General Information
- Course Code: ESRM 64103
- Credits: 3 CH
- Course time and location: Wednesday | 5:00 PM - 7:45 PM; GRAD 239
- Instructor: Dr. Jihong Zhang
- Contact Information: jzhang@uark.edu
- Personal Website: http://jihongzhang.org
- Office Location: GRAD 133B
- Office Hours: Monday 2:00-5:00PM or by appointment
- Office Phone +1 479-575-5235
- Semester: Fall 2025
Course Overview
Welcome to ESRM 64103: Experimental Design in Education. This course is designed to strengthen your ability to analyze and interpret research data using analysis-of-variance (ANOVA) techniques. Building on your foundation from ESRM 64003 (Educational Statistics and Data Processing), you will gain skills essential for advanced ESRM coursework.Prerequisites: ESRM 64003 (Educational Statistics and Data Processing) or an equivalent course with a grade of C or better.
Course Objectives
By the end of this course, you will be able to:
- Select appropriate statistical techniques based on research methods and questions.
- Understand the factors that influence the statistics discussed in class.
- Use the R language to display data and conduct statistical analyses.
- Apply both descriptive and inferential methods to analyze empirical data.
- Interpret, write, and discuss results in accordance with APA format.
- Develop a conceptual understanding of each method, enabling you to apply and interpret statistical techniques appropriately and creatively.
Note: This is not a mathematics course, but a strong conceptual grasp of the methods is essential for meaningful application and interpretation.
Course Topics
Topics covered in this course include:
- Overview of ANOVA Designs
- Model Comparison: Two-Group Situation and One-Way Designs
- Tests of Single and Multiple Comparisons
- Two-Way Between-Subjects Factorial Designs
- Interactions and Simple Main Effects
- One-Way and Multiple Way Within-Subjects Designs
- Designs with Both Between-Subjects and Within-Subjects Factors
Recommended Textbooks
- Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd ed.). Routledge.
- Fox, J. (2008). Applied Regression Analysis and Generalized Linear Models (2nd ed.). Sage.
- Kuhn, M., & Silge, J. (2023). Tidy Modeling with R. https://www.tmwr.org/
Resources
- Course Materials: All lecture notes, homework, and project instructions will be posted on Blackboard (https://learn.uark.edu/webapps/login/).
- Technology: Please bring your own laptop to class for computer-based practice.
- Assignment Submission: All assignments (except exams) must be submitted via Blackboard by the due date.
Online Resource
Attendance
Attendance is expected at the graduate level. If you know you will be out of town when an assignment is due, please make arrangements to complete and submit your work in advance. Deadlines are set to provide the maximum allowable time for completion.
Assignments
Homework
Homework assignments are designed for both instructional and assessment purposes. Each homework will be assigned approximately every two weeks and will consist of questions related to course content, including material from lectures and computer-based exercises. You are strongly encouraged to complete and submit each homework by the specified due date via the online Homework Portal (see Homework portal). Timely submission is essential for staying on track with the course schedule.
Grading Criteria
This is a graduate-level course. Students are expected to read assigned textbook chapters and lecture notes, attend all in-person classes, complete all assignments thoroughly, and actively participate in class activities. Consistent engagement in these activities is typically associated with successful course performance. Grades will be determined according to the following point system:
| Raw Score | Percentage | |
|---|---|---|
| Homework | 80 | 80% |
| In-Class Quiz | 20 | 20% |
| Bonus Points | 2 | — |
| Total | 102 | 100% |
The grading scale for this course is as follows:
| Percentage of Points | Grade |
|---|---|
| 100–90 | A |
| 89–80 | B |
| 79–70 | C |
| 69–60 | D |
| < 60 | F |
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. In that regard, 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: http://safety.uark.edu/emergency-preparedness/emergency-notification-system/
Inclement Weather
If you have any questions about whether or not 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 cancelled 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 are 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 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 an 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.
Homework Portal
- Homework 0: Demo (2 bonus points)
- Homework 1
- Homework 2
- Homework 3
Please see the online gradebook for your current grade.
Schedule
Following materials are only allowed for previewing for students registered in ESRM 64103. DO NOT DISTRIBUTE THEM on the internet. They will be removed after the course ended. All homework are due at noon on next Wednesday.
| Week | Date | Topic | HW |
|---|---|---|---|
| 1 | 08/20 | Lec1: Introduction and Overview | Homework 0: Demo |
| 2 | 08/27 | Lec1 (Online): R programming |
|
| 3 | 09/03 | Lec2: Hypothesis testing | HW#1 Due on 09/10 Noon |
| 4 | 09/10 | Lec3: One-way ANOVA | HW#1 Answer |
| 5 | 09/17 | Lec4: ANOVA Assumption Checking | |
| 6 | 09/24 |
Data: week5_example.csv |
HW#2 Due on 10/01 Noon |
| 7 | 10/01 | Lec6: Validity | HW#2 Answer |
| 8 | 10/08 | Lec7: Blocking design (1) | |
| 9 | 10/15 | ||
| 10 | 10/22 | Lec9: 2-Way ANOVA (1) | |
| 11 | 10/29 | Lec10: 2-Way ANOVA (2) | |
| 12 | 11/05 | Lec11: Repeated Measure ANOVA | |
| 13 | 11/12 | Lec12: ANCOVA | |
| 14 | 11/19 | Lec13: Mixed ANOVA | |
| 15 | 11/26 | No Class: Thanksgiving Break | |
| 16 | 12/03 | HW#3 Due on 04/28 7PM |
|
| 17 | 12/10 | Final week: Homework Q&A |
Citation
@online{zhang2025,
author = {Zhang, Jihong},
title = {ESRM 64103: {Experimental} {Design} {In} {Education}},
date = {2025-01-13},
url = {https://www.jihongzhang.org/teaching/2025-01-13-Experiment-Design/ESRM64103_syllabus_Fall2025.html},
langid = {en}
}