Syllabus

PSYC 640: Graduate Statistics

Professor Dustin Haraden, PhD
Email/Office dxhgsh@rit.edu; Eastman Hall - 3378
Office Hours Wednesdays 9 - 11am or By Appointment
Class Times Mondays 8:00 - 10:50am
Class Location Wallace - 4640

For a PDF copy of the syllabus: Download File

Course Overview

This course is the introduction to statistics for graduate students. The goal of the course is to provide a grounding in statistical concepts, methods and application to research. I aim to increase student’s confidence in using these techniques and introducing them to R. Topics will range from including mathematical conceptualizations to practical application with various techniques ranging from descriptive statistics to regression.

Course Materials

We will be using R for all data wrangling, visualization, and analysis. You may use another statistical program in this course, but I will only be providing examples in R. Students must have the latest version of R and it is strongly recommended that students also download the RStudio GUI, both can be found here. Both types of software are free.

We will primarily be referring to chapters in the following textbooks:

These textbooks are available for free online and able to be downloaded. You may choose to purchase a paper copy if you wish, but it is not required.

All additional readings will be provided by the instructor.

Important

Note: Readings on the schedule will need to be completed prior to the course they are listed for. We will build on the concepts you read about in that specific class period, so it is important that you have read.

Course Goals

  1. Build confidence in statistical reasoning & analysis.
  2. Apply regression-based methods to real-world research questions.
  3. Develop practical R skills for data wrangling, visualization, and reporting.
  4. Produce a portfolio-ready, reproducible final analysis.

Evaluation and Grading

Your grade is a reflection of your consistent effort, active engagement with the material, and ability to apply new concepts. The components are designed to build on one another, leading to a comprehensive understanding of data analysis.

Component Weight
Weekly Labs 30%
Journal Entries 10%
Participation & Engagement 15%
Midterm Project 20%
Final Project 25%

Weekly Labs

These are hands-on R assignments that directly reinforce the concepts from the week’s class. They are your primary opportunity to practice coding, build models, and interpret results. Labs will be submitted as R Markdown files, and your lowest score will be dropped. Labs will be due the Sunday night (11:59pm) before the next class.

Journal Entries

Each week, you will submit a short, reflective journal entry. This is a space for metacognition—thinking about your own learning. Prompts will include questions like, “What was the clearest concept this week, and why did it click?” or “What was the ‘muddiest’ point for you, and what question would you ask about it?”. They can also take the form of just a general reflection. I want to get to know you and your learning throughout this process. This can also include anything related to your personal life or mental health that you would like for me to know, such as whether you are struggling to balance classes and research, having trouble creating a workspace at home, or whether you can balance time spent on campus and off. This can also be completely random things, like a news article you can’t stop thinking about, or a favorite TV show, movie or book that you just love (especially if it is LOTR or Cosmere related). The content of what you write has no impact on your grade. In addition, what you write will be kept confidential.

The purpose of this “assignment” is to help facilitate communication between you and me. I have found other instructors using this and I would like to be able to develop supportive relationships with students, so I decided to implement this. Other instructors reported that they found that many students were more comfortable discussing questions and concerns in their journal assignments rather than through email.

In-Class Engagement & Activities

Our class is a workshop, and your active participation is key. This portion of your grade is earned by being present and engaged. This includes participating in group discussions, engaging with the readings, working with peers on problems, and completing the small, hands-on coding exercises we’ll do together or in small groups in class. This is a low-stress grade based on your consistent effort and collaboration during our classes.

Midterm Project

This is a comprehensive analysis of a dataset I will provide. You will be asked to clean and visualize the data, formulate a research question, build an appropriate regression model, check its assumptions, and write a concise report of your findings. This project assesses your mastery of the first half of the course and will be due before the first class after Fall Break.

Final Project

For your final project, you will choose a dataset (either your own research data or from a list of options), develop your own research questions, and conduct a full analysis from start to finish. You will present your work in a short, manuscript-style report and a brief “lightning talk” to the class in our final meeting. This is your capstone assignment to demonstrate your independent data analysis skills.

Grade Scheme

Grade A A- B+ B B- C+ C C- D F
Percentage 93+ 90-92 87-89 83-86 80-82 77-79 73-76 70-72 60-69 <60

Course Policies

Late Policy

“A Wizard is never late, nor are they early. They arrive precisely when they mean to.” 🧙‍♂️

Thanks Gandalf. Super helpful. Unfortunately, we are not wizards and late penalties will be applied to work that is not on time. There will be a 15% deduction on the first day. And a 5% increase for each day beyond the deadline. Work will not be accepted beyond 5 days after the deadline.

Statement on Reasonable Accommodations

RIT is committed to providing academic adjustments to students with disabilities. If you would like to request academic adjustments such as testing modifications due to a disability, please contact the Disability Services Office. Contact information for the DSO and information about how to request adjustments can be found at www.rit.edu/dso. After you receive academic adjustment approval, it is imperative that you contact me as early as possible so that we can work out whatever arrangement is necessary.

Mandatory Reporting

As an instructor, I have a mandatory reporting responsibility as a part of my role. It is my goal that you feel comfortable sharing information related to your life experiences in classroom discussions, in your written work, and in our one-on-one meetings. I will seek to keep the information you share private to the greatest extent possible. However, I am required to report information I receive regarding sexual misconduct or information about a crime that may have occurred during your time at RIT. 

Statement on Title IX

RIT is committed to providing a safe learning environment, free of harassment and discrimination as articulated in our university policies located on our governance website. RIT’s policies require faculty to share information about incidents of gender-based discrimination and harassment with RIT’s Title IX coordinator or deputy coordinators when incidents are stated to them directly. The information you provide to a non-confidential resource which includes faculty will be relayed only as necessary for the Title IX Coordinator to investigate and/or seek resolution. Even RIT Offices and employees who cannot guarantee confidentiality will maintain your privacy to the greatest extent possible.

If an individual discloses information during a public awareness event, a protest, during a class project, or advocacy event, RIT is not obligated to investigate based on this public disclosure. RIT may however use this information to further educate faculty, staff and students about prevention efforts and available resources.

If you would like to report an incident of gender based discrimination or harassment directly you may do so by using the online Sexual Harassment, Discrimination and Sexual Misconduct Reporting or anonymously by using the Compliance and Ethics Hotline. If you have a concern related to gender-based discrimination and/or harassment and prefer to have a confidential discussion, assistance is available from any of RIT’s confidential resources (listed below).

  • RIT Counseling and Psychological Services
    • 585-475-2261 (V)
    • 585-475-6897 (TTY)
    • www.rit.edu/counseling
  • NTID Counseling and Academic Advising
    • 585-475-6400
    • www.ntid.rit.edu/counselingdept
  • RIT Student Health Center
    • 585-475-2255 (V)
    • www.rit.edu/studentaffairs/studenthealth
  • Center for Religious Life
    • 585-475-2137
    • www.rit.edu/studentaffairs/religion
  • RIT Ombuds Office
    • 585-475-7357
    • 585-475-6424
    • 585-286-4677 (VP)
    • www.rit.edu/ombuds/contact-us

Academic Integrity Statement

As an institution of higher learning, RIT expects students to behave honestly and ethically at all times, especially when submitting work for evaluation in conjunction with any course or degree requirement. The Department of Psychology encourages all students to become familiar with the RIT Honor Code and with RIT’s Academic Integrity Policy. RIT’s policy on academic integrity requires the instructor to investigate of any suspected breach of academic integrity. If the preponderance of evidence indicates a breach of academic integrity, the student who did so may incur a consequence up to and including failure for the entire course.

About Generative AI

You may use generative AI tools (such as ChatGPT, Grammarly, or CoPilot) as a support for your work in this course. However:

  • You must personally review, edit, and take ownership of all submitted work.

  • Any use of AI must be acknowledged in a brief note at the end of the assignment (e.g., “I used ChatGPT to generate initial bullet points for my resume, which I then revised and expanded.”) as well as being properly cited (RIT Library Citation Infoguide)

  • AI tools may not be used to generate entire assignments without your input or to misrepresent your work. Submitting unedited or minimally edited AI output as your own is considered academic dishonesty.

  • In professional contexts, you will be expected to present work that is authentically your own — this course is practice for that.

If I suspect that the work that you have turned in is using AI, we will have to have a conversation to determine the next steps. Turning in AI work is considered plagiarism, and you may be asked to re-do the assignment, or possibly receive a 0 on the assignment. Your information may also be submitted to the university as a Breach of Academic Integrity.

RIT COVID-19 Safety Plans

RIT is committed to the safety of the RIT community and beyond. Because the situation is still in a rapid state of change, checking the RIT Ready website, and specifically the RIT Safety Plan for the most up to date information is recommended: https://www.rit.edu/ready/rit-safety-plan.

Changes to the Syllabus

I have provided this syllabus as a guide to our course and have made every attempt to provide an accurate overview of the course. However, as instructor, I reserve the right to modify this document during the semester, if necessary, to ensure that we achieve course learning objectives. You will receive advance notice of any changes to the syllabus through myCourses/email.

Course Plan

This is subject to change and instructor will inform the students as soon as possible.

  • Week 1 — Getting Started with R & Tidy Data

    • We’ll dive right into our primary tool, R. This session covers installing R and RStudio, navigating the interface, understanding R projects, and learning the foundational syntax of the R language and the tidyverse, including data types and basic functions.

  • Week 2 — LABOR DAY (NO CLASS)

    • Continue to develop skills and comfort in R. You will be provided with readings and other practice problems, and maybe a video lecture.

  • Week 3 — Describing, Visualizing & Communicating

    • What is in our data? We’ll learn how to calculate descriptive statistics (e.g., mean, median, standard deviation) and master the “grammar of graphics” to create compelling, publication-quality plots with ggplot2. We will also introduce R Markdown for creating reproducible reports.

  • Week 4 — Designing Studies & Sampling

    • This is a conceptual week focusing on the foundations of research. We’ll discuss different research designs (experimental, correlational), sampling methods, the distinction between a sample and a population, and the fundamental logic of null hypothesis significance testing (NHST).

  • Week 5 — Correlations & Effect Sizes

    • We’ll quantify the relationship between two continuous variables using covariance and correlation. We will also introduce the concept of effect sizes as a standardized way to describe the magnitude of a relationship, moving beyond a simple “yes/no” significance test. “How strong is the association?”

  • Week 6 — Comparing Groups

    • We’ll explore classic methods for comparing group means, including independent and paired-samples t-tests. We’ll conduct these tests in R and learn how to interpret their output. This will serve as our first formal hypothesis-testing tool.

  • Week 7 — Building to Regression: Variability & Model Fit

    • We’ll introduce the core logic of the General Linear Model by partitioning variance. Concepts like sums of squares will be introduced to help us understand how a model “explains” variance in an outcome variable. We will introduce R2 as a fundamental measure of model fit. “How much of the outcome can we explain?”
  • Week 8 — FALL BREAK (NO CLASS) 🍂

  • Week 9 — Simple Linear Regression

    • The formal introduction to the regression model: Yi​=β0​+β1​Xi​+ϵi​. We will learn how to estimate the model’s parameters (intercept and slope), interpret their meaning, and assess overall model fit using the lm() function in R. “What is the exact formula for prediction?”

  • Week 10 — Multiple Regression I: Adding Predictors

    • We’ll expand our model to include multiple continuous predictors. Key topics include interpreting partial slopes (the effect of one predictor while controlling for others), understanding adjusted R2, and identifying issues like multicollinearity.

  • Week 11 — Multiple Regression II: Categorical Predictors

    • How do we include groups (e.g., experimental conditions) in our regression model? We’ll learn about dummy coding and indicator variables. This is where we will explicitly demonstrate that t-tests and ANOVA are simply special cases of regression.

  • Week 12 — Assumptions + Model Diagnostics

    • Is our model trustworthy? We will learn how to check the assumptions of ordinary least squares (OLS) regression, including linearity, normality of residuals, and homoscedasticity. We will use graphical methods in R to diagnose problems and discuss potential remedies.

  • Week 13 — Expanding Regression

    • Topics: We will explore ways to capture more complex relationships in our data. The primary focus will be on adding interaction terms to the model to test for moderation, but we may also touch on including non-linear relationships (e.g., quadratic terms) or talk about some mediation!

  • Week 14 — Model Building + Comparison

    • How do we choose the best model? We will discuss practical strategies for building models, such as hierarchical regression, and learn how to use information criteria (like AIC and BIC) to compare different competing models.

  • Week 15 — Making R work for you

    • We’ve worked a whole lot with R, but what are some of the cool things that it can do. We will explore and organize what we have learned so far to solidify the use of R for reproducible workflows!

  • Week 16 — Wrapping Up + Workshop

    • This final session will serve two purposes. First, it will be a dedicated workshop for you to get help and ask questions about your final projects. Second, we will have a course wrap-up, reviewing the major themes and discussing next steps for your statistical journey.