The linear regression model is a critical decision making tool used in organizations. Linear regression helps to establish the type of relationship that exists between two types of variables namely independent and dependent factors. The association between the variables in the linear regression model is summarized using a straight line that is commonly known as “best fitting line”. The independent and dependent variables in a simple linear regression are inherently different; but they are used jointly in quantitative and qualitative research.
There is one significant difference between independent and dependent variables in a linear regression that is revealed in their definition. According to Hoffmann and Shafer (2015) , the independent variable is controlled during experiments to determine whether it has an effect on another variable. During experiments, the independent variable is manipulated and controlled to determine how the change will affect the independent variable. Conversely, dependent variables respond to the changes of the independent variable ( Faraway, 2016) . During studies or scientific experiments, the dependent variables are measured and monitored to determine their response to variation of the independent variables. Another difference is that the independent variables are donated by x, while dependent variable is donated by y during regression analysis. Lastly, the independent variable is presumed to be the cause of a relationship, while dependent variable is the observed effect.
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Examples of the use of independent and dependent variables in qualitative and quantitative research increases the understanding about their difference. An example of quantitative research in a simple regression model is to determine the impact of height, independent variable, on the BMI, dependent variable. For qualitative research, the model is used to determine the impact of early sexual activity among girls, independent variables, on the development of the human papillomavirus infection, dependent variable. From the two example, the dependent and independent variables are easily recognizable.
References
Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models . Chapman and Hall/CRC.
Hoffmann, J. P., & Shafer, K. (2015). Linear regression analysis . Washington, DC: NASW Press.
