About me: Dr Jens Roeser

  • senior lecturer in psycholinguistics @ psychology department (Nottingham Trent University)
  • module leader for PSYC40540 (confidential matters: jens.roeser@ntu.ac.uk)
  • research
    • Language production / comprehension / acquisition (e.g. Roeser, Torrance, and Baguley 2019; Garcia, Roeser, and Kidd 2023)
    • Bayesian modelling (Roeser et al. 2021); keystroke logging; eye tracking
  • teaching:
    • 10 years of experience teaching data science and statistics to UG, PG students, academics and professionals (psyntur, Andrews and Roeser 2021)
    • cognitive psychology and language acquisition (Roeser and Wood 2019)

Who are you?

Go to menti.com

(cause lets keep things anonymous for now)

Elephants in the room

Some of you know more about statistics and R/RStudio than others.

  • Things might feel too fast or slow.

For some students, statistics might feel stressful.

  • In our experience, students that engage with the module do not fail.

Module Aims

  • Theoretical underpinnings of statistics used in psychology.
  • General linear regression models approach to statistics: focuses on the relationship between multiple linear regression, ANOVA and ANCOVA, and other models.

Delivery mode

  • Weekly pre-recorded lectures
  • Weekly 2hr in-class workshops every Friday, 3pm - 5pm.

Schedule

Week 1

  • Module overview
  • Introduction to R & RStudio

Week 2

  • Lecture: Introduction to statistical inference
  • Workshop: Exploring, summarising and visualising data with R

Weeks 3 & 4

  • Classical statistical inference (hypothesis testing)
  • From here onward, there will be online problem sets for you to answer

Weeks 5 & 6

  • Simple and multiple normal linear regression

Week 7

  • General linear models (categorical predictor variables)

Week 8

  • Varying slope, varying intercept, general linear models (ANCOVA)

Week 9: Revision session

Week 10: Exam

Assessment

  • Single 90 mins exam (approx. 30 questions)
  • Friday, 29 November, 2024, 3:30pm - 5pm (room will be available 3pm).
  • Questions will involve R, which can be used on personal laptops.
  • All questions will be multiple choice or short-answer.
  • Not open-book, but you may use 2 two-sided A4 pages of notes.
  • Normal exam rules apply: no collaboration or communication, no use of (online) resources.

Assessment criteria

Practical and theoretical understanding of all contents, in particular

  • nature of the statistical models
  • assumptions and the contexts in which the statistical models are applied
  • ability to perform analysis using RStudio
  • interpret the output of statistical analysis

How to engage with this module?

  • Watch prerecorded lecture videos every week (before the workshop)
  • Post questions / comments on our dedicated Q&A forum for both psyc40540 and psyc40545; please sign up.
  • Attend workshops and participate in activities
  • Formative assessments (weeks 3 - 8):
    • Lecture learning-check quiz (after lecture)
    • Homework problem set (after workshop)
  • Prepare and share questions (per post on Q&A forum) what I should cover in the revision session.
  • If you require additional reading materials, please ask.
  • Additional maths / statistics support is provided by NTU’s library.

Reading recommendations

  • Andrews (2021) Doing Data Science in R: An Introduction for Social Scientists
  • Field, Miles, and Field (2017) Discovering statistics using R
  • Faraway (2015) Linear models with R

Introduction to R and RStudio

  • Installation
  • RStudio environment, R-projects (working directory)
  • R-Scripts and executing code
  • Comments, commands, sections
  • Using R as a calculator
  • Variables and assignment (<-)
  • Vectors and combining vectors (c())
  • Base-R functions
  • Indexing ([1])
  • Booleans (TRUE, FALSE) and logical operators (|, &, >, <, ==)
  • Data types (numeric, character, factor, logical)
  • Coercing vectors (vectors are homogeneous)
  • Missing values (NA)
  • Data frames
  • Installing and loading libraries (tidyverse, psyntur)
  • Nested functions
  • Help pages
  • Reading in and viewing data

References

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

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

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, Sven De Maeyer, Mariëlle Leijten, and Luuk Van Waes. 2021. “Modelling Typing Disfluencies as Finite Mixture Process.” Reading and Writing, 1–26.

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.

Roeser, Jens, and Clare Wood. 2019. “Language and Literacy.” In Essential Psychology, edited by P. Banyard, C. Norman, G. Dillon, and B. Winder, 3:197–226. London: Sage.