Medical Statistics Made Easy - Data Organization And Visualization - DCOVA Framework Part 6.1
This is the sixth video in the medical statistics made easy series and it focuses on the data organization and visualization part of the DCOVA framework. The first part of this video describes the data organization and visualization for categorical variables.
When it comes to organizing and visualizing nominal and ordinal variables, there are several methods you can use. Here are a few examples:
Frequency Tables: A frequency table is a simple way to organize and summarize nominal and ordinal data. For nominal data, you can create a frequency table that shows the number of occurrences for each category. For ordinal data, you can create a frequency table that shows the number of occurrences for each possible value or range of values.
Bar Charts: Bar charts are a popular way to visualize nominal and ordinal data. For nominal data, you can create a bar chart that shows the frequency or percentage of each category. For ordinal data, you can create a bar chart that shows the frequency or percentage of each value or range of values.
Pie Charts: Pie charts are another way to visualize nominal data. You can create a pie chart that shows the percentage of each category.
Stacked Bar Charts: Stacked bar charts are useful for comparing the distribution of two or more nominal or ordinal variables. You can create a stacked bar chart that shows the frequency or percentage of each category for each variable.
Heatmaps: Heatmaps are useful for visualizing the relationship between two nominal or ordinal variables. You can create a heatmap that shows the frequency or percentage of each combination of categories.
Bubble Charts: Bubble charts are a way to visualize the relationship between two ordinal variables. You can create a bubble chart that shows the frequency or percentage of each combination of values, with the size of each bubble representing the frequency or percentage.
These are just a few examples of ways to organize and visualize nominal and ordinal data. The choice of method will depend on the specific research question and the nature of the data.