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Quantitative or Qualitative: How to Classify Variables

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Quantitative or Qualitative: Overview

In introductory statistics, it’s easy to get confused between qualitative variables and quantitative variables. Quantitative means it can be counted, like “number of people per square mile.” Qualitative means it is a description, like “brown dog fur.” A Deck of cards contains quantitative variables (the numbers on the card) and qualitative variables (Spades, Hearts, Diamonds, Clubs).
quantitative or qualitative
The simplest way to classify Quantitative or Qualitative variables is this: if it can be added, it’s quantitative. Qualitative variables can’t be added. For example:
Cat + dog + mouse = ?
Hair color + Eye color = ?
I’m sure you could come up with a joke or two to answer those questions, but realistically you can’t add them, so they are qualitative. Technically you could add them if you assign a number to them. For example,
Cat = 1
Dog = 2
Mouse = 3
And in fact, that’s what happens a lot in statistics; Numbers are assigned to make calculations easier. But! Assigning a number does not make them a quantitative variable; They are just qualitative variables that have been assigned a number.

How to Classify Quantitative vs Qualitative variables

Step 1:
Think of a category for the items, like “car models” or “types of potato” or “feather colors” or “numbers” or “number of widgets sold.” The name of the category is not important. Jot the category down on a piece of paper.

Step 2:
Rank or order the items in your category. Some examples of items that can be ordered are: number of computers sold in a month, students’ GPAs or bank account balances. Anything with numbers or amounts can be ranked or ordered. If you find it impossible to rank or order your items, you have a qualitative item. Examples of qualitative items are “car models,” “types of potato,” “Shakespeare quotes.”

Step 3:
Make sure you haven’t added information. For example, you could rank car models by popularity or expense, but popularity and expense are separate variables from “car model.” If the item is “potatoes,” it’s qualitative. If the item is “number of potatoes sold,” it’s quantitative.

Quantitative or Qualitative Data Analysis

qualitative data analysis

Data analysis falls into two categories:

  • Quantitative data analysis, which deals with numbers.
  • Qualitative data analysis, which deals with text or pictures.

Texts can include: written observations, participant statements, interview transcripts, emails, or field notes. Pictures can include: any kind of image, from journalistic-quality photos or videos to drawings made by children in Kindergarten.

Qualitative data analysis tends to be focused on the participants’ viewpoints and terms rather than on the researcher’s. In other words, the researcher’s role is to watch, take notes, and try to record an accurate account of what they are seeing. This is in contrast to traditional experiments, where the researcher makes a hypothesis statement and the experiment is conducted under carefully controlled conditions.

The main difference between quantitative or qualitative data analysis is that quantitative deals with numbers and qualitative deals with text. There are, however, many more subtle differences, including:

  • Quantitative data analysis tends to focus on collecting a small amount of data for a large number of cases. For example, a researcher might sample 10,000 people to obtain a couple of statistics like the mean or variance. The qualitative researcher tends to focus on a small number of cases (say, 10) and collect a large and varied amount of data (i.e. demographic information or personal history) on those subjects.
  • Categories and directions are predetermined in quantitative research. Qualitative research does not have these predefined boundaries.
  • Quantitative data analysis seeks to find a universal generalization. For example, “the average SAT reading score is 497.” The qualitative researcher seeks to understand the context behind phenomenon. For example, “Inner city students tend to perform more poorly on standardized tests because of lack of nutrition and lack of access to computers at home.”

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

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