I'm currently helping to evaluating a large market research survey, which ueses Likert Scales. To visualize the data I've tried several plots. The plots below where created with artificially created data to expose the strengths and weaknesses of different plot types.

Data distribution:

You can find the code for all plots here!

Bar Plot: Mean Values

Pros:

• Easy to create
• Simple to read
• Q3 – Q5 distinguishable

Cons:

• Hides a lot complexity
• Doesn't show spread
• Creates high confidence in shown values

Bar Plot: Mean values and Standard Deviation

Extension of first plot.

Pros:

• Still simple
• Introduces skepticism into shown data
• Hints at spread in data

Cons:

• Hides a lot complexity
• Still doesn't show distribution

Violin Plot

Pros:

• High Resolution
• Shows distribution of data

Cons:

• Harder to read
• Noisy on small sample sizes
• Shows data as being continuous

Vertical Histograms

Pros:

• High Resolution
• Shows distribution of data

Cons:

• Harder to read
• Shows data as being continuous

Scatter Plot

Pros:

• High Resolution
• Actually shows complete data
• Shows distribution of data

Cons:

• Hard to read
• Introduced noise and overdraw can distort data

Scaled Dots

Pros:

• High Resolution
• Shows distribution of data
• Shows data as discrete values

Cons:

• Visual distortion of proportions
• Humans can't easily compare circle sizes

Scaled Dots

Pros:

• Looks scientific
• Contains Median, Quantiles
• Shows outliers

Cons:

• Not designed for discrete data
• Doesn't show distribution correctly (e.g. Q4 + Q5)

#statistics #data #datascience #likert