Fall 2016: PhD 604: Quantitative Research Methods

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Class meetings: Tuesday, 3:10pm-5:50pm, CI-304.
Instructor: Dr. Chirag Shah
Phone: (848) 932-8807
Office: Room 302 in SC&I
Office hours: TBD, or by appointment

Course Description
This course will focus on facets of research, problem areas, research techniques, and experiments. Each student develops a research relating to a chosen topic.

16:194:601, 16:194:602, statistics competency. Co-requisites: None. Students should have completed a basic introductory statistics course. See instructor if you have questions about this.

Course Materials

The main book for this course will be: Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics using R. It's available from Amazon, among other places. A used copy is fine.

Additional material that help the students learn R will be provided as a free ebook chapters.

Learning Objectives

This course is designed to provide students with the opportunity to learn and evaluate alternative quantitative research methodologies. Students are expected to investigate selected topics in depth and to have general familiarity with all major course topics.

By the end of the course, students should feel at ease in testing theoretical models by analyzing quantitative data using multivariate statistical techniques. The course establishes multiple regression as a cornerstone and general data analytic method; furthermore, we will build to other methods from this base. Survey and experimental designs will be compared. Students are expected to contribute their interests in the emphases given to survey or experimental designs.

Instructional Objectives

It is my intention that you will improve in your understanding of research models with improvements to your knowledge of statistics.

Each person will be expected to improve markedly in both areas. I do not assume that everyone in the class begins with the same knowledge. I have a good sense of how to benchmark your expertise from the beginning of the course to its completion. This might create problems. If a student at a basic level becomes proficient in the fundamentals of multivariate statistics, then that person will have met my requirements for this course. If an advanced student (who may have already had a course such as 604) does not improve by mastering more complex material, then that person will not have met my requirements for the course. Let us be clear about this: the standard here is your improvement; it is not your final performance on some sort of GRE Mathematics Test. Course 604 is not a gut course for those who have more knowledge in statistics. In the past, some of the students more advanced at the start of the course often received lower grades since they did not demonstrate that they had worked to improve their knowledge.

Instructional Methods

Each class will be split into at least three components and possibly more. We will review text readings and assignments using R. We will also review the progress you are making on specific projects and on the major paper required for this course.

Students can expect to be called on and to present the results of the data analyses they were asked to do for a particular class or course project or paper. They can expect that each person will be asked to contribute to this discussion. Thus, students will be expected to make substantive comments during all class sessions.


Each week you the student be expected to review the listed topics from the course book, do some experiments or run some analyses, and present in the class. This work will also need to be submitted as a report before the class. Together these two components (report and presentation) will make up the weekly assignment.

The student will also pick a major method (typically from the later part of the class) to present in the class.

There will be two kinds of final exam: one using the problem and data assigned by the instructor, and the other based on a problem of choosing by the student. In each case, the student will submit a report.

Grading is based on four aspects of the course described below.
  • Weekly assignments (25%)
  • Major method presentation (20%)
  • Take-home final exam (20%)
  • Research project (35%)
Course grades are assigned according to the following:
  • A   (91-100%): Outstanding and excellent work of the highest standard, mastery of the topic, evidence of clear thinking, good writing, work submitted on time, well organized and polished.
  • B+ (85-90%:) Very good work, substantially better than the minimum standard, very good knowledge of the topic; error free.
  • B  (80-84%): Good work, better than the minimum standard, good knowledge of the topic.
  • C+ (74-79%): Minimum standard work, adequate knowledge of the topic.
  • C   (70-73%): Work barely meeting the minimum standard, barely adequate knowledge of the topic; errors.
  • F  (< 70%): Unacceptable, inadequate work
Please note that only alpha grades will be assigned, and not numerical points. A brief note about grades: Incomplete grades will only be given in extraordinary circumstances. It is your responsibility to check with the registrar's office and the department to ensure you meet the deadline for turning an 'incomplete' to a real grade.

Course Policies
Announcements: Students are responsible for all announcements made in class, whether or not they are present when the announcements are made.
Late submissions: Deadlines are your responsibility. Late submissions may be accepted with a penalty. In the case of unforeseen emergencies (e.g. with a doctor's note), or with a prior permission from the instructor (obtained before the due date), late submissions will be graded normally. Late submissions will not receive any verbal or written feedback.
Communication: For emails, Rutgers accounts preferred. Always include your name (esp. if emailing from non-Rutgers account) and always include the course number (PhD 604) in subject line. If you don't, your email most likely will not be read. This course uses Sakai, primarily for submitting assignments and posting grades. Speaking of communication, please turn off or silent your cellphones and anything that can spontaneously make noise before entering the class.
Attendance: Students are expected to attend all classes. If you expect to miss one or two classes, please use the University absence reporting website https://sims.rutgers.edu/ssra/ to indicate the date and reason for your absence. An email is automatically sent to me. You are responsible for obtaining any material that might have been distributed in class the day when you were absent.

Academic Integrity
Academic integrity means, among other things:
  • Develop and write all of your own assignments.
  • Show in detail where the materials you use in your papers come from. Create citations whether you are paraphrasing authors or quoting them directly. Be sure always to show source and page number within the assignment and include a bibliography in the back.
  • Do not fabricate information or citations in your work.
  • Do not facilitate academic dishonesty for another student by allowing your own work to be submitted by others.
If you are doubtful about any issue related to plagiarism or scholastic dishonesty, please discuss it with the instructor.
The consequences of scholastic dishonesty are very serious. Rutgers' academic integrity policy is at this site. An overview of this policy may be found here. Multimedia presentations about academic integrity may be found here and here.

How to Succeed in this Course
  • Successful students will attend class regularly. If you know you must miss a class, please contact the instructor in advance, either by phone or email. You can obtain assignments or notes from a fellow classmate or from the instructor. In the case of a prolonged absence from class, you should schedule an appointment with the instructor so we can discuss the course material and concepts that you missed.
  • Successful students will pay close attention to the course goals and objectives, because they will help you master the course material. If you have any questions about any of the objectives, please ask the instructor. Questions are encouraged during class for clarification. Remember that you're probably not the only one in the class with the same question. If you have questions about material from previous classes, please email me prior to the next class session, and I'll address your question at the beginning of the class session, prior to any quizzes.
  • Successful students will talk to their classmates about the course material. You will find that they can help you understand many complex issues.
  • Successful students will come prepared to the class with assigned readings for that class. This will help you comprehend the material for that class better. Regular assignments will also be given at the end of each class. Doing these assignments and turning them on time (typically before the next class), will help you obtain higher-order learning goals for this course.

  1. Access the class material promptly and on time.
  2. Respect yourself, classmates, and the instructor.
  3. Participate in class discussions.
  4. Display preparedness for class through completing reading assignments.
  5. Present content knowledgeably with supported reasoning.

Chirag Shah