General Information
- Course Code: ESRM 64503
- Course ID: 10434
- Credits: 3 CH
- Course time and location: Mon 17:00-19:45; GRAD 229
- Instructor: Jihong Zhang, Dr.
- Contact Information: jzhang@uark.edu
- AI tutor: Link to AppliedMA Mentor on OpenAI
- Personal Website: http://jihongzhang.org
- Office Location: GRAD 133B
- Office Hours: Mon 2:00-4:00 PM
- Office Phone +1 479-575-5235
- Semester: Fall 2025
- Prerequisite: ESRM 64103 and ESRM 64203, both with a grade of C or better.
Course Objectives, Materials, and Prerequisites
The online syllabus at the address provided above will always have the most up-to-date information. This course focuses on multivariate statistical procedures as applied to educational research settings, with an emphasis on the use of existing computer statistical packages.
The course has two main objectives:
- Modern multivariate analysis using observed variables
- Building a foundation in terminology, concepts, and software to enable participants to learn more advanced analyses in the future
This course provides an exploration of multivariate statistics within the context of education and psychology. Class time will be devoted primarily to lectures and examples. Lecture materials in .html format will be available for download and viewing at the website above the day prior to class, or paper copies will be provided in class. Audio/video recordings of the class lectures in .mp4 format will also be posted online upon request, but are not intended to take the place of class attendance. Selected book chapters and journal articles will be assigned for each specific topic as needed. The initial list of readings is provided below but will likely be updated throughout the semester. Updates to the reading list will be posted in the online syllabus and announced in class and via email. The R language will be the primary statistical tool used throughout the course, with RStudio as the user interface.
Prerequisite
Participants should be familiar with the general linear model (such as analysis of variance, linear regression) prior to enrolling in this course.
Also, it is assumed that students have has solid statistical training up to and including topics in multivariate statistics (ESRM 64103 Experimental Design in Education, and ESRM 64203 Multiple Regression Techniques for Education). In addition, it is assumed you are also familiar with R language. SPSS may not be sufficient for this course.
How to Be Successful in This Class
- Come to class ready to learn.
- Complete the out-of-class exercises prior to each class.
- If you become confused or don’t fully grasp a concept, ask for help from your instructor
- Know what is going on: keep up with email, course announcements, and the course schedule.
- Try to apply the information to your area of interest — if you have a cool research idea, come talk to me!
Software
- R and R packages (tidyverse)
Homework
Homework Assignments
- Posting & Submission: Homework (HW) assignments will be posted on the online homework portal on this page and Blackboard every Monday morning.
- Homework Format: Takehome assignment. Answering questions on Microsoft Forms. May include multiple-choice questions or open questions. May take you around half hour to finish.
- Due Date: Assignments are due by the end of work hours on the following Monday.
In-Class Quizzes
- Format: Short quizzes (1-3 questions) at the beginning of the class (not always for every class) to refresh your memory.
- Duration: 5-10 minutes per quiz.
- Content: Questions will be general and cover recent material.
- Device: Please bring your laptop or mobile device to class to answer the quiz.
After everyone finishes, we will review the quiz together as a class.
Office Hours & Getting Help
To make the most of office hours, please come prepared:
- In Person: Bring printouts and/or your files on a laptop so we can review them together.
- Electronically: If you cannot attend office hours, email me your specific and detailed questions. Be sure to attach your R code and data set to the email.
Online Homework Portal
- Homework 0 (HW0)
- Homework 1 (HW1)
- Homework 2 (HW2)
- Homework 3 (HW3)
- Final Homework (HW4: Report of data analysis using your own data)
- Grading Online Checking
Grading
Your final grade in this class will be based on a combination of homework assignments, in-class quizzes, and optional extra credit. Please review the breakdown below for clarity:
Point Breakdown
- Total Possible Points: 100
| Component | Number | Points Each | Total Points | Notes |
|---|---|---|---|---|
| Homework Assignments | 4 | ~20 | 80 | Assignments are posted weekly; see schedule for due dates. |
| In-Class Quizzes | Matches number of in-person classes | Varies | 20 | Must be present in class to earn quiz points. |
| Extra Credit (HW0) | 1 | 2 | 2 | Optional; see online course website for details. |
- All homework and answers must be your own work. Copying or paraphrasing from others is not allowed.
Letter Grade Scale
| Percentage of Points | Letter Grade |
|---|---|
| 90–100 | A |
| 80–89 | B |
| 70–79 | C |
| 60–69 | D |
| < 60 | F |
Note:
- There are 4 homework assignments (20 points each, totaling 80 points).
- The number of in-class quizzes will match the number of in-person class sessions (20 points total).
- Extra credit is available through Homework 0 (up to 2 points).
- You must be present on the day a quiz is given to earn quiz points.
If you have questions about grading or how your grade will be calculated, please ask!
Policy on Late Homework Assignments and Incompletes:
In order to be able to provide the entire class with prompt feedback, late homework assignments will incur a 4-point penalty. However, extensions may be granted as needed for extenuating circumstances (e.g., conferences, family obligations) if requested at least three weeks in advance of the due date. As noted above, missed in-class quizzes cannot be made up.
Academic Policies
AI Statement
Specific permissions will be provided to students regarding the use of generative artificial intelligence tools on certain graded activities in this course. In these instances, I will communicate explicit permission, as well as expectations and any pertinent limitations for use and attribution. Without this permission, the use of generative artificial intelligence tools in any capacity while completing academic work submitted for credit—independently or collaboratively—will be considered academic dishonesty and reported to the Office of Academic Initiatives and Integrity.
Academic Integrity
As a core part of its mission, the University of Arkansas provides students with the opportunity to further their educational goals through programs of study and research in an environment that promotes freedom of inquiry and academic responsibility. Accomplishing this mission is only possible when intellectual honesty and individual integrity prevail.
Each University of Arkansas student is required to be familiar with and abide by the University’s Academic Integrity Policy at honesty.uark.edu/policy. Students with questions about how these policies apply to a particular course or assignment should immediately contact their instructor.
Emergency Preparedness
The University of Arkansas is committed to providing a safe and healthy environment for study and work. To that end, the university has developed a campus safety plan and an emergency preparedness plan to respond to a variety of emergency situations. The emergency preparedness plan can be found at emergency.uark.edu. Additionally, the university uses a campus-wide emergency notification system, UARKAlert, to communicate important emergency information via email and text messaging. To learn more and to sign up, visit: http://safety.uark.edu/emergency-preparedness/emergency-notification-system/
Inclement Weather
If you have any questions about whether class will be canceled due to inclement weather, please contact me. If I cancel class, I will notify you via email and/or Blackboard. In general, students need to know how and when they will be notified in the event that class is canceled for weather-related reasons. Please see here for more information.
Access and Accommodations
Your experience in this class is important to me. University of Arkansas Academic Policy Series 1520.10 requires that students with disabilities be provided reasonable accommodations to ensure their equal access to course content. If you have already established accommodations with the Center for Educational Access (CEA), please request your accommodations letter early in the semester and contact me privately so that we have adequate time to arrange your approved academic accommodations.
If you have not yet established services through CEA, but have a documented disability and require accommodations (conditions include but are not limited to: mental health, attention-related, learning, vision, hearing, physical, health, or temporary impacts), contact CEA directly to set up an Access Plan. CEA facilitates the interactive process that establishes reasonable accommodations. For more information on CEA registration procedures, contact 479–575–3104, ada@uark.edu, or visit cea.uark.edu.
Academic Support
A complete list and brief description of academic support programs can be found on the University’s Academic Support site, along with links to the specific services, hours, and locations. Faculty are encouraged to be familiar with these programs and to assist students with finding and using the support services that will help them be successful. Please see here for more information.
Religious Holidays
The university does not observe religious holidays; however, Campus Council has passed the following resolution concerning individual observance of religious holidays and class attendance:
When members of any religion seek to be excused from class for religious reasons, they are expected to provide their instructors with a schedule of religious holidays that they intend to observe, in writing, before the completion of the first week of classes.
Schedule
Following materials are only allowed for previewing for students registered in ESRM 64503. DO NOT DISTRIBUTE THEM on the internet. They will be removed after the course ended.
| Week | Date | Topic | Reading | Homework |
|---|---|---|---|---|
| 1 | 08/18 | Introduction and Overview | dplyr get started |
|
| 2 | 08/25 | Cancelled | ||
| 3 | 09/01 | No Class: Labor Day Holiday | ||
| 4 | 09/08 | Descriptive Stats. and General Linear Model | Maxwell & Delaney (2004): |
|
| 5 | 09/15 | Simple, Marginal, and Interaction Effects | Hoffman (2014) Ch. 2 | |
| 6 | 09/22 | Distributions and Estimation | Kutner et al. (2005), Ch.1 and Appendix A |
|
| 7 | 09/29 | Maximum Likelihood Estimation (MLE) | ||
| 8 | 10/06 | Generalized Linear Models and Matrix Algebra | Johnson & Wichern (2002) 6th ed.: |
|
| 9 | 10/13 | No Class: Fall Break | ||
| 10 | 10/20 | Matrix Algebra (Cont.) | PennState STAT 415 Sec.1.2 | |
| 11 | 10/27 | Path Analysis: Introduction | Kline (2010, 3th Ed.): |
|
| 12 | 11/03 | Path Analysis: Model Interpretation | Wright (1998) |
|
| 13 | 11/10 | Repeated Measures ANOVA | None | |
| 14 | 11/17 | Bayesian Analysis and MCMC | Enders (2010) Ch.6.An Introduction to Bayesian Analysis | |
| 15 | 11/24 | Johnson & Wichern (2002): |
|
|
| 16 | 12/01 | Q & A | ||
| 17 | 12/08 | Final Week |
|
Academic calendar for Fall 2024: Here
Course Materials
- Enders, C. K. (2010). Applied missing data analysis (1st Ed.). New York, NY: Guilford.
- Enders, C. K. (2022). Applied missing data analysis (2ed Ed.). New York, NY: Guilford.
- Hoffman, L. (2014). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY:Routledge Academic.
- Johnson, R. A. & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th Ed.). Upper Saddle River, N.J.: Prentice-Hall.
- Kline, R. B. (2010). Principles and practice of structural equation modeling (3th Ed.). New York, NY: Guilford.
- Kline, R. B. (2023). Principles and practice of structural equation modeling (5th Ed.). New York, NY: Guilford.
- Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th Ed.). New York, NY: McGraw-Hill.
- Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data. Mahwah, NJ: Erlbaum.
- Venables, W. N., Smith, D. M., & the R Core Team (2013). An introduction to R – Notes on R: A programming environment for data analysis and graphics (3.0.2 ed.). R Core Development team. Retrieved from http://cran.r-project.org/doc/manuals/R-intro.pdf.
McCutcheon, A. L. (2002). Basic concepts and procedures in single- and multiple-group latent class analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 56-88). Cambridge, United Kingdom: Cambridge University Press.
Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 56-88). Cambridge, United Kingdom: Cambridge University Press.
Wright, S. P. (1998). Multivariate analysis using the mixed procedure. Proceedings of the Twenty-Third Annual SAS Users Group International Conference, paper 229. Retrieved from http://www2.sas.com/proceedings/sugi23/Stats/p229.pdf.
- muticomp package vignettes
- Prof. Lesa Hoffman’s course: PSQF 6243 - Intermediate Statistical Methods
- Prof. Jonathan Templin’s course: EPSY 905 - Fundamentals of Multivariate Modeling
Citation
@online{zhang2025,
author = {Zhang, Jihong},
title = {ESRM 64503: {Applied} {Multivariate} {Analysis}},
date = {2025-08-14},
url = {https://www.jihongzhang.org/teaching/2024-07-21-applied-multivariate-statistics-esrm64503/ESRM64503_syllabus_Fall2025.html},
langid = {en}
}