ANFS 933: Design, Analysis, and Interpretation of Experiments

Syllabus and general information

 

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Course goals

 

1. To introduce graduate students in the biological sciences (sensu lato) to the fundamental concepts and a subset of statistical methods necessary to plan, conduct, and interpret effective experiments

2. To introduce graduate students to R, the open-source statistical programming language and their new best friend

3. To provide an opportunity for graduate students to receive critical input, both one-on-on with the instructor and from the class, on the experimental design and analysis of their individual research projects

 

 

Course description

 

Through in-depth consideration of common general linear models used in the analysis of variance, this course is intended to challenge graduate students to reflect upon the process of experimental thinking itself while equipping them with the skills necessary to design and execute experiments that address the research questions of most interest to them. Problem sets are the heart of this course, providing students the opportunity to internalize the concepts covered in lectures. Optional scheduled weekly lab sessions provide opportunities for students to work through the self-paced lab (R) materials, with help on hand. Regular open office hours provide informal venues for students to discuss the course material with each other and the instructor. Finally, consultations with the instructor will provide students opportunitues to critique their individual graduate research projects, preparing them for optional final presentations to the rest of the class.

 

 

Course objectives

 

1. Students will understand the fundamental concepts underlying general linear models through an in-depth examination of the analysis of variance.

2. Students will gain an appreciation for the importance of experimental design in all phases of their research, from initial planning to final analysis.

3. Students will gain familiarity with the application of common experimental designs, treatment structures, and error-reduction techniques.

4. Students will learn how to manage data and analyze common experiments designs using R.

5. Students will have the opportunity to apply the knowledge they gain to their own graduate research projects, honing and ultimately presenting their objectives and methods for critical feedback from the instructor and their peers.

 

 

Pre-requisite

 

A basic college-level course in statistics or instructor permission.

 

 

Software

 

The class is based on and all homework and exams require the free statistical software package R. R is compatible with Mac, Windows, and Linux platforms and can be downloaded here.

 

In addition to this, we will be using the R Studio interface for R, which can be downloaded for your desktop here.

 

 

Course format and student evaluation

The course consists of two 1.5-hour lectures and one hour of optional computer lab per week. The lab time is optional because the hour is open work/Q&A time; video presentations of all lab content will be available online, to view if and when you wish. Grades are based on nine homework assignments / problem sets, two in-class quizzes, and two exams:

 

Homeworks            45%

In-class quiz 1          5%      Feb 11
First exam               20%      Design Tables due Feb 25; Full Exam due March 3
In-class quiz 2          5%      April 3
Second exam          25%      Design Tables due April 29; Full Exam due May 6

 

Group office hours: The instructor will host open group office hours on Mondays and Wednesdays, the main purpose of which is to field questions about the weekly problem sets. You are strongly encouraged to make use of these sessions. They are meant to serve as opportunities to work through problem sets with each other, while having the instructor on-hand to ask questions and confirm results. To the extent possible, more conceptual questions should be asked in real time during lecture so that the entire class may benefit from the discussion.

 

Weekly-ish problem sets (homework): You are strongly encouraged to work with each other on the homeworks, discussing strategies and comparing results, though the work you finally submit must be your own (i.e. no divvying up the work and copying and pasting from one another). Detailed solution keys will be provided within 24 hours of each problem set being due. The keys are intended as teaching tools, so you should review them carefully while that week's problem set is fresh in your mind. Each week, a random subset of questions from the homework will be selected for detailed grading.

 

Exams: In contrast to the homeworks, the two take-home exams are to be completed entirely on your own. Regardless of its content, discussion of any exam question with another student is considered a violation of the UNH Academic Honesty Policy. If two or more exams share more than two uncommon incorrect answers, it will be considered cheating and the case will be fully documented and referred to the Associate Dean of the Graduate School. This is serious business, and I am serious about it.

 

Student presentations: Interested students will have an opportunity at the end of the semester to present their research methodology to the class for critical discussion.

 

 

Topical outline

1.    Introduction to the principles of experimental design
2.    Distributions, hypothesis testing, and sample size determination
3.    General linear models and the fundamentals of analysis of variance (ANOVA)
4.    Orthogonal contrasts
5.    Means separations
6.    Randomized complete block design (RCBD)

7.    Factorial treatment structures

8.    Incomplete block designs: Latin squares
9.    Data transformations
10.  Fixed, random, and mixed models
11.  Unbalanced designs
12.  Split-plot designs and their relatives, including repeated measures
13.  Analysis of covariance (ANCOVA)


Click here for a printable course schedule and here for the course calendar.

  

 

Materials, the course website, and Canvas

 

Nearly all written course materials can be found on this website, under detailed schedule and course materials, your one-stop-shop for practically everything.

 

I will post lecture notes to the course website no later than the night before each class. I strongly encourage you to print them and have them at the ready for additional note-taking. I will post the lab materials to the course website no later than the start of lab (4:10 PM, Thursdays). All problem sets, exams, and other materials (readings, supplemental tables, etc.) will also be provided to you via this website.

 

The supplemental lab instructional videos will be available to you via Canvas (Media Gallery) prior to each lab session, and lecture recordings will be made available via Canvas as soon as they are processed (also Media Gallery).

 

Keys will be made available to you via Canvas (Accouncements). Finally, all of your assignments will be submitted via hard-copy (on paper).

 

 

Grading

 

Letter grades will be assigned according to the standard scale:

 

            Above 95 = A                         90 - 94 = A–

            87 - 89 = B+                           83 - 86 = B                              80 - 82 = B–

            77 - 79 = C+                           73 - 76 = C                              70 - 72 = C–

            67 - 69 = D+                           63 - 66 = D                              60 - 62 = D–

            Below 60 = F

 

 

Whether due to weather or the next epidemic, if we get the rug pulled out from under us and have to go remote at any point, some notes on Zoom Netiquette:

 

Zoom works quite well for a class like this, as long as we all abide by some common sense rules:

 

1. Turn on your camera, if you have the bandwidth for it.*  Facial expressions and body language are an important part of communication, which includes active listening. Being seen will also tend to support your being more present.

2. Mute unless you are talking and be mindful of what’s being captured by your camera.

3. Remember that a virtual class requires the same degree of respect as a live class, so be sure to consider your appearance and behavior on camera the same as you would in an IRL classroom.

 

As with any class, our time together should be devoted to intellectual engagement with the material at hand. Please take seriously our shared responsibility for the welfare of this learning community.

 

* Having your camera on during lecture is strongly encouraged. For the optional group office hours and lab support sessions, however, it is required, should we need to go remote.

 

 

Academic Honesty

 

The UNH Academic Honesty Policy can be found here.

 

You must adhere to the principles and rules of the University and pursue academic work in a straightforward and truthful manner (see above section on Student Evaluation). Failure to comply could result in a failing grade for the course and/or referral to the Associate Dean of the Graduate School. Please let me know if you have questions about how the policy applies to your work in this class.

 

 

Curtailed Operations

 

With our newfound ability to function via Zoom, UNH curtailment will not affect our schedule. Unless I indicate otherwise, you should assume that the schedule of activities printed on this syllabus remains in effect. If we need to pivot to Zoom for a class, I will let you know via email.

 

 

Disability Services

 

According to the Americans with Disabilities Act, each student with a disability has the right to request services from UNH to accommodate his/her disability. If you are a student with a documented disability or believe you may have a disability that requires accommodations, please contact Student Accessibility Services (SAS) at 201 Smith Hall. SAS will work with you to create an accommodation letter. Please follow-up with me as soon as possible to ensure timely implementation of the identified accommodations in the letter. Faculty have an obligation to respond once they receive official notice of accommodations from SAS but are under no obligation to provide retroactive accommodations. For more information, refer to the SAS website or contact SAS at 603-862-2607, 711 (Relay NH), or sas.office@unh.edu.

 

 

Emotional or Mental Health Distress

 

Your academic success in this course is very important to me. If, during the semester, you find emotional or mental health issues are affecting that success, please contact UNH Psychological and Counseling Services (PACS) (3rd Floor, Smith Hall; 603-862-2090/TTY: 711) which provides counseling appointments and other mental health services.

 

 

Other resources

 

A highly recommended and very affordable introductory text to R (not required!):

 

A.P. Beckerman and O.L. Petchey. Getting started with R: An introduction for biologists (2nd edition). Oxford University Press, 2017.

 

 

Some FREE R resources, to complement course materials (also not required!):

 

YouTube channel of R tutorials by Mike Marin

 

swirlstats: Learning R in R

 

Quick-R by datacamp

 

 

A smattering of references on Experimental Design:

 

·       R.G.D. Steel, J.H. Torrie & D.A. Dickey. Principles and Procedures of Statistics. McGraw-Hill, Publisher, 3rd Edition, 1997.

 

 

 

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Last updated: January 2025