About me: Dr Jens Roeser

  • associate professor in psycholinguistics @ psychology department (Nottingham Trent University)
  • theory: language processing (e.g. Roeser et al. 2019, 2025; Garcia et al. 2023)
  • methods: controlled experiments; keystroke logging; eye tracking (Roeser et al. 2024, 2025)
  • \(>\) 10 years experience teaching data science to UG, PG, PGR students, academics and professionals (psyntur, Andrews and Roeser 2021)
  • also teaching: cognitive psychology, bilingualism, and language acquisition (Roeser and Wood 2019)
  • profile: ntu.ac.uk/staff-profiles/social-sciences/dr-jens-roeser

Today’s session

  • Talk about what data analysis is and where you have seen it before
  • See how Analysing Data fits into your degree
  • Find the learning room and choose your study mode
  • Understand how assessment works
  • Share expectations and worries

What’s your experience with data analysis?

  1. Write down your own answers first.
  2. Compare your answers with the people on your table.
  3. Choose one point your table would be willing to share.

Questions:

  • What do you think data analysis is?
  • Where have you come across data analysis before?
  • How are you feeling about learning data analysis?

Data is already part of psychology

At your table, choose one example and discuss:

  • social media feeds
  • mental health questionnaires
  • sleep or fitness tracking
  • exam results
  • therapy outcome measures
  • crime or health statistics

For your example:

  • What is the data?
  • What question could we ask?
  • What could go wrong if we interpreted it badly?

How does Analysing Data fit into your course?

Analysing Data 1 - HyFlex

  • HyFlex: flexibility around the mode of how you complete your module.
  • Core knowledge building (online via learning room)
    • Pre-recorded lectures with theory focus
    • Self-paced formative assessments and knowledge checks
  • Core practical experience (online and / or in person)
    • Focus on data analysis using RStudio
    • Practical elements can be done in-person or online
  • But we strongly suggest you attend the in-person workshops: students who have previously attended in-person benefit much more.
  • Complete the Core knowledge building unit before the Core practical experience.
  • You will need to indicate each week whether you will engage with the Core Practical Experience in-person or online.

Analysing Data 1 Learning Room

Open NOW and find the Analysing Data 1 learning room:

PSYC10305: Analysing Data: Methods & Tools 1 202627 Full Year

Your task:

  • Find Week 1 – Choose your mode of study
  • Find where the Core Knowledge Building materials are
  • Find where the Core Practical Experience materials are
  • Check what you need to do before next week’s workshop

Choose your mode of study

  • Each week, select the appropriate week’s choose mode of study option.
  • Indicate how you will engage with the content.
  • Once you do this, the content for that week will open for you to view.

Check with the person next to you: what is one thing you need to do every week?

How does attendance work with HyFlex?

  • If you are doing the Core Practical Experience in-person, you will need to scan the QR register code in the workshop session.
  • If you select the Core Practical Experience online, we will track your engagement with the online content and continuous assessments.
  • If you initially select in-person but then cannot make it, the online content will still be available.
  • You can also change your selection on NOW if needed.

HyFlex induction

  • More information about HyFlex and how to engage with Analysing Data 1 will be given on Friday 18th September 12 – 1pm, online (via Teams).
  • You should have received an invite to this.
  • Please make sure to attend this induction session.

Assessment for Analysing Data 1

  • Analysing Data 1 is assessed with a continuous assessment and an exam.
  • Exam: 80% of overall grade
  • Continuous assessment:
    • 20% of overall grade
    • End of weeks 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15
    • Around 8 questions each
    • Three attempts for weeks 3 and 4; 1 attempt for week 5 and thereafter.
  • Questions assess all module content, including both Core Knowledge Building and Core Practical Experience.
  • Assessment specifications with all details are on the NOW Learning Room.

Continuous assessment: don’t leave it late

  • There are deadlines: make sure you know when each assessment needs to be completed.
  • The continuous assessment is designed to be completed each week.
  • Do not save several quizzes until just before the deadline.
  • Each quiz connects to that week’s Core Knowledge Building and Core Practical Experience.
  • You will benefit much more if you use the quiz as weekly practice while the material is fresh.

Assessment check

In pairs, answer these without looking back first:

  • What is worth 80%?
  • What is worth 20%?
  • When do the weekly quizzes start?
  • Why might weeks 3 and 4 having three attempts matter?
  • What weekly habit would make the exam easier?

What support is available?

Why should we care about learning data analysis?

Think alone first, then discuss at your table:

  • Where might data analysis matter in psychology?
  • Where might it matter outside psychology?
  • What could a psychologist get wrong without data analysis skills?

Why should psychology students care?

  • Psychology is evidence-based: claims about people, behaviour, mental health, education, work, and society depend on data.
  • Data analysis helps you make better decisions when the answer is uncertain, messy, or contested.
  • Data skills help you spot weak evidence, misleading graphs, bad averages, overclaiming, and unsupported conclusions.
  • Psychology graduates often work with people-data: customers, patients, users, students, employees, communities, and organisations.
  • These skills support your research methods modules, final-year project, and postgraduate study.

Why should future graduates care?

  • Most of you will work outside psychology, but many graduate jobs involve data, reports, dashboards, surveys, evaluation, or performance measures.
  • Employers value graduates who can turn information into a clear argument, not just repeat opinions.
  • Analysing data builds technical confidence: coding, problem solving, precision, documentation, and independent troubleshooting.
  • AI and automated systems are increasingly data-driven; you need enough data literacy to question their outputs responsibly.
  • Data skills are useful in high-skilled roles in research, HR, UX, policy, marketing, health, education, consulting, and analytics.

Over to you!

Write down one answer for each question:

  • What are your expectations?
  • What worries have you got about learning data analysis at uni?

Then compare at your table and choose one theme to share.

Reading recommendations

  • Andrews (2021) Doing Data Science in R: An Introduction for Social Scientists. LINK
  • Wickham and Grolemund (2016) R for data science. LINK
  • Field et al. (2017) Discovering statistics using R LINK
  • Faraway (2015) Linear models with R LINK
  • Dienes (2008) Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference LINK

Before next week

  • Choose your mode of study on NOW
  • Complete the Core Knowledge Building element
  • Bring questions to the workshop

Jens’s two cents

  • Engage with the HyFlex contents on a weekly basis!
  • Complete the Core Knowledge element before the workshop.
  • Don’t skip weeks and definitely don’t squeeze everything into the last weeks before the exam!
  • If you don’t understand something, please ask; don’t wait!

References

Andrews, Mark. 2021. Doing data science in R: An Introduction for Social Scientists. SAGE Publications Ltd.

Andrews, Mark, and Jens Roeser. 2021. Psyntur: Helper Tools for Teaching Statistical Data Analysis. https://CRAN.R-project.org/package=psyntur.

Dienes, Zoltan. 2008. Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. Palgrave Macmillan.

Faraway, Julian J. 2015. Linear Models with R. Vol. 2. CRC press.

Field, Andy, Jeremy Miles, and Zoe Field. 2017. Discovering Statistics Using R. W. Ross MacDonald School Resource Services Library.

Garcia, Rowena, Jens Roeser, and Evan Kidd. 2023. “Finding Your Voice: Voice-Specific Effects in Tagalog Reveal the Limits of Word Order Priming.” Cognition 236: 105424.

Roeser, Jens, Rianne Conijn, E. Chukharev, G. H. Ofstad, and Mark Torrance. 2025. “Typing in Tandem: Language Planning in Multisentence Text Production Is Fundamentally Parallel.” Journal of Experimental Psychology: General 154 (7): 1824–54. https://doi.org/10.1037/xge0001759.

Roeser, Jens, Sven De Maeyer, Mariëlle Leijten, and Luuk VaWaes. 2024. “Modelling Typing Disfluencies as Finite Mixture Process.” Reading and Writing 37 (2): 359–84. https://doi.org/10.1007/s11145-023-10489-4.

Roeser, Jens, Mark Torrance, and Thom Baguley. 2019. “Advance Planning in Written and Spoken Sentence Production.” Journal of Experimental Psychology: Learning, Memory, and Cognition 45 (11): 1983–2009. https://doi.org/10.1037/xlm0000685.

Roeser, Jens, and Clare Wood. 2019. “Language and Literacy.” In Essential Psychology, edited by P. Banyard, C. Norman, G. Dillon, and B. Winder, vol. 3. Sage.

Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.