As a quantitative research approach, it might be essential to look into other forms of data collection variables. A choice of correlational study using binary logistic regression is a viable option for data collection variables. However, depending on variability and how multiple the data could be, I might suggestion looking further into multiple regression. Multiple regression is another form of bivariate regression that allows for the examination of more than one independent variable (Frankfort-Nachmias & Leon-Guerrero, 2015). For example, I read an article by Sim Chong, Beh Boon, Lim Hwee & Mat Jafri (2015), which used multiple regression analysis because there were multiple independent variables to correlate. The date on the depletion of petroleum materials such as non-energy, LPG, diesel, kerosene, refinery gas, ATF and AV Gas, fuel oil and motor petrol were used as independent predictors (or variables) to create the multiple regression equations for CO2 (Sim Chong, Beh Boon, Lim Hwee & Mat Jafri, 2015). The authors used multiple linear regression in the most appropriate fashion. That is for the reason that multiple regression is valuable for breakdown of data composed of using varied research designs, and containing experimental, quasi-experimental, and non-experimental strategies (Orme & Buehler, 2001). The authors statistically examined the data of the correlation between intake of petroleum produces and CO2 emissions via Minitab 14 (Sim Chong, Beh Boon, Lim Hwee & Mat Jafri, 2015). This system is seemed to be one of the most commonly utilize method of expressing the dependency of a response variable on multiple independent variables and to achieve linear input.
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.
Orme, J. G., & Buehler, C. (2001). Introduction to multiple regression for categorical and limited dependent variables. Social Work Research, 25(1), 49-61.
Sim Chong, K., Beh Boon, C., Lim Hwee, S., & Mat Jafri, M. Z. (2015). Multiple Regression Analysis in Modelling of Carbon Dioxide Emissions by Energy Consumption Use in Malaysia. AIP Conference Proceedings, 1657(1), 1-5. doi:10.1063/1.4915185.
