Understanding Qualitative, Quantitative, Attribute, Discrete And Continuous Data Types

Quantitative data deals with numbers and things you can measure objectively:

i. Dimensions such as height, width, and length.

ii. Temperature and humidity.

iii. Prices.

iv. Area and volume.

Qualitative data deals with characteristics and descriptors that can't be easily measured, but can be observed subjectively such as smells, tastes, textures, attractiveness, and color. 

i. Quantitative Flavors:

There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete. 

As a general rule, "counts are discrete" and "measurements are continuous."

Discrete data is a count that can't be made more precise. Typically it involves integers. For instance, the number of children (or adults, or pets) in your family is discrete data, because you are counting whole, indivisible entities: you can't have 2.5 kids, or 1.3 pets. If I tally the number of individual Jujubes in a box, that number is a piece of discrete data

Continuous data, on the other hand, could be divided and reduced to finer and finer levels. Example: You can measure the height of your kids at progressively more precise scales meters, centimeters, millimeters, and beyond so height is continuous data. If I use a scale to measure the weight of each Jujube, or the weight of the entire box, that's continuous data. 

Continuous data can be used in many different kinds of hypothesis tests. For example, to assess the accuracy of the weight printed on the Jujubes box, we could measure 30 boxes and perform a 1-sample t-test. 

ii. Qualitative Flavors:

When you classify or categorize something, you create Qualitative or attribute data.

There are three main kinds of qualitative data.

Binary data place things in one of two mutually exclusive categories:

Right/wrong, true/false, or accept/reject. 

Occasionally, I'll get a box of Jujubes that contains a couple of individual pieces that are either too hard or too dry. If I went through the box and classified each piece as "Good" or "Bad," that would be binary data. I could use this kind of data to develop a statistical model to predict how frequently I can expect to get a bad Jujube.

When collecting unordered or nominal data, we assign individual items to named categories that do not have an implicit or natural value or rank. If I went through a box of Jujubes and recorded the color of each in my worksheet, that would be nominal data

This kind of data can be used in many different ways for instance, I could use chi-square analysis to see if there are statistically significant differences in the amounts of each color in a box. 

We also can have ordered or ordinal data, in which items are assigned to categories that do have some kind of implicit or natural order, such as "Short, Medium, or Tall."  Another example is a survey question that asks us to rate an item on a 1 to 10 scale, with 10 being the best. This implies that 10 is better than 9, which is better than 8, and so on.