Introduction

Recap of level of measurements 

1. Nominal data visualization 

As nominal data (also commonly known as categorical data) do not provide any quantitative value, they can neither be ordered nor measured. Therefore, when we look at the descriptives of the nominal data, we usually look at the count of the total number and the categories of the variables.  

Given this information, we can select an appropriate type of graph to visualize the result. Bar charts and pie charts are some common graphical representations for nominal data. Here, we will look at how to generate a bar chart for a nominal variable.

For example, suppose we want to present the following data in graphs: which faculty students belong to (the variable “Faculty”), which hostel they live in (the variable “Hostel”) and what relationship status they're in (the variable “RelStatus”).

Q: How do we visualize the results of students’ faculty, hotel and relationship status? 

A: We use the “Descriptives” under the “Exploration” in jamovi.

2. Non-nominal data visualization 

While for non-nominal data (ordinal, ratio, interval data), they provide orderable and measurable quantitative values. So, in addition to frequencies (described above), we can also look at the following information in descriptives, apart from N and Missing:

• • • Mean 

    • Standard deviation 

    • Median

    • Range

With this information, we can also select some appropriate graphs to visualize the result. Histogram, density plots, box and whisker plots, and line graphs are some common graphical representations for non-nominal data. Here, we will look at how to generate a histogram and a box and whisker plot for a numerical variable.

For example, we want to present the data of “Sleep” and “GPA” of students in graphs, instead of numeric values. We will need to visualize them. 

Q: How do we visualize the results of students’ sleep and GPA? 

A: We use the “Descriptives” under the “Exploration” in jamovi.

[Typo: In the labels for Box Plot during 0:41 - 0:45 in the following video, the labels for "25th percentile" and "75th percentile" should be swapped, i.e., the correct order of the labels (from top to bottom) should be: Max. > 75th percentile > Median (50th perceptile) > 25th percentile > Min.

3. Outliers

Outliers are data points that are very different from the rest of the data. There are many possible reasons for outliers to exist, e.g., there was some error during data collection, data entry, or data pre-processing,  or it could be a "real outlier" among all observations (e.g., the exam score of a student who had been absent for the entire course including the exam). Regardless of the reason of its existence, an outlier could have huge influence and implications on our interpretation and analysis of the data (e.g., mean score of the whole class could be dragged down by the absent student). Therefore, before conducting any data analysis, it is important to identify outliers. 

One of the advantages for visualizing data is to identify outliers (if any) in a sample. 

For example, we can visualize the perceived intelligence of students with a box plot and observe whether there is/are outlier(s). For example, there could be a few students who viewed themselves as super intelligent (e.g., giving themselves a score of 100), or super unintelligent (e.g., giving themselves a score of 0).