A variable is a part of a study that can take on various values. A person’s age may be a variable, as it has different values for each individual. Another example is a country, which may have a number assigned to it. Research may use these different values to examine how other nations influence the results of their study. A researcher may use a variety of variables in their research, varying from qualitative to quantitative.
Numeric variables:
Researchers can use either continuous or discrete numeric variables in their research. Continuous variables are not categorical but can have any value between a certain set of real numbers. Examples of continuous variables include weight, age, and height. These variables are often compared because of the flexibility that continuous variables offer.
Discrete numeric variables:
Discrete numeric variables take a value based on a count of fundamental values. They cannot take a fractional value, such as a percentage. These variables are useful in many studies and are often easier to collect than discrete variables. For example, researchers can use a thermometer to capture numerical data when examining body temperature. Although discrete numeric variables aren’t necessarily more accurate than continuous ones, they are still useful for confirming and testing hypotheses.
Qualitative variables:
Often qualitative variables are not given numerical values, but they are still classified as qualitative variables. Ordinal and nominal qualitative variables allude to attributes with no numerical value but are organised within a range. These variables are not usually given numerical values because the practicalities of measurement do not allow for this. However, ordinal and nominal variables can be considered quantitative since they are useful in a research study. But how do we use them?
Extraneous variables:
If you’ve ever done a research study, you probably know that extraneous variables may cause errors in your results. The extraneous variables that affect your research results include factors outside your control, like your personal knowledge and prior experience with statistics. In addition to the variables, you can control, extraneous variables can also affect participants’ behaviour, including how they respond to different conditions.
When designing a study, you must identify all the extraneous variables that may affect the results. For example, you should examine the participants’ demographics, moods and level of understanding. Also, consider the participant variables that could affect your results, such as age, gender identity, marital status, or religious affiliation. By controlling these variables, you can prevent your research from being affected by extraneous variables.
Composite variables:
In research, a composite variable is a combination of several different independent variables influenced by each other. Composites must have meaning in the context of the study. As such, the choice of the variables should be based on the science of the field and not on the results of individual studies. For example, a composite variable may contain two levels of treatment, but the investigators did not apply any other treatment groups. This way, the combined ‘Treatment’ captures the whole universe of treatment possibilities.
Dependent variables:
The concept of dependent variables in a research paper means that the results of one variable depend on another. For instance, rainfall is a dependent variable, as are the amounts of water present in a soil sample. If you are studying how water levels influence plant growth, you will use the dependent variable rainfall to measure the impact of rainfall on plant growth. Similarly, the type of therapy used by patients is a dependent variable.
Confounding variables:
One of the most common problems that plague research is the presence of extraneous variables. These variables may include the independent and dependent variables and environmental factors. These extraneous variables often complicate results. This article will discuss extraneous variables and how to deal with them. Here are some examples of extraneous variables. The effects of these variables can compromise the validity of your research.
