5. JASP & Visualization

Johnny van Doorn

University of Amsterdam

9 September 2026

In this lecture we aim to:

  • Introduce JASP
    • Descriptive statistics
    • Data visualizations

Reading: Chapters 4 (§4.1–4.10), 5 (§5.1–5.6)

JASP

Why JASP?

  • User-friendly, intuitive interface
  • Open-source (free)
  • Reproducible analyses (easy to share)
  • Better than IBM’s SPSS for transparency and workflow
  • Integrates with R for advanced users

Origin

Artwork by Viktor Beekman, via Bayesian Spectacles

Getting started with JASP

  1. Download from jasp-stats.org
  2. Open JASP, load your dataset (.jasp, .csv, .sav, etc.)
  3. Familiarize with the interface: Data view, Input panel, Output panel

Two modes: data editor (view and edit) and analysis mode.

The start-up window

Figure 4.2: The JASP start-up window

Two buttons

The hamburger button :

The module button :

  • Add modules to the top ribbon bar
  • Update modules

The data library

Figure 4.3: Opening the Sleep data from the JASP data library

Loading Data in JASP

  • Click Open > select your file
  • Data appears in spreadsheet view

Figure 4.4: The JASP data editor

Two file formats

.csv

A lightweight file format that contains only the raw data.

Opens in virtually anything: Excel, R, SPSS

.jasp

Contains the original data, the results, and the settings that created them.

Ideal for sharing your research.

Sometimes you’ll see a .sav file, previously used in SPSS.

Variable settings

Doubleclick a column name to open its settings. The measurement level you set here decides how JASP treats your variable.

Figure 4.6: Variable settings menu

Ordinal variables

Figure 4.7: Specifying the values for an ordinal variable

Computing a new variable

Drag and drop, no syntax required.

Figure 4.8: The drag-and-drop interface for computing a new variable

Missing values

Figure 4.9: Defining missing values

Descriptive Statistics in JASP

  • Go to Descriptives > select variables
  • Options: mean, median, SD, min, max, quartiles
  • Output updates instantly

Figure 4.10

The output window

  • Updates in real time
  • Tables already in APA format, so publication ready
  • ▼ dropdown: copy to Word/PowerPoint, save a plot, open the plot editor
  • Annotate your own output

Annotated output

Figure 4.11: Example of annotated output

Saving and exporting

  • Save (Ctrl/Cmd + S) writes a .jasp file: data, output and settings
  • Export Data writes a plain .csv
  • Export Results writes .html or .pdf, which anyone can open without JASP

See discoverjasp.com for examples.

Filtering your data

Two routes to using only part of your data.

Using the filter

Using variable settings

  • Filtered rows struck through, analyses update immediately
  • Nothing is deleted

Getting help

Visualization

Why Visualize Data?

  • Quick overview of the data
  • Spot patterns, outliers, or errors
  • Invite the reader

Why Visualize Data?

Figure 5.15 Catterplot

Scutari, 1854

Florence Nightingale

  • Military hospital, Crimean War
  • Recorded every death and its cause
  • Two years of counting:
Cause Deaths
Preventable disease 14476
Wounds 1758
Other 1748
  • Infection killed far more than the enemy did
  • Sanitary Commission arrived March 1855
  • Later the first woman elected to the Statistical Society

The diagram

Each wedge is a month; its area is the death rate.

Different types of graphs in JASP

  • Distribution plots (basic plots)
    • Frequency plot when nominal
    • Histogram when scale/ordinal
  • Correlation plots (basic plots)
  • Scatter plots (customizable plots)
  • Raincloud plots

Boxplots: Visualizing Spread and Outliers

  • Shows median, quartiles, and outliers
  • Useful for comparing groups

Figure 5.9 Boxplots

Raincloud plots: Boxplots + Raw Data

Figure 5.11 Raincloud plots

Critically Assess Graphs

  • Are axes labelled (variable, units)?
  • Are axes broken/equally spaced?
  • Are error bars (e.g., confidence intervals) included where relevant?

Best practices

  • Use clear labels and titles
  • Avoid unnecessary 3D effects
  • Choose appropriate scales
  • Check for misleading representations

Labels and axes

Scale Matters

Breaking the axis

Error Bars!

What is the plot for?

Tufte’s principles, as Field lists them:

  • Show the data, and avoid distorting it
  • Many numbers, minimum ink
  • Encourage the reader to compare
  • Reveal the underlying message

In practice:

  • Cut the chartjunk: 3-D effects, gradients, decorative fills
  • Colour marks the one thing you want noticed, not decoration
  • Decide the message first, then build the plot around it

Using plots to predict

28 January 1986

  • Night before the Challenger launch
  • Would the cold damage the O-ring seals?
  • Evidence: damage on previous flights, by launch temperature

Tomorrow’s forecast: -0.5°C. Should they launch?

The evidence they reviewed

The flights that were excluded

What the plot could not say

  • 7 damaged flights only: \(r = -0.58\), \(p = .17\)
  • All 23 flights: \(r = -0.64\), \(p = .001\)
  • Launch day: 12°C colder than any data point, so can you realistically extrapolate??

Closing

Next Week

  • Variance and z-scores
  • The t-distribution
  • Correlation
    • How it works
    • Controlling for a third variable

Contact

CC BY-NC-SA 4.0

Scientific & Statistical Reasoning