Analysing Data: Lecture 01
Jennifer Mankin and Martina Sladekova
29 January 2025
Overview
- Introductions
- Module Structure and Sessions
- Attendance Policy and Assessments
- Materials and Resources
- Kahoot! Points and Hex Awards
- Data skills
Note
Differences from PaaS highlighted with ✨sparkles!✨
Module Information
What It Says On The Tin
First year core statistics and research methods module
Comprises lectures, skills labs, and practical sessions
What will you learn?
- Data literacy: how to think like a scientist about data
- Working with data: cleaning, wrangling, summarising
- R skills: using RStudio/Quarto, writing and reading code
- Statistics: NHST, common tests, linear model
Contact
✨Module email: analysingdata.psychology@sussex.ac.uk
Module Discord rolled over from PaaS!
Dr Jennifer Mankin
Confidential queries: J.Mankin@sussex.ac.uk
- Convenor and primary point of contact
- Lectures, Skills Labs, Practicals
- All module admin, assessments, queries
Dr Martina Sladekova
- Co-convenor
- Website Lead
- Lectures, Skills Labs, Practicals
Dr Jenny Terry
- Lectures, Skills Labs, Practicals
Module Structure
Types of Sessions
Lecture
- One-hour lecture session Wednesday mornings
- Concepts, ideas, statistical tests and principles
✨Skills Lab
- One-hour interactive, live-coding session Thursday afternoons
- How to think about and work with data
✨Practical
- Two-hour supported working time
- Ask questions and get help
- Complete the tutorial/worksheet
- Take a quiz
- Multiple sessions throughout the week
Important
Skills Labs are not optional! They are distinct from the lectures and will contribute to your assessments.
Topics Structure
Teaching sessions are grouped around topics, which cover two weeks
First Week
- Most materials released Tuesdays
- Lecture, Skills Lab, Tutorial
- Posit Cloud project
- Lecture Wednesday morning
- Skills Lab solutions Thursday afternoon
Second Week
- Practical session
- Complete tutorial
- Complete worksheet
- Worksheet quiz
Topics by Week
| Week | Lecture | Skills Lab |
|---|---|---|
| 1 | Introduction | Logical assertions, filter() |
| 2 | Sampling and Distributions | Cleaning with mutate() |
| 3 | Uncertainty and CIs | Using the pipe |> |
| 4 | NHST | Summarising data |
| 5 | t-tests | t-tests |
| 6 | Correlation | Correlation |
| 7 | Chi-squared | Chi-squared |
| 8 | Linear model 1 | Linear model 1 |
| 9 | Linear model 2 | Linear model 2 |
| 10 | Linear model 3 | Linear model 3 |
| 11 | QPRs | Kahoot revision |
| ✨ | SPRING | HOLIDAY |
Attendance
Lectures/Skills Labs
- Attendance is required and recorded via PIN
- Delivered in person, recordings posted on Canvas
If you miss a lecture or skills lab…
- Watch the recording on Canvas
- Take notes and follow along with the materials
- For Skills Labs, try out the code yourself
- Ask questions on Discord, in your practicals, or come to a drop-in for extra one-on-one attention!
Attendance is Key
- Strong recommendation to attend live sessions consistently
- Use recordings to supplement or review, not replace, lecture attendance
- Highest marks for students who attended live lecture and reviewed recordings (Bos et al., 2016)
- Attendance and recording usage both predict achievement (Nordmann et al., 2019)
- Guidelines for students (Nordmann et al., 2020)
Practicals
- Attendance is required and recorded manually
- Interact in some way with tutors (any way is fine)
- Ask questions, ask for help, get your work checked before the quiz!
If you attend any practical, you can access help and the quiz as normal.
If you miss a practical…
- You can attend another practical in the week to take the quiz, but you will not be marked present
- If you can’t attend another practical, you must notify of your absence in order to be able to use the mop-up quiz
More on this in just a moment…
Changing Your Timetable
- Only allowed where unavoidable (e.g. consistent clash)
- Email the School Office - the teaching team cannot help!
Assessments
All Assessments
See detailed information on Canvas
| What | Weight | When |
|---|---|---|
| Worksheet quizzes | 20% | Every week in practical sessions |
| Take-away paper (TAP) | 20% | 48-hour period, due Week 7 Wednesday |
| Research participation | 10% | Throughout term, due Week 11 Friday |
| Exam | 50% | A2 assessment period |
Worksheet Quizzes
- Before or during each practical, complete a worksheet
- In the second hour, complete a marked quiz
- Next week: practice only!
- ✨Covers the lecture, skills lab, tutorial, and worksheet
- ✨You must attend a practical in order to access the quiz
- We will not give out access codes to individual students!
Worksheet Quizzes
- Final mark: mean of your best quiz scores
- Lowest two scores dropped automatically, including 0s due to absence
- You can only submit an EC claim after >2 missed quizzes
This week: Practice quiz on Canvas anytime
Next week: Practice quiz in practicals only!
Week 3: First marked quiz
Worksheet Quizzes - Mop-Up
Each week on Friday afternoon we will send out a “mop-up” quiz code via Canvas Announcement.
To use this code to complete the quiz, at least one of the following must be true:
- You were absent from your practical session that week, and have already notified the School of the absence for the relevant practical.
- You were unable to complete your quiz in your practical due to circumstances outside your control (e.g. your computer broke, WiFi failure), and have contacted the convenor to agree to use the mop-up quiz.
- You are registered with Disability Advice and have reasonable adjustments in place, and have contacted the convenor to agree to use the mop-up quiz for accessibility reasons.
Important
If you complete the mop-up quiz, but have neither a notified absence nor an agreement with the module convenor, the mark will be replaced with a 0.
✨Take-Away Paper
Take-Away Paper Information released around Week 5
- 48 hours Monday - Wednesday Week 7
- Series of tasks which may include:
- Making and justifying data analysis decisions
- Data inspection/cleaning, describing
- Data manipulation and summarising
- Performing and reporting a statistical analysis
- To best prepare, complete tutorials and worksheets, and come to Skills Labs!
Research Participation
SONA information on Canvas and on the Psychology website
- Complete five hours of research participation before the end of term
- Via SONA, same as last term
- Requirement to complete credits live
- You must complete 20% of your credits in “live” studies (in person, or over Zoom)
- Students registered with the Student Support Unit are exempted automatically
- Can participate in live studies but are not required to
Research Participation - Opportunities
Get a head start on your SONA participation!
Analysing Data Learning Study
- 2 credits (one online, one in person)
- Three steps:
- Complete Part 1 before the Skills Lab tomorrow
- Attend the Skills Lab session in person
- Complete Part 2
Exam
Exam Information to be released near the end of term
- Two-hour, 50-question MCQ exam in A2 (May)
- ✨Covers everything on the module, both stats and R!
- Will take place online
AI
There is NO acceptable use of AI on this module.
Any suspected use of AI on any assessment will be treated as academic misconduct.
But what is the problem with getting a little help? Well…
The Cost of AI: Climate Disaster
- Ever-increasing energy consumption
- Reversal of green energy targets to meet outsized demand
- “Globally, the electricity consumed by datacentres is expected to match the entire power consumption of Japan within the next two years”
- “…[I]n the near-term most markets with a large density of datacentres end up using more gas power: ‘It would absolutely pile pressure on already very difficult clean power targets.’”
- Deprivation of already-deprived communities to power AI
- Use of drinkable fresh water as server coolant
- “In July 2022…Microsoft pumped in about 11.5 million gallons of water to its cluster of Iowa data centers…That amounted to about 6% of all the water used in the district, which also supplies drinking water to the city’s residents.”
- “ChatGPT gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions.”
The Cost of AI: Destruction and Theft of Creative Industries
- SAG-AFTRA strikes over AI use and compensation in film, TV, and video games
- Lawsuits for copyright infringement
- Famous musicians join other voices criticising removal of copyright protections
- “The government is also consulting on an overhaul of copyright laws that would result in artists having to opt out of letting AI companies train their models using their work, rather than an opt-in model.”
- Sale of scientific papers for AI training with no author compensation or consultation
- AI image generation steals and takes work from human artists
- “These programs rely entirely on the pirated intellectual property of countless working artists, photographers, illustrators and other rights holders.”
The Cost of AI: Privacy, Reality, and Human Rights
- Sensitive data read and decisions made by “black box” AI without consent
- “The DWP’s data protection impact assessment also says consulting individuals about this way of processing their data is ‘not necessary as…these solutions will increase the efficiency of the processing’.”
- Propagation of human biases offloaded to supposedly “objective” computer systems
- Surveillance and AI
- Use of AI to influence public opinions, elections and distort reality
- Internet flooded with AI “slop”
Who Benefits?
- Tech billionaires get more billions
- You get to not learn what you are paying tuition to learn :)
In Summary…
There is no acceptable use of AI on this module.
Any suspected use of AI for any assessment will be treated as academic misconduct.
AI use falls under personation
- “Someone, or software, other than the student prepares the work, part of the work, or provides substantial assistance with work submitted for assessment”
- Typically considered major misconduct
Materials and Resources
Canvas
Repository of all administrative info about the module
- Schedule and syllabus
- Assessment info and resources
- Assessment submission points
- Quiz and exam testing
- Policies, rules, and guidelines
Also hosts all session recordings under Panopto Recordings
Important
If you have a question about the module or assessments, check Canvas first!
Website
- Repository of (almost) all module content
- ✨Lecture slides and tutorials
- ✨Skills Lab example solutions
- Contains everything you might read/refer to
- Exception: Lecture/Skills Lab recordings on Canvas as usual
Important
Posit Cloud
- Weekly projects containing:
- ✨Tutorial notebooks
- ✨Skills Lab notebooks
- ✨Practical worksheets
- Contains everything you might do/complete
Important
Complete each week project on the Cloud!
Fun Stuff
Hex Stickers
- Hexagonal (“hex”) stickers are collected by R enthusiasts and used to display their pRide.
- Choose one for each Methods module you pass
- If you don’t have one yet, get one before you leave!
Kahoot! Points
Two main ways to collect:
- Play Kahoot! at the end of each Skills Lab
- You must register your Kahoot! username to collect points
- Complete ChallengR tasks in tutorials
Every 25,000 points, choose a new hex sticker of your choice (including shinies), with ExtRa pRizes at the end of term for top scorers!
Hex Awards
Special hex stickers that have been designed by the Methods teaching team
Some weekly and some at the end of the module:
Weekly - Keen Bean, SavioR
Termly - ClimbR & High FlyR
Weekly
Keen Bean
Awarded for enthusiasm, dedication, and curiosity.
Everyone earned this one last term! Get one at the end of the lecture.
SavioR
Students who go out of their way to help other students learn.
Recipients will be chosen by the teaching team and/or from nominations from fellow students.
Use the SavioR Award Nomination form in Canvas Quizzes to nominate someone.
End of Term
High FlyR
Goes to the students who get the highest final overall marks on Analysing Data.
Awarded once overall marks are confirmed at the end of the module.
ClimbR
Goes to students who have the biggest improvement between their final overall mark on Psychology as a Science and their final overall mark on Analysing Data.
Awarded once overall marks are confirmed at the end of the module.
Why Fun Stuff?
- It’s fun!
- Research shows that it helps you learn
- Better attainment when combined with traditional methods (e.g., Legaki et al., 2020)
- More info on the Games and Awards page
Data skills
Data Skills - in Psychology
Core findings in Psychology come from quantitative research
We’re able to challenge and update existing research because our tools are improving
The type of data we collect is becoming increasingly more complex - “back of the envelope” calculations no longer enough.
Replicability crucial for improving research
Data Skills - in Psychology
Clinical
Is an intervention for a mental health condition effective?
Developmental
Given a child’s score on a test, is their development unusual?
Cognitive
How can we program an experiment to study a complicated cognitive process?
Social
What is the engagement of social media users with posts about specific topics?
Meta-Science
Are psychology findings reported in published papers reproducible?
Organisational
What are the predictors of employee retention in a company?
Neuroscience
Which brain area is more active under certain conditions?
Educational
Does a new teaching method improve educational outcomes?
Can’t AI just do it for me?
No.
Use of AI allows:
- Cognitive offloading (Grinschgl, Papenmeier, and Meyerhoff 2021)
Use of AI can prevent:
Critical thinking
Developing problem solving skills
Learning
Bai, Liu, and Su (2023)
Important
It is your responsibility to make the best of your degree. You can leave with the ability to ask a chatbot a question, or you can leave with a range of skills applicable in the real world.
Data Skills - in the world
Being able to make sense of data is a crucial skill outside of academia.
Potential career trajectories for Psychology graduates with data-skills:
Data analyst
Data scientist
Insights analyst
Quantitative specialist
Civil Service
Market research
UX research
Industry research
Academic research
Working with start-up companies
Administrative positions
Professional services positions
(and many more)
Getting Help
If you’re finding the module challenging, it doesn’t mean that you’re failing - it means you’re learning.
For the quickest help, ask questions about anything on Discord
Book onto the R Help Desk to set up a one-on-one help session with one of the teaching team.
Come to your weekly practical session to get help from a lecturer or doctoral tutor.
Getting Help
