1 Schedule overview

Week Lecture Lab
1 Quantitative methods and uncertainty R basics
2 Research process RStudio + R scripts
3 File management Read data
4 Data viz Quarto + Plotting basics
5 Statistical variables Data transformation I F1
FL No classes
6 Data summaries Data transformation II
7 Tidy data Data tidying and joining F2
8 Plot design Advanced plotting
9 Where to next? Troubleshooting
25 Mar S2 proposal
28 Mar S1
25 Apr S2

2 Functions and data

Functions Data
1 c(), sum(), mean(), cat(), data.frame()
2 length(), library(), .libPath()
3 read_csv(), read_excel(), readRDS() shallow, relatives, glot_status
4 ggplot(), aes(), geom_point(), labs(), colour and alpha, scale_colour_brewer(), scale_colour_viridis_d(), |> polite
5 filter(), mutate(), geom_bar(), as.factor(), factor(), facet_grid() glot_status, polite
6 group_by(), summarise(), count(), geom_density(), geom_rug(), mean(), sd(), median(), min(), max(), range(), log() shallow, gestures
7 pivot_*(), join_*(), list.files(), geom_violin(), geom_jitter(), facet_wrap()
8 geom_hist(), stat_ellipse(), geom_tile(), likert, geom_sf
9
F1 token-measures
F2
S1
S2

3 Weekly schedule

3.1 Week 1

Learning Objectives

Questions

  • What is quantitative data analysis?
  • Is statistics a science, an art, or both?
  • How do uncertainty and variability affect data analysis?

Skills

  • Think critically about statistics, uncertainty and variability.
  • Use R to perform simple calculations.
  • Master the basics of the programming language R.
Homework

Course website

  • Carefully read the homepage.

  • Familiarise yourself with this Course content page (note that the materials will be updated throughout the course).

Intake form

  • You must complete the intake form before coming to the Tuesday lecture.
  • The link to the form can be found on the Learn website.

OPTIONAL: Install R, RStudio and Quarto

  • The labs will take place in the PPLS computer lab which will have all the necessary software (hopefully…).
  • If instead you wish to use your own laptop, you are welcome to do so provided you take care of installing everything and fix issues that might arise. We won’t be able to help you with the installation process.
  • Please, follow the instructions in the Setup page.
Readings

ALL READINGS ARE OPTIONAL. The textbook can be used to revise what done in class and you can pick and choose any of the other readings based on your interests.

Textbook

This course will be loosely based on the following online textbook. Note that the order of the contents of the course will not closely follow that of this book, but specific chapter will be noted on this page in each week

Other

  • Ellis and Levy 2008. Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem.
  • Methods as theory.
  • Vasishth and Gelman 2021. How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis.
  • Molnar 2022. Modeling Mindsets: The many cultures of learning from data.
  • Darwin Holmes 2020. Researcher Positionality - A Consideration of Its Influence and Place in Qualitative Research - A New Researcher Guide
  • Jafar 2018. What is positionality and should it be expressed in quantitative studies?

3.2 Week 2

Learning Objectives

Questions

  • Which are the steps of the research process/cycle?
  • How can questionable research practices affect each step in the research cycle?
  • How can we prevent questionable research practices?

Skills

  • Using RStudio and Quarto Projects
  • Write and run code in R scripts.
  • Install and attach R packages.
Readings

Textbook

From the lecture

Other

3.3 Week 3

Learning Objectives

Questions

  • What does file management involve?
  • What is a research compendium?
  • What are licenses and how does one choose how to license work?

Skills

  • Organising project files and picking licenses.
  • Read data into R.
  • Submitting a Data Management Plan.
Readings

3.4 Week 4

Learning Objectives

Questions

  • What are the principles of good data visualisation?
  • Which are the pitfalls to avoid?
  • How can showing the raw data make the plot more reliable?

Skills

  • Create dynamic reports with Quarto.
  • Learn about plotting systems.
  • Basics of plotting with ggplot2.
Readings

3.5 Week 5

Learning Objectives

Questions

  • What is a statistical variable?
  • Which types of statistical variable are there?
  • What is “operationalisation”?

Skills

  • Filter rows and mutate columns.
  • Create bar charts.
  • Separate data in plots using faceting.

3.6 Week 6

Learning Objectives

Questions

  • What are summary measures?
  • Which summary measures are appropriate for which type of variable?
  • How should summary measures be reported?

Skills

  • Obtain summary measures in R.
  • Use grouped data.
  • Create density plots.
Readings

Textbook

3.7 Week 7

Learning Objectives

Questions

  • What types of data do exist?
  • What does data coding entail?
  • What is tidy data and what does it mean for data to be non-tidy?

Skills

  • Coding data.
  • Tidy up non-tidy tibbles.
  • Join separate tibbles based on key columns.
Readings

Textbook

3.8 Week 8

Learning Objectives

Questions

  • What other type of plots can be helpful?
  • What are common pitfalls of designing plots?
  • How can we avoid those pitfalls?

Skills

  • Think about pros and cons of different types of plots.
  • Create specialised plots.
  • Normalise data and compute proportions.
Readings

See week 8 slides.