Types of Variables and Their Use in Qualitative Research:

 There are many different ways to describe a variable. Variables may be related but cannot be measured directly, like salt and sugar. However, they can affect the same variable more than they do separately, such as the level of salt in a salad. Variables may also be proxy variables for factors that cannot be measured. The types of proxy variables are based on the researcher's preferences or those of the participants in the study.

Nominal scales:

Nominal scales are a way to categorize items and people based on categories. They are not numerical and are therefore used in situations where a number is not relevant. A good example of this is when you use a gender scale, for example. Although a male is considered more intelligent than a female, neither does the average male. Nominal scales are useful for this kind of research, but there are a few things to remember.

Ordinal scales:

An ordinal scale is a ranking system that is not based on numerical values. It is used to measure the degree of agreement or disagreement with a statement. The scales can be applied to any statement, from a product or service to an opinion. For example, a company can use an agreement ordinal scale to measure customer satisfaction by asking customers whether they agree or disagree with a certain statement about the company. Or, an employer can use an ordinal scale to assess employee satisfaction levels in a company. The data can then be used to determine what needs to be fixed or improved.

The problem in ordinal scales:

The problem with ordinal scales in qualitative research is that they don't allow for the comparison of categories. The data placed on these scales can be confusing and are best suited for gathering additional information. One common example is the distinction between "super light" hair and "no hair." While this may not seem like an important distinction, the ordinal scale makes it easier for researchers to analyze data and make inferences from the results.

Continuous variables:

The difference between continuous and discrete variables lies in their nature. Continuous variables can take on a wide range of values without having a fixed range. For example, a person's age can be a continuous variable when measured in years rather than in centigrade. On the other hand, a person's height or weight can have only one value, such as five to ten centimetres, and a temperature measured in Fahrenheit cannot have two values.

Ratio variables:

Unlike interval data, which is continuous, ratio variables are discrete. This means that they have no zero point, but the difference between the two values is a meaningful ratio. Examples of ratio variables are the number of children in a household and the years of experience an employee has. A ratio is a measure of the extent to which a variable is twice as large as its opposite. These two types of variables in research are similar but not the same.

Author Bio:

Carmen Troy is a research-based content writer, who works for Cognizantt, a globally recognized professional SEO service and Research Prospect; an 论文和论文写作服务 Mr Carmen holds a PhD degree in mass communication. He loves to express his views on various issues, including education, technology, and more.