Lecture 01: Welcome to ESRM64103

2 Overview

2.1 Presentation Outline

1. Self introduction

2. The syllabus

   1. Goal of this class

   2. Delivery method of class materials

   3. Assignments

   4. Policy

   5. Weekly schedule

3. Brief introduction to statistical software

2.2 Self Introduction

University of Kansas (2015-2019)

University of Iowa (2019-2022)

Chinese University of Hong Kong (2022-2023)

2.3 My job

2.4 Your turn

• Tell me:

  1. Your name
  2. Your department and program
  3. Why you are interested in Experimental design

2.5 About this class

• No examination

• Three graded homework assignments, plus a Homework 0 demo, with multiple-choice questions and light calculations

• Provide big picture and general directions rather than statistical details

• A lot of empirical examples

• All materials use R!

2.6 This Course is:

1. Required for Graduate Certificate in Educational Statistics and Research Methods
2. Prerequisite to Multiple Regression and Applied Multivariate Statistics
3. to provide the broad picture of one of the most popular research design - group comparisons

2.7 Learning Path of Graduate Certificate

Path A Statistics Path From foundations to multivariate modeling

1. 01 ESRM 64003 Educational Statistics & Data ProcessingBuild statistical foundations Core
2. 02 ESRM 64103 Experimental DesignDesign and analyze group comparisons You are here
3. 03 ESRM 64203 Multiple RegressionModel multivariable relationships Core
4. 04 ESRM 64503 Applied Multivariate StatisticsExtend analysis to multiple outcomes Elective
5. 05 ESRM 65203 Structural Equation ModelingTest complex latent-variable models Elective

Path B Measurement Path From foundations to modern test theory

1. 01 ESRM 64003 Educational Statistics & Data ProcessingBuild statistical foundations Core
2. 02 ESRM 64103 Experimental DesignDesign and analyze group comparisons You are here
3. 03 ESRM 64203 Multiple RegressionModel multivariable relationships Core
4. 04 ESRM 66503 Measurement and EvaluationDevelop assessment foundations Elective
5. 05 ESRM 67503 Item Response TheoryApply modern test theory Elective

2.8 Class Time

1. Unit 1 (17:00 - 17:45): Lectures about concepts
2. Unit 2 (18:00 - 18:45): Examples
3. Unit 3 (19:00 - 19:45): (Optional) Self Practice with R Code on your laptop and Ask Questions

2.9 What To Expect This Semester

• Philosophy: Focus on accessibility + learning-by-doing

  • The class heavily emphasize on hands-on task-oriented practices

  • No anxiety-prone tasks (e.g., hand calculations, memorizing formulas)

  • No anxiety-prone methods of evaluation (e.g., timed tests)

• Materials:

  • Lecture slides present concepts—the what and the why

  • Example documents: reinforce the concepts and demonstrate the how using software—R packages

  • All available at the course website (hosted outside of Blackboard)

    • Let me can show you how to use the website

2.10 Assignments and Grading

• Participants will have the opportunity to earn up to 100 total points in this course.

• Up to 80 points can be earned from three homework assignments.

• Up to 20 points may be earned from submitting in-class quiz. In-class quiz will be delivered randomly in class. These will be graded on effort only—incorrect answers will not be penalized.

• Bonus points (2 points)

  • There may be other opportunities to earn extra credit at the instructor’s discretion.

• Assignment Deadlines:

  • Assignments must be submitted by the stated deadline. If you know you will be unavailable when an assignment is due, make arrangements to complete and submit it in advance.

2.11 Homework Delivery Format

1. Homework will typically be completed and submitted through a Google Form, with the link posted in the syllabus.

2.12 Our Other Responsibility

• My job (besides providing materials and assignments):

  • Answer questions via email, in individual meetings, or in group-based zoom office hours—you can each work on homework during office hours and get immediate assistance (and then keep working)

    • Email me first

• Your job (in descending order of timely importance):

  • Ask questions—preferably in class, but any time is better than none

  • Frequently review the class material, focusing on mastering the vocabulary, logic, and procedural skills

  • Don’t wait until the last minute to start homework, and don’t be afraid to ask for help if you get stuck on one thing for more than 15 minutes

    • Please email me (jzhang@uark.edu) a screenshot of your code+error so I can respond easily

  • Practice using the software to implement the techniques you are learning on data you care about

  • Do the readings for a broader perspective and additional example (best after the lecture)

2.13 More About Your Experience in this Class

• Attendance: Expected at the graduate level

  • Please do not attend in-person if you might be sick!

  • Please do not attend if you received the inclement weather notification

  • You can also join the class via Zoom

  • You won’t miss out: I will post YouTube recordings (audio + screen share) by requested at the course website.

• Changes will be sent via email by 9 am on class days

  • I will update the homework and in-class quiz links on class days. If not uploaded, then there are two situations: (1) I forget to do that. I will re-upload later and notify you by emails. (2) I decide not to upload it or remove it.

  • I may change to zoom-only for dangerous weather or if I am sick.

2.14 Statistical Software

• I will show examples primarily using R and R packages. Some important R packages include:

  • Tidyverse: a comprehensive R package including multiple mini packages for multiple data cleaning, data transformation.

  • ggplot2: a popular package for data visualization

• Why not SPSS?

  • SPSS could only be used for some—but not all–of our content

  • More importantly, it doesn’t have as much room to grow; R has many new packages being developed via CRAN and GitHub

• Why not SAS?

  • SAS is not open-sourced, meaning that we cannot check source code if something goes wrong

  • SAS is also commercial, but R is free

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2.15 What We Will Cover

• Hypothesis Testing
• ANOVA
  • One-way
  • Two-way
  • Repeated-measure
• ANCOVA
• Linear Mixed Model

3 Unit 2: Brief Introduction to R

3.1 Why R?

• There are some point to consider

  • R packages are only as good as their authors (so little quality control)

  • Syntax and capabilities are idiosyncratic to the packages

• The good things are:

  • If you really master R, you can do by yourself (write your own algorithm for complex model)

  • You can check the source code of R packages and know where issues come from

  • You can communicate with R package authors and provide some suggestions

  • You can be R package author yourself and be famous

3.2 What is R

• R is an comprehensive statistical and graphical programming language

• We can use R language via multiple graphical user inferences or IDE, i.e., terminal, VS Code or RStudio.

  • We will mainly focus on RStudio because of its convenience

  • Rstudio is a product of posit company and is free to use for personal use

Terminal

Rstudio

VSCode

3.3 RStudio User Interface

3.4 More RStudio

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3.5 Installation of R and RStudio

• Download and install the current version of R from CRAN:

  • for Linux

  • for Windows

  • for MacOS

• After installing R, download and install the current version of RStudio Desktop from Posit.

• After installation of R and RStudio, you can open up the RStudio to start your R programming.

  • however, your R only has the base package

  • To enhance its utility, most users will install R packages for certain purposes

3.6 R packages

• R packages are uploaded to some platforms (i.e., CRAN or Github) by researchers or companies

  • Those R packages typically have their version numbers. Some functions may be available for some version (like Ver. 1.1) but not be available in other versions.

  • Do not upgrade your packages if you code is running well

• R users are free to download and use those R packages

  • To download certain package, you should know package name

  • For example, if you want to download the latest version of tidyverse package, you can type in following command in the console panel of Rstudio

  install.packages("tidyverse")

  • Or if you want to install the older version of package

  library(devtools)
  install_version("tidyverse", version = "1.3.0", repos = "http://cran.us.r-project.org")

  • If you find some error message like:

  Error in `unknown_function()`:
  ! could not find function "unknown_function"

3.7 More about R packages

• CRAN (Comprehensive R Archive Network) is a network of servers around the world that store identical, up-to-date, versions of code and documentation for R.
  • It contains most stable version of packages.
  • Most of time, we download package from CRAN
• Github is for the fast development for R packages

  • It contains the up-to-date version of R which may potentially be unstable

  • You can download the package from Github using pak package

    pak::pak("tidyverse/ggplot2")

  • You can update the package and its dependencies

    pak::pkg_install("ggplot2", upgrade = TRUE)

3.8 R functions

• To operate certain tasks, you need to use functions contained in R packages

  • Note: Package is loaded in your session before you can call the function name without specifying the package name

    install.packages("remotes") # if you did not install the remotes package before
    library(remotes)
    remotes::install_github("JihongZ/ESRM64103", force = TRUE)

3.9 R functions (Cont.)

• Question: How do you know whether the package has been loaded or not

• You can use sessionInfo function

  sessionInfo()

  R version 4.5.2 (2025-10-31)
  Platform: aarch64-apple-darwin20
  Running under: macOS Tahoe 26.5.2
  
  Matrix products: default
  BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
  LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
  
  locale:
  [1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
  
  time zone: America/Chicago
  tzcode source: internal
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base     
  
  loaded via a namespace (and not attached):
   [1] digest_0.6.39     fastmap_1.2.0     xfun_0.56         Matrix_1.7-4     
   [5] lattice_0.22-9    httpgd_2.0.4      reticulate_1.45.0 glue_1.8.0       
   [9] knitr_1.51        htmltools_0.5.9   png_0.1-8         rmarkdown_2.30   
  [13] lifecycle_1.0.5   cli_3.6.5         unigd_0.2.0       vctrs_0.7.1      
  [17] grid_4.5.2        systemfonts_1.3.1 compiler_4.5.2    tools_4.5.2      
  [21] pillar_1.11.1     evaluate_1.0.5    yaml_2.3.12       Rcpp_1.1.1       
  [25] otel_0.2.0        rlang_1.1.7       jsonlite_2.0.0   

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• sessionInfo() outputs multiple info:

  • R version, Operations System, Matrix operation package, Locale

  • Attached packages (you can call the functions of those package)

  • Loaded package via a namespace (and not attached), which you cannot call functions and need to library or require them

3.10 Run R code

• After you finish R script, you have multiple ways of running the code:

  • Method 1: you can click Run button in the top right-head of Rstudio

  • Method 2: you can select certain code and press Ctrl + Enter (Win) or Command + Return (Mac)

  • Method 3: you can Rscript [FILENAME].r to run the whole script

  • Method 4: you can using R notebook to interactively run R code

	Script file is .R	Script file is .rmd or .qmd
Run the whole script	Method 1 Method 3	Method 4
Run the partial script	Method 2	Method 4

3.11 Summary

1. Note that the syllabus, schedule, and all materials are uploaded online the week before class.
2. We learn that R, Rstudio, and Quarto (.qmd) can be used to execute R code/syntax.
3. In-class quiz will be administered randomly. Should be quick and easy. Don’t be stressful ever!
4. Office hours are Monday from 2:00 PM to 4:00 PM or by appointment. Feel free to stop by my office or contact me with questions.

3.12 Next Week

3.12.1 Reading

SWE-bench: Can Language Models Resolve Real-world Github Issues?

3.12.2 Open Questions

1. What experimental design do you observe in this paper?
2. Which aspects of the design or findings match your expectations?
3. Which aspects of the design or findings are unexpected?