Compare Independent, Dependent, Extraneous Variables

Introduction

In research, a variable refers to a concept that symbolizes a variation within a group of items. Variables are inherent properties, characteristics or qualities of an event, object, situation or individual that change, such as weight, height, color, age, income, blood type, number of and degree of (Abu-Bader, 2020). They are the basic elements that are measured in research studies. These variables are both observable and measureable. There are many different kinds of variables, including independent variables, dependent variables, continuous variables, discreet variables and extraneous variables. These classes are centered on their actions, nature and effects on other variables. The purpose of this paper is to compare independent, dependent and extraneous variables. 

Independent, Dependent, Extraneous Variables

Most research studies encamps the examination of cause-effect relationships between variables. An independent variable is defined as any activity or intervention that is varied or manipulated to produce an effect on dependent variable. Independent variable is the presumed cause for change in a dependent variable (Grove, & Gray, 2019). The dependent variable is also called intervening variable. It is variation or change that takes place in the study population when antecedent factors are changed. It is the presumed effect of another variable within a study (Grove, & Gray, 2019). An example is the study on the relationship between exposure to computer video games and violent behavior among teens. The independent variable is exposure to computer video games and dependence variable is violent behavior. 

Independent and dependent variables are interrelated and observed in experimental, quasi-experimental, correlational and pre-experimental studies. The dependent variable is a function of independent variable. This means that the value of dependent variable changes as the independent variable changes (Abu-Bader, 2020). Therefore, the dependent variable is the means by which changes effected in the independent variable are measured.  Changes in the dependent variable is assumed to be reliant on changes in the independent variable. Many dependent variables have many causes. For example, if researchers were interested in investigating factors influencing people’s weight, they might consider diet, height and physical activity.

Variables are not inherently independent or dependent. That is, a variable could be dependent in one study and independent in another study (Abu-Bader, 2020).  For instance a study could look into the effect of physical activity (independent variable) on osteoporosis (dependent variable) and another study could examine the effect of osteoporosis (Independent variable) on the incidence of bone fracture (dependent variable).

            Extraneous variable is a variable which interferes with the relationship between the dependent and independent variables, and thus needs to be controlled (Abu-Bader, 2020).  When present, the extraneous variable competes with independent variable in explaining the outcome. Thus it must be controlled to prevent it from influencing the outcome.  Extraneous variable are present in most studies and may affect the integrity of study variables to be measured. If this variable happens to influence the outcome, it is call a cofounding variable. Extraneous variable is capable of destroying the integrity of any research study depicting the cause-effect relationship. For example, researchers might be interested in examining whether high vehicle exhaust concentrations could influence asthmatic incidence in adults. Since it is not feasible to expose participants to high vehicle exhaust concentrations, the researchers comparing two groups of participants with differential vehicle exhaust exposure would depend on the natural experiment. It happens that in the natural experiment, residents living near high levels of vehicle exhaust can also live close to factories that contaminate the environment. As a result, pollutants from factory can increase the incidence of asthma. In this case, factory particulate matter affects the expected outcome and contribute to bias.     

References

Abu-Bader, S. (2020). Using Statistical Methods in Social Science Research: With a complete SPSS guide. S.l.: Oxford University Press Us.

Grove, S. K. & Gray, J. R. (2019). Understanding Nursing Research: building evidence-based practice. Elsevier.     

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