Before you can start researching, you must identify what you are trying to measure. You can do this with ordinal or interval scales. You can also measure extraneous variables. However, this step is more complicated than it may seem.
Categorical variables:
CATEGORICAL variables have at least two categories, positive and negative, or both. In contrast, ordinal and nominal variables have no inherent ordering. For example, a variable such as economic status can be classified into three categories: low, medium, and high. Another example of a categorical variable is the type of education one has.
A person may be classified as having an elementary school education, a high school education, or some college education. In both cases, the spacing between the categories is not uniform.
Categorical variables are helpful in research because they make the data easier to analyze. For example, researchers studying the relationship between income and health can use categorical variables to analyze their findings. This allows them to control for other variables and focus on the relationship between the two variables.
Categorical variables are sometimes also called discrete or qualitative variables. Among other types, they include ordinal, nominal, and binary variables.
Numerical variables:
In research, a numerical variable is a variable with a numerical value. These variables can be continuous or discrete. They are used in various statistical methods and are often referred to as continuous variables. Continuous variables have a range of values from zero to one hundred and include variables such as the length of a leaf in centimetres.
An example of a numerical variable is a student’s Grade Point Average (CGPA). The CGPA of a student is expressed as a percentage, and the results are summarised by taking the mean of all students’ grades over some time. In contrast, annual income is a countable, discrete variable and is often used in business settings to understand the purchasing power of customers.
To correctly analyze numerical data, researchers need to identify the variable type. Numerical variables are further divided into discrete, continuous, and ratio. The latter type is used in statistical analyses to find trends in a population.
Composite variables:
When a study is conducted to determine the effectiveness of a treatment, researchers may use several different types of variables to measure their effects. These variables may be correlated, confounding, or independent. Confounding variables can mask the effects of another variable, invalidating the experiment’s results.
They can also suggest a relationship between variables when one does not exist. The researchers may also use a composite variable consisting of two or more variables. An example of this would be the overall health of an individual. It can be measured by weighing a person’s weight or blood pressure.
Another type of variable is a nominal variable. This variable consists of two categories, but it has no inherent order. These variables may also have a pattern, such as a daily, weekly, or monthly schedule. On the other hand, an ordinal variable has more than two categories, such as the number of satisfaction levels.
Extraneous variables:
In research, extraneous variables influence the dependent variable but are not directly measured. These variables can introduce noise and variability to the data and mask the actual effect of the independent variable. These variables can also affect the outcome of a study. For example, a study might find that a particular drug or medication causes nausea and vomiting but does not affect the overall incidence of headaches.
Extraneous variables can also negatively impact the reliability of a study. Because they can’t be controlled, extraneous variables make it difficult for researchers to determine whether a particular treatment causes one specific effect. However, researchers can compensate for the effects of extraneous variables by using random assignment.
Randomization is one of the most common techniques for controlling extraneous variables in research. This method distributes irrelevant effects across experimental and control conditions, thereby avoiding the bias caused by the extraneous variables. Randomization also allows the researcher to control for differences between groups.
Author Bio:
Carmen Troy is a research-based content writer for Peno phile, a global proofreading and editing service provider. And Research Prospect, The best academic writing services company worldwide. They provide many Academic writing services, dissertation writing services, etc. Mr Carmen holds a PhD degree in mass communication. He loves to express his views on various issues, including education, technology, and more.
