Simon Fraser University
Engaging the World

Course Outlines

Spring 2021 - CRIM 863 G100

Research Methods IV: Advanced Quantitative Methods (3)

Class Number: 4205

Delivery Method: Remote

Overview

  • Course Times + Location:

    Jan 11 – Apr 16, 2021: Mon, 9:30 a.m.–12:20 p.m.
    Burnaby

  • Prerequisites:

    CRIM 861, or permission of the instructor.

Description

CALENDAR DESCRIPTION:

A survey of advanced statistical techniques in criminological research. Specific topics may include: limited (e.g., categorical, ordinal, and count) dependent variables, multi-level modeling, longitudinal data techniques, spatial data analysis, missing values analysis, and propensity score matching. Attention will be given to the decisions involved in data exploration and preparation for statistical modeling purposes using the appropriate statistical software. There is an emphasis on conceptual foundations and application. A strong background in regression-based techniques is assumed.

COURSE DETAILS:

This course will address a range of statistical techniques, primarily parametric statistics. The seminars will allow the instructor to present one particular statistical technique, i.e.: purposes, assumptions, type of information provided, interpretation of the results, how to conduct such analysis using sophisticated statistical software: I will be using R, but you may use other software such as Stata or SAS. The seminar will also include a discussion on the technique using published scientific studies, i.e.: strengths and limitations of the statistical analysis, when to use (and not to use) such technique, as well as the interpretation of the findings.

Participation:
This course is predicated on active and informed participation. Simply coming to class every week, occupying space, and warming the room is not enough. You are expected to have done the readings for each week before the class, and to be prepared to discuss them.

Course structure:
There is one seminar (2 hours) per week, plus one 1-hour lab. Given the REMOTE nature of this course, lectures (one theoretical and one applied) will be recorded and posted each week. These will consist of the first 2 hours of this course each week (asynchronous component). We will then have an ONLINE tutorial/seminar via Zoom (synchronous component). The tutorial will occur during the last hour of the course, 1130am – 1220pm, beginning on the first day of classes, 11 January 2021. Students are expected to have watched both lectures prior to the tutorial.

Last day of classes: 12 April 2021.

COURSE-LEVEL EDUCATIONAL GOALS:

Lecture and lab topics:

  • Introduction, course outline, getting R to work
  • Regression assumptions and diagnostics (multicollinearity, heteroskedasticity, autocorrelation, model specification, interaction effects)
  • Panel data (fixed and random effects models)
  • Advanced qualitative response models (review of logistic/probit regression, multinomial logit/probit, ordinal logistic regression)
  • Count data models (Poisson, Negative binomial, Zero-inflated models)
  • Time series
  • Hierarchical linear models
  • More material if time permits (Spatial statistics and trajectory modeling, for example)
Please note: I retain the right to modify the nature of the course structure and schedule based on instructional needs.

Grading

  • Weekly Seminar Contributions 20%
  • Assignments 30%
  • Presentation 10%
  • Term Paper (Due: 19 April 2021) 40%

Materials

MATERIALS + SUPPLIES:

No official textbook for this course. I am providing a number of references below as resources for you.

I will also be using a set of books from the “Blue Book” series written by G. David Garson. These books are $5 for Kindle versions, but can be obtained for free by filling out a form requesting PDF copies; only two books may be requested per 48 hours.

The web page for these books, with information on the books is here:

http://statisticalassociates.com

And the list of these books is available here:

http://statisticalassociates.com/booklist.htm

We will be covering material from the following Blue Books:

Multiple Regression
Logistic Regression, Binary & Multinomial
Ordinal Regression

If you are obtaining these books for free I suggest you get them early because there is a delay in obtaining access and you can only request two books per 48 hours.

The following books are all available in Full text - Unlimited user access from SFU Library and may prove to be useful (Ignore the math, unless you are interested!):

  1. Lewis-Beck, Michael. Applied Regression: An Introduction. Thousand Oaks, CA: Sage Publications, 1980. ISBN: 9780803914940

  2. Jaccard, James and Robert Turrisi. Interaction Effects in Multiple Regression. Thousand Oaks, CA: Sage Publications, 2003. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412984522

  3. Berry, William D. and Stanley Feldman. Multiple Regression in Practice. Thousand Oaks, CA: Sage Publications, 1985. ISBN: 9780803920545
    Ignore: Measurement Error, Nonlinearity and Nonadditivity
  1. Kaufman, Robert L. Heteroskedasticity in Regression: Detection and Correction. Thousand Oaks, CA: Sage Publications, 2013. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781452270128

  2. Allison, Paul D. Fixed Effects Regression Models. Thousand Oaks, CA: Sage Publications, 2009. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412993869

  3. Finkel, Steven E. Causal Analysis with Panel Data. Thousand Oaks, CA: Sage Publications, 1995. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412983594

  4. Pampel, Fred C. Logistic Regression: A Primer. Thousand Oaks, CA: Sage Publications, 2000. ISBN: 9780761920106
    Ignore: Estimation and Model Fit, Probit Analysis
  1. Menard, S. Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage Publications, 2002. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412983433

  2. Jaccard, James. Interaction Effects in Logistic Regression. Thousand Oaks, CA: Sage Publications, 2001. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412984515

  3. Borooah, Vani K. Logit and Probit. Thousand Oaks, CA: Sage Publications, 2002. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412984829

  4. Luke, Douglas A. Multilevel Modeling. Thousand Oaks, CA: Sage Publications, 2004. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412985147
     
  5. Ward, Michael D. amd Kristian Skrede Gleditsch. Spatial Regression Models. Thousand Oaks, CA: Sage Publications, 2008. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781412985888

  6. Pickup, Mark. Introduction to Time Series Analysis. Thousand Oaks, CA: Sage Publications, 2015. DOI: http://dx.doi.org.proxy.lib.sfu.ca/10.4135/9781483390857

The “Sage Little Green Books” are technical at times, but excellent resources for the material we will cover.

There may also be other required readings (books, book chapters, journal articles, etc.) available through Canvas. You are responsible to download, photocopy, or borrow these readings from the library.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SPRING 2021

Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).