Understanding variables is integral in scientific research, either when conducting a study or seeking to interpret and understand a study. A variable in the context of research can be defined as an empirical phenomenon that takes on different values or intensities or one that varies (Flannelly et al., 2014). Variables are, therefore, properties that can be used to define certain things. For example, the properties can be their age, height, weight, and health status when describing humans. Therefore, it is correct to say variables represent characteristics of a sample that are examined, measured, described, and interpreted (Andrade, 2021). In research, properties are normally classified into dependent and independent variables, the main divisions. And then, there is another group of variables referred to as extraneous or confounding variables.

Research in healthcare or any other field is designed to understand the causes of phenomena such as the cause and effect relationship. For example, in this case, the independent variable is the variable that influences other variables. For example, a human characteristic of being overweight or obese has been identified as a risk factor for heart disease or diabetes. This means that being obese influences the development of other conditions such as diabetes. On the other hand, the dependent variable is the variable that is affected/influenced by other variables. For example, lung cancer can be the dependent variable influenced by an independent variable such as smoking in a sample of older men. Therefore, in research, the researchers are usually more interested in understanding and predicting the dependent variable. Therefore, research is a way in which the researcher tests whether the independent variable affects the dependent variable (Andrade, 2021). However, it is important to note that when seeking to classify a variable as either dependent or independent depends on the research question being asked. Indicating that a variable is dependent or independent does not make them permanent because that can change in a different context (Andrade, 2021).

Extraneous or confounding variables are another variable that researchers should be aware of while designing or analyzing research studies. Extraneous variables are of major concern to the researchers because they have the potential to either alter or obscure the relationship between independent and dependent variables. During a research study experiment, extraneous variables can indicate a causal relationship where there is none (Flannelly et al., 2014). This means that if extraneous variables are not controlled, there is a high likelihood that the study will not be accurate, and its validity and reliability will also be questionable. Therefore, the researcher tries to control these variables in the best way possible. For example, they control the conditions of the environment where the experiment is taking place to keep them as constant as possible. In studies involving humans, extraneous variables include age, sex, income, education, and ethnicity (Flannelly et al., 2014). In such a case, controlling extraneous variables can be challenging. Researchers try controlling them by trying to match experimental and control groups by the characteristics key to confounding variables. Another way they control them is by including the extraneous variable in the study design (Flannelly et al., 2014). For example, the variable of concern is age. The research might decide to incorporate it by grouping the participants into subgroups made up of age groups.

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