Experimental variables are factors measured in an experiment. These variables can either be dependent or independent. Independent variables are variables that are not affected by the measurements to be made in an experiment ( Moyé, Chan & Kapadia, 2017) . It might also be used to refer to the manipulated factors in an experiment. Independent variables are hypothesized to cause certain effects to dependent variables. In research dependent variables are factors that depend on the independent variables ( Moyé et al., 2017) . It is expected that the dependent variables will be affected by changes in the independent variables. Notably, independent variables can be one or more while only one dependent variable is present in each single experiment.
There are several real life situations where the concept of experimental (dependent and independent) variables is applicable. Weight for example is in most cases dependent of the height of the person. In this case weight is the dependent variable while height is the independent variable. It can be hypothesized that weight increases with increase in height. Another example in the health sector is the relationship between blood sugar levels and exercise. Taking blood sugar levels as the independent variable and exercise as the independent variable, we can hypothesize that increase in exercise hours lowers the level of blood sugar. A most practical case is the association between the number of learning hours and the scores in a test. Students who spend more time in their books tend to score more in exams. In fact, given the number of hours a student studies, we can come up with a mathematical model to predict their scores. The study discussed below explains the relationship between scores in particular courses and the average score in KPSS. It is expected that students who score more marks in the courses will definitely have a high score in the KPSS.
Delegate your assignment to our experts and they will do the rest.
Multiple regression aims at analyzing the linear relationship between one dependent variable and more than one independent variable. In the study, KPSS was the dependent variables and the independent variables were counselling, instructional techniques, educational psychology, evaluation, and program development (Gulden & Nese, 2013). The data used in the research comprised of end-of-term scores by 240 students from the five courses. The objective was to determine if the scores from the courses could be used to predict KPSS scores (Gulden & Nese, 2013). Statistical analysis was used to conduct Multiple Linear Regression on the collected data.
The results of the test showed that the five independent variables could be used to predict KPSS scores. The regression coefficients for measurement, educational psychology, teaching methods, counselling and curriculum development were 1.157, 0.090, -0.339, -0.195 and 0.078 respectively (Gulden & Nese, 2013). The linear model is: KPSS=9.811+1.157 measurement+0.078 curriculum dev’t+0.090 educational psychology-0.339 teaching methods-0.195 counselling (Gulden & Nese, 2013). R squared was 0.87 indicating that 87% of the variations in KPSS scores could be explained by score in the five courses (Gulden & Nese, 2013). The results showed that the scores from measurement course and teaching methods could possibly make the greatest contributions in determining the value of the dependent variable. The linear model could be used to illustrate the fact that the changes of the dependent variable depend on the changes in the independent variable.
Assuming that student 1 and student 2 scored 10 and 20marks on each course respectively. Student 1 would have a KPSS score of 17.721 while student 2 will have a score of 25.631. Clearly, an increase in the course scores (independent) increases the KPSS scores (dependent).
Independent variables are variables that are not affected by the measurements to be made in an experiment. Independent variables are hypothesized to cause certain effects to dependent variables. In research dependent variables are factors that depend on the independent variables. The concept of experimental variables is applicable in real life situations such as those presented in this paper. The case study used in this study best explains the relationship between dependent and independent variables.
References
Gulden, K. U., Nese, G. (2013). A study on Multiple Linear Regression Analysis. Elsevier Ltd.
Moyé, L. A., Chan, W., & Kapadia, A. S. (2017). Mathematical statistics with applications . CRC Press.
