All course information is listed within this syllabus.

FDSC 515: Sensometrics - Applied Multivariate Analysis in Sensory & Food Science (3 credits). The main objective of this course is to allow each student to develop the necessary data analysis skills needed for analyzing and interpreting sensory and consumer data.

Prerequisite: STAT 500 or BBH 505


Instructor for FDSC 515

Dr. Helene Hopfer
Assistant Professor of Food Science
Rasmussen Career Development Professor in Food Science
Program Chair, Graduate Certificate Sensory & Consumer Science

Department of Food Science
218 Rodney A. Erickson Food Science Building
University Park, PA 16802

Office Phone: 814-863-5572
E-mail: Use Canvas Inbox or

Office Hours: By appointment

Course Objectives

Upon completion of this course, you will be able to:

  1. use statistical software to analyze sensory data sets with uni-and multivariate methods
  2. create code to import, analyze, and plot data
  3. select an appropriate data analysis method to answer a research question
  4. evaluate the quality and interpret the results of statistical analyses to answer a research question
  5. explain and write about the results of the data analysis in a scientific report
  6. critically evaluate scientific literature for their experimental design and used statistical methods
  7. lead and guide a discussion on scientific literature
  8. experience how statistical methods are used for sensory data sets

Course Outline

Module 1: Intro to R
Module 2: Working With Data
Module 3: Experimental Design & (M)ANOVA
Module 4: Power Analysis & Missing Data
Module 5: Principal Component Analysis (PCA)
Module 6: Correspondence Analysis
Module 7: Multidimensional Scaling (MDS) & DISTATIS
Module 8: Multi-Factor Analysis (MFA) & Generalized Procrustes Analysis (GPA)
Module 9: Cluster Analysis
Module 10: Discriminant Analysis (CVA)
Module 11: Modeling (PLS)
Module 12: Preference Mapping (IPM, EPM)
Module 13: Conjoint Analysis & Consumer Segmentation
Module 14: New Approaches in Sensometrics

Course Schedule

For due dates, refer to your Syllabus and Calendar within Canvas.

Course Materials

No required text, but useful books can be found on the Further Readings page in Canvas.


Your grade in this course consists of 4 parts:

  1. Pre-lecture code quiz (10% of your grade)
  2. Homework assignments (10% of your grade)
  3. Milestone assignments (30% of your grade)
  4. Literature discussions (20% of your grade)
  5. Take-home final (30% of your grade)
    • You will analyze a data set using the techniques studied in the course (see below). The due date for the final is Friday, May 3rd at 5 p.m. I encourage you to analyze your own data, However, if you would like to analyze your own data for the final, I need to approve the data set by at the latest April 1st, in writing!

Code Quiz (10% of your grade)

To help you with the coding for the homework assignments there will be a coding review and interpretation every week. To prepare for this code review class I will provide a data set and a R code script file on Canvas, together with an online quiz (graded).

Homework Assignments (10% of your grade)

To prepare you for your 4 milestone assignments and your take-home final, there will be weekly homework assignments that are peer-reviewed. You are expected to create code to analyze, and interpret the results for data sets assigned for each topic. The code and interpretation will be reviewed by one of you peers, using a grading rubric.

The purpose is to provide you with the opportunity to apply your knowledge of the selected data analysis method(s) and to help you master written communication of statistical analysis results. You are expected to submit your R code and a 1-page summary of your interpretation for peer-review. Your code needs to run on somebody's computer, be understandable to that person (so use the comment function extensively), and your interpretation needs to make sense and be supported by the data. You may include a graph or table in support of your interpretation, but you also need to ensure that your report covers the major findings of your analysis.

Milestone Assignments (30% of your grade)

At the end of each milestone, there will be a graded assignment where you are expected to demonstrate the skills you learned about in the weeks before. You are expected to create code, analyze, interpret, and report your findings in a report for the assigned data set. These assignments will build upon your weekly homework assignments.

The purpose of the milestone assignments is to provide you with the opportunity to apply your knowledge of the selected data analysis technique(s) and to help you master written communication of these methods.

Unless otherwise noted, your report should include:

  • a description of the data set
  • a description of the statistical method(s) used
  • table(s) and/or graphs of the analysis results
  • a full narrative interpretation of the results using the GEE principles
  • suggested further analyses, if needed, to further explain what is going on with the data
  • statistical program commands (R script)

Each homework is limited to 5 pages, double-spaced, 12-point Arial with 1" margins; statistical program commands should be commented for ease of understanding.

Reports and R code files need to be uploaded to Canvas by the deadline. Acceptable file formats are .pdf for the report and .r for the R scripts, so make sure you are uploading the correct files.

All uploaded files need to be named in the following way:

Report: LastName_HW#.pdf where # is replaced with the homework number.
R script file: LastName_HW#.r

Literature Discussions (20% of your grade)

In order to further your critical thinking skills we are using online discussions for the weekly paper and general bigger picture questions. Participation is a requirement for this course, and the Piazza platform will be used for online discussion about class topics.

My goals for using Piazza are:

  1. to critically evaluate the discussion papers with regards to the s of the study, the methods used, the results and conclusions (e.g., used statistical methods, experimental design, data analysis plan)
  2. to provide a platform where we look at the bigger picture, e.g., how does the material covered in the paper relate to the methods we discussed, how could you apply these methods for your own the data, what could be some of the advantages and limitations, which areas you are confused about, etc

In order to receive your points per week, you must post 2 questions and 2 responses relevant to our class subject matter per week. There will be a Sunday 11:59 p.m. deadline for submissions in your community each week.

Take-Home Final (30% of your grade)

For the final you will analyze a larger data set with different techniques that we discussed in class. You can analyze your own data set if you have obtained prior authorization (see above), otherwise, I will provide a data set.

  • You have to develop research questions for the provided data set. Come up with a background story that allows you to develop those research questions for the provided data set.
  • You have to use at least three different methods to analyze the data and you have to justify why you are using each method. That means, tell me how these methods allow you to answer your research questions.
  • You need to hand in a fully written research paper in the form of a journal article. Use the provided word template to write your final paper—I was told this is actually fun to do. You are limited to a maximum of 5 pages (excluding the appendix of your R script).
    • Provide an abstract, introduction, materials & methods, followed by results and discussion and conclusions.
    • Make sure you state your research questions at the end of the introduction.
    • Describe your data set and all statistical methods used in the Materials & Methods section.
    • Place all necessary tables and graphs directly into the document. Use only graphs and/or tables that support your results and discussion. Do not include any tables/graphs just because R provided them!
    • In your conclusion section suggest further analyses if needed.
    • Include your R script as an .r file as an appendix. If you make any changes to the data set outside of you you need to upload that file as well.
  • Acceptable file formats are .pdf for your final report, .csv or .txt for your raw data set if you made any changes to the provided data set or use your own data set, and .r for your R script.
  • Only material uploaded to Canvas by the submission deadline will be used for your final grade.

Late Submission Policy

Based on previous experiences, I developed a late submission policy as follows:

  • Homework assignments build upon each other, therefore, submission of your homework assignments are due as indicated (typically the Wednesday after the module ends). Peer-reviews of the homework assignments are due 1 week after the submission due date.
  • There are 4 milestone submission deadlines throughout the semester: you are expected to submit your report by the listed due date, past that deadline no late submissions will be accepted!
  • The same holds true for the take-home final.

Scheduling Conflicts

I'd be happy to make arrangements for scheduling conflicts, but need to be advised ahead of time. I am happy to discuss how to best to accommodate scheduling conflicts, but strongly encourage you to contact me as early as possible, rather than waiting until a deadline to ask for an accommodation.

Grading Policy

Grading Criteria
Requirement Cumulative Point Value Weight
Pre-Lecture Code Quiz 59 10%
Homework Assignments 130 10%
Milestone Assignments 30 30%
Literature Discussions 14 20%
Take-Home Final 10 30%
TOTAL: 243 100%
Grading Scheme
Letter Grade Percentage
A 100% – 94%
A- < 94% – 90%
B+ < 90% – 87%
B < 87% – 84%
B- < 84% – 80%
C+ < 80% – 77%
C < 77% – 70%
D < 70% – 60%
F < 60%

Please refer to the University Grading Policy for Graduate Courses for additional information.

NOTE: If you are planning to graduate this semester, please communicate your intent to graduate to your instructor. This will alert your instructor to the need to submit your final grade in time to meet the published graduation deadlines. For more information about graduation policies and deadlines, please see "Graduation" under World Campus Student Resources.

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Technical Requirements

This course is offered online and it is assumed you possess the minimum system requirements and computing skills to participate effectively. A list of technical requirements is listed on World Campus' Penn State Technical Requirements page.

Minimum Skills

  • You should have an understanding of basic computer usage (creating folders/directories, switching between programs, formatting and backing up media, accessing the internet).
  • You must be able to conduct word processing tasks such as creating, editing, saving, and retrieving documents.
  • You must be able to use a web browser to open web pages, download files, and search the internet.
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Subject to Change Statement

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