Module 04

Descriptive Statistics

Introduction

    • After pre-processing the data (covered in Module 01, Module 02, and Module 03), we have obtained more meaningful measurements (e.g., the openness subscale of personality) for each individual participant. However, with tens or even hundreds of participants, we need a concise way to describe various aspects (e.g., central tendency, variability) of a measurement across participants. In this module, we will focus on how to compute such descriptive statistics (or commonly known as "descriptives") using jamovi.

1. Descriptives on nominal data

As nominal data (also commonly known as categorical data) do not provide any quantitative value, they can neither be ordered or measured. Therefore, when we look at the descriptives of the nominal data, we usually look at:

      • N (the total number of samples); and

      • Missing (the number of missing data).

Apart from descriptives, we often want to look at the frequencies of nominal data. Frequencies provide information such as:

      • Levels (groups) of variable;

      • Count, which is the number of samples in each level; and

      • Percentage of total

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.


Example 3.1 Descriptive_visualize_nomial.mp4

2 Descriptives on non-nominal data

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.

Example 3.2 Descriptive_visualize_scale.mp4

3. Reporting descriptives in APA format

We often need to report descriptive statistics in research reports. The American Psychological Association (APA) has published some guidelines on how such statistics should be reported in standardized formats.

In fact, the Descriptives tables generated in jamovi are very close to the APA format.

Interpretation / Conclusion:

    • University students are getting a fair amount of sleep (M = 6.88, SD = 1.48).

    • In general, the GPAs of the students at XXX University are decently good (M = 3.03, SD = 0.515) with over half of them can get a second-up honor.

Module Exercise

Complete the exercise!

    • Now, if you think you're ready for the exercise, you can check your email for the link.

    • Remember to submit your answers before the deadline in order to earn the credits!