GSND 5135Q - Research Design and Statistics, Fall 2023

COURSE DESCRIPTION:
The course is open to laboratory, basic, and clinical scientists; prior training in statistical analysis is not assumed.  The course will introduce frequentist statistical analysis techniques, including selection and execution of appropriate statistical tests, and assessing validity of statistical analyses as they may be reported in published articles. Topics include hypothesis-driven experimental design (sampling, Type I and II errors, power analysis, etc.), basic graphical techniques, descriptive statistics, parametric tests (e.g., t-test, ANOVA, correlation, regression), and non-parametric tests (e.g., chi-square, rank-order tests). The class is application-oriented; emphasis is less on memorizing equations, and more on how to choose, apply, and interpret statistical tests using software to complete computations quickly and accurately.

COURSE OBJECTIVES:

• Master basic principles of research design and frequentist statistics based on null hypothesis significance testing.
• Use the R statistical environment, supplemented by R Commander and other packages, to select, run, and interpret statistical tests.
• Be able to report research results via appropriate descriptive statistics (including graphs); determine significance of results via appropriate inferential statistics; and critically evaluate research reports as they may appear in peer-reviewed journal articles.

COURSE FORMAT:

• It is expected that SGS Fall 2022 PhD-level courses will be in-person; PhD-level students are expected to attend class and participate.
• Lecture slides will be made available on Canvas before each class.
• Each student will also be assigned to one of the course TAs, who will provide additional contact and support; appointments with TAs can be made in-person or online.

FACULTY AND STAFF:

COURSE TEXTBOOKS AND SOFTWARE:
Computer Software

• The course uses the R statistical package (free download for Windows, Mac, and Linux at https://cloud.r-project.org), and R Commander (a menu-driven interface for R).
• “Getting Started with R” document is provided on the Canvas site for this course (under Files).
• G*Power will be used for a priori power analyses (free download at https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html).
• Additional instructions for using these packages will be provided in class.
• Students may also use other software for exploratory analyses or to produce graphs.

Supplementary Reading: Optional supplementary reading (journal articles) will be noted in class each week; all articles are available online from RU libraries.

• Students with little/no prior statistics background may find a helpful resource in Gravetter & Wallnau’s Statistics for Behavioral Sciences, which introduces most of the topics covered in this class; the text also contains additional practice problems similar to the homework, many with answers in back of book. 
• For more advanced reading, students may consider Winberg, Harel & Abramowitz’s Statistics Using R and/or Field, Miles & Field’s Discovering statistics using R (London: Sage), each of whichcover much of the course material (and a lot more); an excerpt on getting started with R is also available (free) at Andy Field’s website at https://www.discoveringstatistics.com/repository/dsur_ch3_excerpt.pdf. 
• Students may also wish to explore the many online resources for learning and using R.

EVALUATION METHODS & COURSE GRADING
Homework: Most weeks, there will be a homework assignment, usually short data analysis exercises requiring statistical software. 

• These are skill-building assignments, and will not be graded for correctness per se, only evidence of effort.
• Assignments are due (uploaded to Canvas) timestamped before the due date/time specified in schedule.
• Each homework assignment should be uploaded as a single file in MS Word (.doc or .docx), PDF (.pdf), or raw text (.txt) format, with graphs, tables or screenshots pasted in as needed.
• Students will have approximately 1 week to complete each assignment.
• Late assignments are penalized 50%; assignments > 14 days late will not be accepted without prior permission from instructor.
• Answers to the homework assignments will be reviewed in class.

Exams: There will be two take-home exams and an in-class final exam.

• All three exams are “open-book” exams; students may use class notes, lecture slides, textbook and other materials; however, help from any other person (in-person or online) is not permitted; students will sign an honor statement to this effect.
• Exams 1 and 2 will be take-home exams.
• Exams 1 and 2 will focus on analysis and interpretation of datasets using statistical software. Exam 1 will cover roughly the first half of the course; exam 2 will be cumulative but will focus on the second half of the course.
• Students will have approximately 1 week to complete each exam.
• Take-home exams will be due online (uploaded to Canvas) timestamped before the date/time specified in the course schedule.
• Late exams will not be accepted without prior written approval from instructor.
• In case of trouble uploading to Canvas, a “backup” copy may be emailed to TA, which will be considered on-time if timestamped as received by the TA’s email server before due date/time.
• Exam 3 will be an in-class final exam, and will focus mainly on theory and interpretation.
• Exam 3 will be a “traditional” paper-and-pencil exam.
• Students may bring/refer to class notes, lecture slides, textbook, and other materials. Statistical software (and Internet connection) are not required for the in-class exam; however, students may bring/use laptop if they wish.
• Exam 3 will be given on the last night of class; students will have the usual class period (2h) to complete the exam.

Course Grading

• Course grades will be calculated based on homework (25%), take-home exam 1 (25%), take-home exam 2 (25%), in-class final exam (25%).
• Letter grades will be assigned as: A=90-100, B+=85-89.99, B=80-84.99, C+=75-79.99, C=70-74.99, F=69.99 or below. Course and exam grades are usually not curved.

 

EDUCATION PORTAL AND CANVAS COURSE ACCESS:
The CANVAS course website is an essential part of the ­­­­­­­­­­­­­­­­­­Research Design and Statistics Course. CANVAS can be accessed using the Educational Portal website at: https://ep.njms.rutgers.edu  or by logging in to CANVAS at: https://canvas.rutgers.edu/

ABSENCE FROM COURSE QUIZZES AND EXAMS:
SGS has issued a MISSED EXAM POLICY for students enrolled in their programs. Below are the valid excuses for missing a quiz or exam in this course (verbatim from the SGS Exam Policy):

• Significant illness, with official note by doctor, which must include a valid reason for missing the exam. The doctor’s note cannot result from a remote (online) diagnosis. NOTE: Illness prior to the exam ("didn’t have time to study") does not constitute a valid excuse, although exceptions can be given by the SGS Associate Dean of Student Affairs if the prior illness has been severe or lengthy.
• Death in the immediate family (parents, siblings, children, grandparents, aunts/uncles, niece or nephew, sister-in-law or brother-in-law, parents-in-law, first-cousins). Verification, with dates, is required, but this can be a newspaper announcement, event (e.g. wake) announcement, etc.
• Medical/dental school interview, in which the interview or unavoidable travel overlaps with the exam. Copy of the invitation letter.
• Presentation at a meeting. Proof of attendance required.
• More than two final exams on the same day. (NOTE: An exam should not be rescheduled for an individual with two exams on the same day). If a student has more than two exams on the same day, they will need to contact the SGS Associate Dean of Student Affairs, who will contact the course directors about allowing the student to take one of the exams on a different day. The vast majority of schools across the country, including Rutgers New Brunswick, allow up to two final exam in the same day.
• Permission of SGS Associate Dean of Students Affairs, with consultation of course-director. This will be rare, but is meant to account for valid, but unforeseen circumstances.

Each of these reasons will require some form of documentation to be submitted to verify the absence. For the full policy, please follow this link: http://njms.rutgers.edu/sgs/current_students/docs/new/ExamPolicy.pdf

CODE OF PROFESSIONAL CONDUCT (COURSE EXAMINATIONS):
All students have a fundamental responsibility for maintaining academic integrity and intellectual honesty in their academic and professional endeavors. They are expected to observe generally accepted principles of scholarly work, to submit their own rather than another's work, to refrain from falsifying data, to acknowledge the published work of others in an appropriate manner, and to refrain from receiving or giving aid during examinations or other work requiring independent effort. When submitting written material, students take full responsibility for the originality of all work not otherwise identified by appropriate acknowledgments and imply that both the ideas and words used are their own. All students are expected to respect the property of faculty and other students, and not use research equipment or laboratory supplies of others without permission.
Specific examples of appropriate behavior in examinations exams are given below:
Examinations: The purpose of an examination is to assess a student's knowledge of a topic defined within a course or courses. Unless given explicit written instructions to the contrary, a student must work without assistance on an examination.

• Classroom examination: Each student will provide answers to questions as directed. Unless otherwise stated, no material (books, calculators, computers, communication devices) of any kind can be used during an examination.
• Take-home examination: Each student will provide answers as directed. Unless otherwise stated, research and writing must be done individually without assistance or exchange of information with others. The ability to use source material in the research of answers will be defined for each examination. But, unless stated otherwise, all source material should be cited appropriately as outlined below.

NOTE: These policies also hold true for ALL exams administered remotely.
To view the full policy for the Code of Professional Conduct in the School of Graduate studies regarding examinations, research and oral presentations follow this link: http://njms.rutgers.edu/sgs/current_students/ac_integ.php

ACADEMIC WARNING POLICY:
http://njms.rutgers.edu/sgs/documents/policys/SGS_Academic_Warning_Policy.pdf
Each program shall clearly inform students of the criteria for satisfactory academic performance.  Academic standing will be reviewed each semester by the Program’s Academic Standing Committee. Students who receive less than an average grade of “B” in the designated Core course(s) or have a GPA less than 3.0 will receive an academic warning notice. Students performing below satisfactory levels of proficiency as outlined by the program may also receive a written warning notice. The written warning states the problem(s), outlines those measures needed for improvement and sets a deadline for compliance. Letters informing students of an academic warning will be sent within 30 calendar days of the end of the semester. A request will be made to students receiving academic warning letters to meet with the Program Director and/or the Academic Standing Committee.

COURSE ADD/DROP POLICY:
Policy for the Addition of a Course:
Students may add courses with the approval of the instructor (when required) and the program director. A student wishing to add a course after the general registration period has closed, must complete the “Add/Drop/Withdraw” form and have appropriate approval of the course instructor (when required) and program director prior to the start of the course. Registration will not be permitted beyond the first week of a course. Credit will not be given for courses in which the student was not registered.
Add Course Form Link: https://na2.docusign.net/Member/PowerFormSigning.aspx?PowerFormId=96fcae95-bc67-45fb-8da3-11300ded2e99
Policy for Dropping a Course:
Students may drop courses with approval of the instructor (when required) and the program director. Students submitting a completed “Add/Drop/withdraw” form to the SGS Registrar’s office within 10 academic days of the start of the course will receive a full tuition refund and the course will not appear on their official transcript. The drop period of 1-10 *academic days, is distinguishable from the withdrawal period in that the drop period is without penalty. A completed and approved Add/Drop/Withdraw” form must be received by the Registrar’s office within the time periods set forth above in order for a course to be “dropped”.
Drop Course Form Link: https://na2.docusign.net/Member/PowerFormSigning.aspx?PowerFormId=c45635ca-6a1e-4936-b436-337b211b8433
*An academic day is defined as a day that the SGS campus at which the student is enrolled is open for business.

REASONABLE ACCOMMODATIONS AT RUTGERS SCHOOL OF GRADUATE STUDIES:
Rutgers School of Graduate Studies is committed to providing equitable access to learning opportunities to students with documented disabilities (e.g., mental health, attentional, learning, chronic health, sensory, or physical). To ensure access to this please contact Student Affairs, to engage in a confidential conversation about the process for requesting reasonable accommodations in the classroom setting. Students are encouraged to register with the Office of Student Affairs as soon as they begin their program. Accommodations are not provided retroactively. Rutgers School of Graduate Studies encourages students to access all resources available through the School for consistent support and access to their program.
More information can be found online at:
http://njms.rutgers.edu/education/student_affairs/student_support/disability_services.cfm.
Rutgers University welcomes students with disabilities into all of the University's educational programs. In order to receive consideration for reasonable accommodations, a student with a disability must complete the ODS registration form: https://webapps.rutgers.edu/student-ods/forms/registration and contact the RBHS Office of Disability Services at 973-972-5396 or cindy.poorepariseau@rutgers.edu to make an appointment for an intake interview.  You will also be asked to provide documentation of your disability:
https://ods.rutgers.edu/students/documentation-guidelines .
If the documentation supports your request for reasonable accommodations, the Office of Disability Services will provide you with a Letter of Accommodations. This Letter will be used to notify appropriate school personnel about the accommodations you are qualified to receive. To begin this process, please complete the Registration form on the ODS web site at:  https://webapps.rutgers.edu/student-ods/forms/registration.

TEACHER-LEARNER POLICY RUTGERS SCHOOL OF GRADUATE STUDIES:
The Rutgers SGS Newark Health Science Campus strongly believes that teaching and learning should take place in a climate of mutual respect where students and faculty are equally responsible for maintaining a professional and collegial environment. An environment where students are evaluated based upon accomplishment, professionalism and academic performance. We are committed to maintaining a positive learning environment and the highest standards of behavior in the teacher-student relationship.
To view the full Teacher-Learner policy for the School of Graduate studies, please follow this link: https://njms.rutgers.edu/sgs/current_students/docs/Teacher%20Learner%20Policy.pdf.