Analysing Data: Lecture 01

Welcome to Analysing Data!

Lecture 01



Jennifer Mankin and Martina Sladekova

1 February 2024


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

Jennifer Mankin

Confidential queries: J.Mankin@sussex.ac.uk

  • Convenor and primary point of contact
  • Lectures, skills labs, practical teaching
  • All module admin, assessments, queries

Martina Sladekova

  • Lectures, skills labs, practical teaching

Jenny Terry

  • Lectures only

Joanna McLaren

  • Practical lead

Module Structure

Types of Sessions

Lecture

  • One-hour lecture session Thursday mornings
  • Concepts, ideas, and understanding statistical tests and principles

✨Skills Lab

  • One-hour interactive, live-coding session Thursday evenings
  • Demonstrates 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, switches between online and in person

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

  • Materials released on Wednesdays
    • Lecture and tutorial in the morning
    • Tutorial and worksheet on Cloud in the evening
  • Lecture Thursday morning
  • Skills Lab Thursday evening

Second Week

  • Practical session
    • Complete tutorial
    • Complete worksheet
  • Worksheet quiz

Topics Structure

Calendar showing days when sessions and materials are released

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
SPRING HOLIDAY
9 Linear model 2 Linear model 2
10 Linear model 3 Linear model 3
11 QPRs Kahoot revision

Attendance

Lectures/Skills Labs

  • Attendance is required, but not marked (no PIN or register)
  • Hybrid delivery: in-person, simultaneously on Zoom, recorded

Important

If you are on a visa in the UK, you must attend teaching sessions in person!

Why Hybrid?

  • Accessibility and engagement are priorities!
  • Hybrid allows live captions, chat, Zoom surveys etc.
  • Support needs and preferences are very different for different people
    • We want you to have options to learn most effectively

However…

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)

Graphic summarising Using Lecture Recordings: a guide for students. The six key points are: attend lectures; take notes; be specific; catch up; ask for help; don't cut corners. Dr Emily Nordmann’s Lecture Capture project

Practicals

  • Alternate in person or online
  • Interact in some way with tutors (any way is fine)
    • Ask questions, ask for help, get your work checked before the quiz!

Important

✨We do not use PIN attendance on this module!

If you attend any practical, you can access help and the quiz as normal.

  • ✨You will be marked present if you attend a practical in your same timeslot (but different mode)
  • You will not be marked present if you attend a practical in a different timeslot (either mode)

Changing Your Timetable

Assessments

All Assessments

See Assessment 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

Worksheet Information on Canvas

  • In the first hour of 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

  • Answers will be released at the end of each week

✨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

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

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
    • ✨Tutorial and practical materials
    • ✨Skills Lab example solutions
  • Contains everything you might read/refer to
    • Exception: Lecture/Skills Lab recordings on Canvas as usual

Important

Check out the module website at https://and-sussex.netlify.app!

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!
Eight hex stickers for the packages dplyr, ggplot2, magrittr (pipe), quarto, RStudio, tibble, tidyr, and tidyverse

Kahoot! Points

Two main ways to collect:

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 - 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?

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?

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.

✨Questions?✨