When it comes to variables and their implications to find a correct answer, it is important to understand that they are interconnected. As a result, the right choice of the first parameter will help to experimentally determine the balanced measure of the dependent one and illustrate the result of working with uncertainty and unintentional confusion. Due to this fact, evidence-based practice projects need a logical and reliable explanation of independent and dependent variables choosing, which will directly influence the experiment outcome.
To begin with, any professional experiment requires cause-and-effect interconnection, which leads its author to the specific result. When it comes to cause the creation, it is possible to put some word-interpreted data at the beginning of the process. However, when the experiment ends and the outcome analysis must be made, the numerical data is indispensable due to the further analysis needs. More specifically, providing word data that cannot be written numerically is impossible for the qualitative evidence-based project due to the structuring obstacle (Nevile et al., 2020). Most of the data should be categorized by some specific criteria so that if independent variables could be easily changed into another type of data, the outcome of the experiment must be written numerically owing to the further qualitative analysis needed.
In addition, every practice that requires evidence must be provided multiple times for the “clarity” of data. As a result, independent and dependent variables are needed to provide a qualitative analysis. However, there is a significant dependent variable issue since the combination of factors that could be implemented in the same experiment might influence the outcome. To find the solution, it is crucial to provide clear experiments and double-check the independent variable factor singularity (Candel et al., 2019). Consequently, dependent variables will become more interesting for the evidence-based project due to their credibility.
References
Candel, J., & Daugbjerg, C. (2019). Overcoming the dependent variable problem in studying food policy.Food Security, 12(1), 169–178.
Nevile, A., & Biddle, N. (2020). The “Dependent Variable Problem”: How Do We Know What Caused Desired Change?The Palgrave Handbook of the Public Servant, 1(1), 1–16.