The research intends to achieve goals. To pursue the goals, you need variables that make the process of goal setting possible to identify which results in the achievement of the goals.
Therefore, research means the measurement of the variables and the importance of the variable is hidden in this concept. Basically, the variables should be determined in accordance with their purpose and components. In other words, the variables should be selected through operational words and research literature.
Consider the following criteria in variable selection:
- Being consistent with the goal
- Being measurable
- Being replicable
- Being used widely in recent years. (Sometimes there may be important diagnostic methods to check a disease or to measure a certain amount of ways. Therefore, obsolete and non-valid methods should be avoided and instead of them using the common methods and consider them as variables.
- Being affordable and can be fitted with the study design
- Being prevalent and common in the community
- Being reliable (i.e., produces stable and consistent results over a period of time)
- Being valid (e.g., kg scale is not a suitable scale to measure the height of the people)
- Can be measured using available tools
- Can be mentioned in the review of literature which indicates the importance of the variable and its relevance to the study.
- Not being so rare that it cannot be measured
- Not being time-consuming
- Not being out of research scope
Types of variables
There are different types of variables, two of them are explained as follows:
According to research objectives and variable roles in the study, they are classified into four types:
1. Independent variable: A variable you can manipulate, but it’s not dependent on the changes in other variables
2. Dependent variable: A factor or phenomenon that is changed by the effect of an associated factor. The dependent variable is the variable being tested and measured in a scientific experiment.
3. Demographic variable: A variable that is neither independent nor dependent, but sometimes can be used by researchers to describe the nature and distribution of the sample.
4. Confounding or intervening variable: This variable affects the causal relationship of the two variables and makes the relationship weaker or stronger.
Sometimes the variables are classified according to their own nature, such as:
1. Quantitative variables
2. Qualitative variables
A quantitative variable is represented by a number and has a measuring instrument and is divided into two types, continuous and discontinuous.
A discontinuous variable is a value that can have two or more possible values but has a limited number of values.
A continuous variable is a variable that can take infinitely many values between any two observed values.
A qualitative variable (e.g. Race and Sex) is a variable that shows the quality of the attributes and cannot be measured by measuring instruments.