ask Ms. Scott if I am available or around, or Ms. Salerno (1-7918) if we need to chat and the phone is not being picked up. SEE EMAIL GUIDELINES


This is a course in statistics for MA and beginning PhD level students in criminal justice. The course has two, interlocking general goals. The first: students can run simple and multiple regression analyses, intelligently interpret the output, and know how to "check out" various problems that may be inherent in the data or analysis. The second: students can read and understand criminal justice research articles using multivariate statistical analyses based on simple or multiple regression, or related techniques Their expertise is such that they can easily grasp the details of the results.

The bulk of the quantitative methods used in these articles rely on the general linear model (GLM). GLMs include simple correlation, simple regression, partial correlation, ANOVA, ANCOVA, and multiple regression. Variations on the GLM include logit, probit, tobit, log-linear, principal components analysis and factor analysis, discriminant functions, and so on.

We will be devoting the bulk of the semester to learning about the basic ideas behind such techniques.

I want you to understand the reasoning behind statistical analyses. Yes, there may be some formulas to memorize, but not a lot (less than 60). I also want you to be able to think intelligently about output produced by statistical analyses.

An additional component being added this year is a little more work on making sense of statistics. We will be reading Joel Best's Damned Lies and Statistics, discussing his points, and you will be bringing in examples.


In teaching this course I stress two general themes; themes you may find useful regardless of where you go after completing your work in this program.

It is always important to find out how the data are arranged. There is no substitute for binocular inspection - eyeballing the data.

Researchers starting analyses with a new set of data may be tempted to "skip" preliminary exploratory analyses, and go right to univariate or multivariate statistical tests. I strongly urge against this for a number of reasons that should become clear over the course of the semester.

Weird cases can make a world of difference in your results and interpretation.

Statistical data processing has become more interactive and iterative over the last decade. It is easier to look at your results and re-do them without a case or two that may be strongly influencing your results. Editors and reviewers expect that we will spend more time doing exactly this. Therefore in this course we spend considerable time on regression "diagnostics" - indicators telling us if a case may be having an unusually strong impact on the results - and redoing analyses in light of those diagnostics.


The course may help you become literate or more literate in microcomputer-based statistical computing. In past years (back in the Cenozoic era), students relied solely on hand calculators. They spent too much time trying to get the numbers to come out right, and less time thinking about what the numbers meant. They also found that their hand calculator skills were not in great demand outside of the classroom. I hope that the microcomputer experience you gain with this course will serve you well on the job or in a position as a research assistant. This is the fifth year that I have taught this course on a microcomputer-base.

 You have three software options.

FREE. If you don't want to spend any money, you can use SPSS FOR WINDOWS VERSION 10.x available in the 5th floor lab, and also available in the GH 107 lab. It also is available in the main Anderson lab. There is a lab on the 5th floor of Weiss Hall that is also open 8:30-4:30, but may have classes scheduled in it some times.

CHEAP. If you need SPSS software to run at home because you cannot spend your time in the lab here on campus, and you want to buy the very cheapest version available, get

SPSS 10.0 for Windows Student Version, 1/e

ISBN 0-13-028040-2

For information on the product and details contact PRENTICE HALL; you can start at their website and search on spss. From their website here are some limitations/requirements.

Save up to 50 variables and 1,500 cases

System Requirements

—Microsoft Windows 95/98 or NT 4.0
—586DX or better IBM-compatible PC
—68MB hard disk space
—32MB RAM minimum
—32MB virtual memory
—SVGA monitor
—Windows-compatible mouse AND CD ROM DRIVE

AMAZON.COM has this available and currently is pricing it at $72 plus shipping. (The mention of AMAZON.COM is used as an example only, and my mention of it is not an endorsement of that organization or a recommendation that you purchase through them.

BE ADVISED you will need a CD-ROM drive to install the software.

For a full list of features click here [you need to be viewing the syllabus online for this to work]

Be aware that this program is limited. From my point of view the two biggest limitations are as follows: * it cannot write command (also called syntax) files. I think being able to write command files is an extremely important capability; * it can only work with data files if they include less than 50 variables and less than 1500 cases.

HOW DO THESE LIMITATIONS AFFECT YOU IF YOU BUY THIS SOFTWARE? We will be working with two data files in this course; an ecological data file of 50 states (so the case limitation is not a problem) and an individual level data file. Although the individual-level data file has not yet been selected, at this time I am leaning toward a file that has about 2200 cases. The ecological file may have more than fifty variables which means -- if it does -- that you will need to download the file at Temple, cut it down so you have the variables you need for you assignment AND less than 50 variables, and then take that file home. For the individual level data file I will cut down the CASES before I put up the file but YOU will be responsible for cutting down the number of variables you need. So you would need to download the file, and create smaller subfiles with fewer variables, then take those home.

Software only works with the requisite hardware. See above.

You also will need ADOBE ACROBAT READER VERSION 4.0 OR HIGHER - this is a freebie you can get - see more details below.


The SPSS version in the CJ, Anderson, and Gladfelter lab is SPSS for WINDOWS 10.x. You may see other versions of SPSS around that are 8, 8.5, or 9. Commands may differ somewhat from version to version. Most importantly, SPSS writes output files differently, and creates charts in different ways. Beware. More specifically: although the DATA files created will be compatible between versions 8 - 10, output files it creates (*.spo), and charts in output files, canNOT be read by earlier versions. So if you have a version 10 output do not expect to read it with SPSS v. 8. 


Students in past years have typically reported that the time involved in this course is anywhere from 25% to 100% greater than what is required in other graduate courses. The extra effort is required because you are learning two different things: how to run computer programs, and how to think about results. Try and plan ahead to allocate more time to this course, particularly if you do not consider yourself "computer literate." The graduate committee and the department many years back recognized that this course is, in effect, a four credit course. The fourth credit comes because we need about an hour each week in the lab, working on this stuff. But we have not changed it to a four credit course because then it just costs you more.  BUT: PLAN on being "in class" about an hour more than the expected 2.5 per week, and plan on this course taking a lot more of your time than you thought.

Further, given the volume of material covered every week it is essential that you be here for every class. If you absolutely must miss a class, please let me know in advance.



There are three working assumptions behind how I have set up this course.

My first assumption is that you have had an undergraduate course in social statistics and remember a fair amount of it, and/or are willing to spend significant time reviewing that material in the first two weeks of the semester. To give you a clue to where you stand: we will be doing a diagnostic on day 1 of class, and I will let you know what your score is very soon thereafter.

 By basic statistics I refer to the following concepts:

 frequency distributions

measures of central tendency

measures of dispersion

the normal curve

areas under the normal curve

the logic of hypothesis testing

probability theory

t test

 To help you get back up to speed on these basics:

 * We will spend a little time early in the semester reviewing some of these concepts,

 * You will probably need to spend some time outside of class re-familiarizing yourself with some of these materials.

* During the first two or three weeks I will be willing to hold tutorial sessions with students in groups or three or more if several of you feel you have big gaps that would not be easily remedied by serious, intensive self-directed study. Or if the computer stuff is driving you totally bonkers. You should network with each other and let me know if there is interest. If there is sizable interest (at least 3 people) we can schedule a specific session and let everyone know about it. I am ONLY willing to do these extra review sessions the first two to three weeks of the semester.

 * If you feel you need more in-depth therapy, ask Dr. Auerhahn if you can sit in on her statistics course throughout the semester. You may want to consider doing that before going ahead with this course.

 My second assumption is that you are somewhat familiar with microcomputers. I assume that you know how to turn them on, how to insert floppies, how to find your way around hard disks, how to use basic WINDOWS (and maybe even DOS commands such as copy, delete, dir, and so on). If you do not have some basic computer literacy, please prevail upon one of your more knowledgeable friends to help you out as soon as possible. You need to be able to use Windows Explorer to: see a list of files; to copy files; to format a floppy or a zip disk; to save files with different names.

 Every time I teach this course, at least one student loses mission critical files. The more you know about Windows, the less likely you are to lose important information. I recommend WINDOWS FOR DUMMIES.  The more you know the less likely you are to lose files.

I STRONGLY encourage everyone to follow standard naming conventions. When you are saving a new version of a syntax file, do NOT overwrite the old one by using the same file name 


but save the new one as 


If you modify it again save it as 


The same idea holds when you make changes to a DATA file - NEVER OVERWRITE THE EARLIER VERSION - you may need it some day!.

It also is true that every time I have taught this course at least one student has had a floppy fail or has suffered from a virus. Keep all mission critical files - including data, command files, listing files, and papers, on at least a couple of disks. The computers in the CJ lab can read 250mb zip disks, they also can read 100mb zip disks. If you have a 100mb zip drive at home it will NOT be able to read 250 mb zip disks.

 My third assumption is that you have access to a Windows 95 or later, IBM compatible computer for running the software OR you are willing to spend time in the Gladfelter lab, or another lab on campus, getting your stuff run. If you are not sure what your hardware can do, see me. If you are having trouble getting access to the appropriate type of microcomputer, please let me know immediately. IN SHORT: either you have the hardware you need at home OR you are willing to put in the time you need here.


We will be working with two different datasets throughout the course of the semester. One is an ecological dataset using information from 50 states. The second is questions from a recent national survey on gun ownership, conducted by Phil Cook and Jens Ludwig for the Police Foundation in 1994. It is a nationally representative sample. I use the two different datasets so we can get used to thinking about theory at different levels, and so we can see some of the differences between individual and ecological data.


You will have a homework assignment almost every week. To complete most assignments you will run one or more statistical analyses on a dataset, and interpret the results, and write it up. For some weeks the homework assignment may involve reading an article and writing the findings up in your own words, or completing an assignment related to the Best book.

 You should bring to class two copies of your homework assignment, or an original and a copy. You will hand in your assignment due for that week at the beginning of class. That way you can keep a second copy and make notes on it as the class discussion unfolds.

You should bring to class specific questions that you have about the readings, or about the assignment. We can spend time at the beginning of class talking about your questions.

 During class I will be presenting conceptual material, reviewing readings, answering questions, and reviewing homeworks. You should be prepared to answer questions on any aspects of the readings and/or homework assignment for that week. In short, when you come to class, be prepared to talk about a few or many aspects of the work you have completed. I WILL go around the room and ask people to report out what they found and what they think it means.

The reason I set the class up this way - so that you are working on it every week, preferably every day, is not to drive you crazy. Rather, I am trying to incorporate some of the latest insights about learning mathematics - it is a new skill, and you need to practice it a lot, even though you have a number of innate mathematical skills already. See


We are scheduling a one hour lab, in addition to the regular 2.5 hour course, as approved in by the department's Graduate Committee. Students in past years have strongly recommended a lab on a SEPARATE night to help them better absorb the vast mountain of material covered in this course. BUT we also have at least a couple of students who have to drive a zillion miles to get here once a week. So we will see.

 In that lab we will run through procedures needed to complete the homework assignment for the following week, and may explore additional issues. The lab -- as of now -- will be held in the 5th floor lab. We are trying to get access to a location where there are more computers.


Although it seems like we have tons of computers around, we also have a bit of a shortage given the number of students in this class. We have 13 in the lab, and we are planning, for the lab period, to also access 3 computers in 560. But the bottom line here is that you should NOT necessarily presume you will be able to complete the analysis needed for your weekly assignment during the lab preceding.


Your grade in this class will be based on the following:


Average grade on handed in homework assignments. I will drop your worst grade from the average. Each assignment will either (a) ask you to run a problem and interpret the results or (b) read an article and describe detailed results in your own words or (c) carry out an assignment related to the Best book.  Toward the end of the semester I may announce that a limited number of homework assignments can be redone


Final examination, to be held at the end of the semester. This will probably be an in-class no-notes exam. The exam will take place MONDAY DECEMBER 10 (First day of Hanukkah) 


In-class participation. The participation may take several forms: answering questions, completing in-class group-work or in-class individual assignments.



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 [   - go to regulations]

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.

Makeup Policy.

There will be no makeups for a missed final exam unless

* you notify me before the missed exam

* and you have a reason for missing the exam that I find valid (e.g., car accident) (I no longer accept excuses like your friend's grandmother dying.)

* and I have something in writing, for my records, verifying the nature of the problem.

 Late Assignments.

Assignments are due on the date indicated. I reserve the right to lower the grade for assignments that are handed in late. The amount the grade is lowered increases the longer the delay in handing the assignment in. Depending on the assignment, the grade may be lowered 1% to 10% a day.

If you have an excuse for a late assignment I will take this in to account only if you notify me beforehand about the problem and I find your excuse for the delay to be a valid one and I have something in writing. Again, a friend's grandfather's death may be questionable.


Regrading Policy.

You have the right to submit any 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, SSN, 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.

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.


Guidelines for Papers

It is VITAL that you follow ALL these guidelines in submitting your papers. Failure to follow these guidelines may result in your paper not being graded in a timely manner. For example, if the output is not attached, I may elect not to grade it.

  1. Type all the written work; make it double spaced; use 12 pitch font.
  2. No cover page
  3. Have as your header your SSN, and the lab assignment number.
  4. Your name must appear NOWHERE on any pages
  5. Your own SPSS output should be appended with your SSN on the upper right of each page.
  6. All pages MUST be stapled.

You also should proof your written work carefully. Mis-spelled words and flagrantly poor grammar will reduce your grade. On your papers I usually take off one point for every mis-spelled word and one point for every flagrant grammatical error. Needless to say, this can add up after a while. I urge you to:

* always run the spell checker

* always run a grammar checker

* proofread carefully, if possible, get someone else to proofread for you as well.

Many students find that their writing improves if they consult some books on writing like Strunk & White's The Elements of Style or Provost's 100 Ways to Improve Your Writing.

Classroom Expectations

- You will arrive for class having read the readings, taken notes thereon,  written out questions you have about the readings, completed the assignment to the best of your ability, and printed out two copies of the assignment.

- Please arrive on time for class. If you have something special, and you know you cannot make it to class on time, please let me know.

 - If you must leave class early, please let me know before class starts.

 - If you must miss class, please let me know beforehand (see above).

Texts and Materials

Main Text

Hamilton, L. C. (1992) Regression with graphics: A Second course in applied statistics. Monterey: Wadsworth. (Priced by at $100; used copy available for $77. Strongly urge you to pester other graduate students to get a copy, or to "go in" with a classmate and share or something.

 We will use this as our main text on regression. I chose this book because 1) students have been unhappy with every other book on regression I chose; 2) it makes extensive use of graphical displays for understanding data, an approach used extensively in this course; 3) he deals "up front" with non-normal data and how to handle it, and this is an important issue in criminal justice research; and 4) although they will be covered only lightly in this course, the volume contains information on important recent developments in the general linear model (e.g, bootstrapping, structural equation modeling) that you may need to know about in the future. In short, I think it will hold up well as a general reference book on the topic. Unfortunately, Hamilton's examples all come from environmental science, which some students find less than enthralling. If you are in the doctoral program, and want a generally up to date carefully done multiple regression reference text, this is not a bad one.

 If you feel that you need another text in this area, here are some that I have used in the past and are basically pretty good. Students, of course, have differed with me in their assessments.

Other Texts

Aldrich, J. H., and Nelson, F.D. (1984). Linear probability, logit, and probit models. Thousand Oaks: Sage. ( for 14.95, used, $11, plus shipping). In criminal justice there are a lot of yes/no outcomes: did she recidivate or not?; did she show up for bail or not? We need a slightly different type of model to analyze these outcomes. In past years we have been able to get to this topic in the last couple of weeks of the semester.

Best, J (2001) Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists. Berkeley: University of California Press. (AMAZON.COM prices this for around $13 plus shipping). This text talks about stat wars, how stats are abused, and how we can perhaps get past all this confusion. I want us to do some reading out of this book early in the semester so you have a broader background about the sociopolitics of statistics, and what that means for your appproach.

Taylor, R. B. (2001). Various notes on statistics and regression.

 These are various notes or "chapters" that I have developed over the past thirteen years. They are in adobe acrobat PDF format. Once you have adobe acrobat reader installed,  you will link to my website and print these out, or save them to your own hard disk. I will do all I can to insure that each file is downloadable. If you encounter any problems whatsoever let me know asap. I used to put all this in a student copy pack but the copy center charged a ton and sometimes gave back unreadable pages.

If you are using the PDF files, and if you are using your own computer, rather than a university computer, you need to be sure that your computer has installed a free piece of software called adobe acrobat reader. It allows you to read  PDF files. To get it go to this address:

You will need about 6 megabytes on your computer to download the files you need. This takes a while. If you are not a tekkie you might wanna get someone who is to help you get Adobe Acrobat Reader installed. You will need this for both the VIEWING and PRINTING of PDF files.

You want to get all this setup straightened out well before you are going to need to access online documents if you plan on using an off-campus computer.

You will need version 4.0 or higher.

 In the past students trying to print PDF files on campus have complained about printing problems. You canNOT print PDF files that are readable in either GH 107, or Anderson labs, or Paley.  However you also should know that there are some good printers around such as in the basement of Speakman and in the lab in Ritter.

You can download  the files here on campus for a fast download, and print it out at home where you have a better printer, but you will need to have adobe acrobat reader already installed on your home computer.

These notes refer mostly to conceptual material we are covering throughout the course. I will tell you which set of notes we are covering which week. You want to read these notes thoroughly and carefully before coming to class for the week they are assigned.

IMPORTANT NOTE ON WHEN TO DOWNLOAD THESE. You do NOT want to download all of these right now. Next to each file I will put an "UPDATE" sign once it has been updated. Do not download and print out a particular file until I have done that update. 


Porkess, R. (1991). The Harper Collins Dictionary of Statistics. New York: Harper. Porkess is a useful guide to some basics - when you want to review variance or skewness or the normal distribution.



 Every week, before lab, I will ATTEMPT to distribute a "lab guide." At the beginning these will give you specific instructions on how to run SPSS. As the semester goes along and you become more proficient (hopefully!) I "fade out" the keystroke by keystroke instructions.



(subject to possible revision at a later date depending upon a host of factors: YOU SHOULD CHECK BACK HERE A COUPLE OF DAYS BEFORE EVERY CLASS TO CHECK FOR UPDATES


Readings DONE for this class

Class topic

Lab (preps you for the homework due the following week)




Class: review syllabi; Starting up SPSS

Descriptive data displays for single variables: I

histogram, box and whisker, s&l, P-P, Q-Q

Generate and interpret univariate graphical displays; use explore; deciding if a variable is normal






HAMILTON,pages 1 - 23;

Taylor (1994) Research methods in criminal justice. New York: McGraw Hill, Chapter 10 "Sampling" pp. 183-192. I will leave a copy in my mailbox

Descriptive data displays for single variables: II

histogram, box and whisker, s&l, P-P, Q-Q

Logic of hypothesis tests

z test

Student t-test





The logic of hypothesis testing: T-test

Carry out independent and dependent t-tests; interpret results






Hamilton, pp.29-42, 51-53, 289-294

Understanding covariance

Transforms and functional relationships;

Looking carefully at scatterplots





Best, Intro, chs 1-3

Simple regression and zero-order correlation: B, A, r

Looking at scatterplots and regression lines



Hamilton pp. 42-49
Best, chs 4-6

Hypotheses we test in simple regression: B and r

R squared; variance of predicted scores and residuals; t test of b; F test of R squared




Hamilton 124-133


No lab


Hamilton, 65-72

Residuals in regression, error, and assumptions' Residual diagnostics

Residual diagnostics with simple regression




Finish up on residuals and assumptions
Best presentations

Leverage, influential cases and outliers




Hamilton, pp. 77-82

More Best presentations

Partial correlation and multiple regression

Multiple regression, intro




Hamilton 109-133

More on multiple regression;- Going back to assumptions: a checklist approach
- Measures of influence, leverage, and general deviance

Multiple regression, more basics



Hamilton, 217-235
Aldrich and Nelson, 1-35, 41-44

Logit and probit

More multiple regression basics; intro to logit and probit



Hamilton,  217-235
Aldrich and Nelson, 1-35, 41-44


Logit and probit
Assessing Change; Different Predictor Sets;Dummy Variables;Interaction Terms;Multicollinearity;

More logit and probit


Hamilton 53-59, 84-88


Path analysis

 Factor/principal components analysis (Hamilton, Ch. 9)

Additional multiple regression topics; presentations