Exploratory Factor Analysis in R
Welcome to the Online Course on “Exploratory Factor Analysis in R”. This is an online course designed to deepen your understanding of how to conduct factor analysis in R. The target audience are graduate students, researchers, and anyone interested in learning how to use open-source data for factor analysis. Factor analysis is a data reduction method used to explore and validate the structure of observed variables in multivariate data.
Please read the course syllabus carefully and email me if you have any questions or need special accommodations.
In this course, you’re expected to read book chapters or papers assigned by the instructor, or provided by your (must be approved by the instructor), watch recorded videos, post questions and assignments, and comment on your colleagues work in the discussion forum. Students are required to use the resources provided by the instructor for this course only and are prohibited from sharing any material provided by the instructor to anyone not enrolled in this course, on social media platforms or any publicly accessible platform, either during or after enrollment without permission from the instructor. Students who violate this rules will be reported to the University’s disciplinary committee for further action.
By the end of this course, students are expected to independently conduct factor analysis in R with open source data or their own data and interpret their findings in according with the APA 7 standards.
Instructor: Mr. Benjamin Kweku Lugu
Email: firstname.lastname@example.org (Preferred mode of communication)
Research Assistance Services The Hillard Building 900 Anna Avenue, Room 109 Tuscaloosa, AL 35401
Benjamin is a third year PhD student and the quantitative research specialist at the Research Assistance Services of the College of Education, The University of Alabama. I have an MSc in Mathematical Statistics from the African Institute in Mathematical Sciences (AIMS), Rwanda. I am passionate about teaching and committed to helping student gain deep understanding of Exploratory factor analysis.
Thursday: 2 – 5pm & Friday: 8 – 2pm
Office hours are strictly by appointment. Meeting can be either virtual or in-person. Please email the instructor to schedule an appointment. Be sure to include the day, date, time and preferred mode of meeting. Please send a follow up email if you do not hear back from me after 24 hours.
Class Meeting Schedule
I understand that online classes can be very challenging for some students. In order to give everyone a good learning experience, we will be having a bi-weekly virtual meeting to address all your comments and questions. The meeting will last for one hour. You will need to sign up so I can have an idea of how many students will be attending. Please come prepared and with questions.
Time and date will be available soon.
Students are required to have a basic understanding of how to interpret statistical results and use R for descriptive statistics, inferential statistics, hypotheses testing, and how to formulate research questions for a study.
By the end of this course, students will be able to:
- Understand the fundamental concepts and principles of exploratory factor analysis.
- Conduct exploratory factor analysis in R.
- Interpret and report results.
- Apply factor analysis in practical contexts.
Student Learning Outcomes
Students will be able to:
- Understand the fundamental concepts of EFA
- Distinguish between observed and latent variables, formulate research questions that can be answered by EFA
- Know the type of data that can be used for EFA
- Assess the need for factor analysis
- To know the factor extraction method and type of rotation to use
- Determine the number of factors to retain
- Evaluate the model fit of the factor analytic model
- Interpret factor loadings and Cross loadings
- Assess the reliability of the extractors.
- Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.
- Watkins, M. (2020). A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio (1st ed.). Routledge. https://doi.org/10.4324/9781003120001
Module 1: Introduction to Exploratory Factor Analysis
- Understanding the need for EFA
- Examples of Studies that Employed EFA
- Key Concepts
- Some Assumptions of EFA
- Assignment 1
- Data Types, Research Questions/Hypotheses for EFA
- Factor Extraction Methods
- Choosing the appropriate extraction method
Module 2: (Un)Rotated Factor Solution
- Unrotated Factor solution
- Rotated Factor solution
- Interpreting rotated factor loadings
Module 3: Assessing the Need for Factor Analysis
- Kaiser-Meyer-Olkin (KMO)
- Bartlett’s Test
- Determining the number of factors
Module 4: Interpreting Factor Loadings
- Factor Loading
- Cross Loading
- Model Assessment and Reporting
- Reliability Analysis
- Reporting results in academic papers
- Assignment 2
Module 6: EFA in R
Module 7: Individual or Group Project
Exams and Assignments
- Two Assignments (30% points each)
- Class Participation and Engagement (10% points)
- Individual or Group Project (60% points)
A computer with stable internet connection will be required. Students who do not have computers should contact the library to loan one or use the library computers. Throughout the course, we will use R programming language. R is available for free download for both PC and MacBook.
Grades will be assigned using the following grading scale:
Late submissions will be accepted with a penalty of minus 1 point for the first day, 2 points for the second day and so forth unless prior permission has been is given Missed assignments will attract a score of zero.
Academic Integrity: Cheating or plagiarism will not be tolerated in this course. You’re permitted to discuss assignments, projects or use AI software like ChatGTP to understand concepts taught in class. However, you are required to write your assignments and interpret findings in your own words, and cite sources where necessary. Students who violate the academic Integrity policy will be reported to the University’s disciplinary committee for further action.
Campus Resource List for Students
College can be a stressful time for students who may often experience issues that get in the way of their academic and personal success and the health and wellbeing of our students is a priority for the University. If you or someone you know is facing a challenging time or dealing with academic or personal stress, anxiety, depression, or other concerns, we strongly encourage and support you to seek assistance or to help friends find the care that they may need by contacting campus Resource List for Students
Statement of Academic Misconduct
Students are required to read thoroughly and adhere to UA academic misconduct policy available in the online catalog (https://catalog.ua.edu/undergraduate/about/academic-regulations/student-expectations/academic-misconduct-policy/)
Statement on disability accommodation
Students who need special attention due to disabilities are recommended to contact the Office of Disability Services
Pregnant Women and Parenting Accommodation
Students who need special accommodations due to pregnancy or parenting should visit the UActs
The University of Alabama respects the religious diversity of our academic community and recognizes the importance of religious holy days and observances in the lives of our community members. Please send me an email if you would like to be absent for religious purposes.