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Background of this Study

Discuss about the Capability of Organization Growth Process.

There has been a recent trend in the growth of educational sector with special reference to the online education and online university. The success of any organization depends on the existing relationship depends on the total number of people graduating from the university (De Boer et al. 2014). In addition, the capability of the organization to retain its clients also matters a lot. This report focuses on the online universities growth process. The main purpose of this report is to find out the existing relationship between these two variables namely graduation rate and retention rate.

Graduation rate is the mathematical concept through which the percentage of students completing their graduation at any specific time with respect to the total number of enrolled student is calculated (Allen and Seaman 2013). Retention rate on other hand is the percentage of students who remains in the same college for the following years in order to complete their education. Both these rates are very crucial to evaluate the performance of any organization (De Boer et al. 2014).  

Online education is the matured stage of the concept of distant education, which originated long back in the 19th century (Spada 2014). It was the after the Second World War that this system was initiated in Afro-Asian countries and then expanded throughout the global economy. After this, America and UK got involved in imparting online education to their students through different modes including radio and television. Under this circumstance, there has been an increasing trend in the growth of online university and that induced us in constructing this report. The impact of the rising demand for online education, the constant increase in the number of colleges, the retention capacity of these online education centers and the number of students graduating from these online universities has been the primary focus of this report (Jordan 2014). Since, there has been a constant growth in this sector, this report tries to observe the impact of growth these colleges using the statistical tools of simple linear regression.

Through this report, around 29 different universities have been focused and their retention rate and graduation rate have been collected to evaluate the impact of these two criteria and the relationship amongst them. At the initial level scatter diagram has been used to show the relationship between the two variables and then the regression analysis has been done to confirm or reject the relationship already stated (Wooldridge 2015). The graduation rate has been taken as the dependent variable and the retention rate of these universities have been considered as as the independent variable. This scatter diagram is used to understand the correlation between the two variables. The way in which the dependent variable behaves can be identified from here (Sowell 2014). The scatter diagram below explains the relationship between the graduation rate and the retention rate of the online Universities operating in U.S.


Scatter Diagram of GR & RR

Figure 1: Scatter Diagram of GR & RR

Source: As Created by the Author

Through this diagram, it has been seen that all the scattered points are plotted in a particular pattern. There is positive relation in between the two variables. The straight line given in the diagram is used to measure the goodness of fit of the measurement. This concept is used to measure the reliability of the data in accordance with the theory in practice. It is clearly seen in the above diagram that there is no point on the fitted line which implies that the model does not show goodness of fit at all. On other hand, since almost all the observations are clustered very close to the fitted line, therefore the variables have weak positive co-relation amongst them.  This implies that universities having high retention rate is going to show high graduation rate and vice-versa. As in this scatter pot it can be seen that the university with 100% retention rate have nearly 60% and above graduation rate as highlighted by red arrows. In addition, the university, which show low retention rate, have lower rate of graduating students as highlighted by the blue arrows.

The analysis about the impact of retention rate on the number graduation rate of the online universities has been done with the help of regression model. The econometric tool with the help of which investigators try to establish a causal effect between two or multiple variables is known as Regression analysis (Gujarati 2014). Usually one variable is taken as the independent variable and the others are taken as dependent variable. The regression analysis can be classified into 7 different types namely, Linear regression, logistic regression, polynomial, step-wise, ridge regression, lasso regression and Lasso regression (Asteriou and Hall 2015). Usually, the first three categories are widely used. Linear regression can again be subdivided as simple and multiple linear regressions. In this report the retention rate of the open universities acts as the independent variable. Since, there are only two variables present, hence simple linear regression has been used to establish the relationship. Through this regression analysis, the economist tries to find out if there is existence of statistical significance between the variable. In other words, it tries to find out whether the sample reflects the estimated relationship of the population (Sowell 2014).

A regression equation can be written as Y = a + bX. In this equation X is the independent variable which helps in explaining all the consequences and the extent in which it is faced by the dependent variable. Y is the dependent variable in the model. The intercept ‘a’ allows the economist to understand the effect of the dependent variable when independent variable is 0. In this report the retention rates of all the mentioned universities has been taken as X and the graduation rate is taken as Y.  The regression analysis of the GR and RR are given below as follows:


Standard Error

t Stat












Results Obtained

From the table above the regression equation of the online universities can be formed. The estimated regression equation is given as:

In this equation intercept ‘a’ is 25.4229 and 0.2845 is the slope ‘b’. The slope coefficient helps in understanding the impact of unit change in independent variable on the dependent variable of the chosen model. Here, the slope coefficient 0.284 clearly indicates the fact that if there if 1 unit increase or decrease in the retention rate, the impact is going to be 0.284 unit increase or decrease in the graduating rate of the 29 open universities surveyed.

In order to check out the existence of any significant association if any between the GR and RR, there is a need to concentrate on the p-value that has been yielded from the regression equation. To understand the use of p-value, first there is a need to understand the null hypothesis which is being tested by this value. This hypothesis states that there is no significance between the given observation set of variables and no variation occurs in between them (Gujarati 2014). In our model the p-value is 0.0001, which implies that there exists a highly significant association in between the variables and henceforth the null hypothesis of this regression analysis can and should be strictly rejected.

The given regression equation  do provide a good fit. The concept of fitness of a model comes from the urge to check out the digression of the observed value from the expected value as predicted by the model. In the given model p being 0.0001 is not even near to 1% level of significance and hence the chosen model is highly significant. In addition, the model also has high t-value which again establishes the significance of the model.

South University has an average level of retention rate of 51%. Under this circumstance, the graduation rate is very poor that is around 25%. Being the President of South University, I would get worried under this circumstance. The students are the main capital of any educational organization. Henceforth, a less number of total graduate rates are a bad omen for the online education system as a whole.

The data collected from the University of Phoenix suggests that retention rate of the university is only 4% whereas the the graduation rate is 28%. As the President of the University of Phoenix this result is going to bother me. This is because high graduating rate is indeed a bright aspect for a company but at the same time retention rate as low as 4% provides a negative vibes about the online university. Low retention rate also signals the potential customers to move away from this university. In addition, from the overall calculation there has been a positive relationship between these two variables. Hence, here since this relationship gets violated, this is a matter of concern for the president to uplift the retention level and maintain a subtle balance between these two factors. 

The true impact of the retention rate and graduation rate on the rising trend in online university can only be properly analyzed with the help of R2 and Adjusted R2. In the initial stage of this report, the scatter diagram has been used to see the goodness of fit and the exiting co-relation between the variables of the model. From the scatter diagram it has been observed that none of the observations are lying on the fitted line but are clustering round the central point of the line. That clearly indicated that the model is not properly fitted. But the scatter diagram highlighted the existence of positive correlation between the two variables. Again while looking at the regression result it has been found that the line is getting best fitted. The regression statistics below provides with further modes of analysis.

Regression Statistics

Multiple R


R Square


Adjusted R Square


Standard Error




The table above shows that R2 is 0.449228. It implies that 44.9% variation of the model is explained by variables. Since, often this statistic gives misleading results, hence Adjusted R2 have also been calculated whereby it has been observed that the model can explain only 42.8% variation. It has been observed that retention rate plays a significant role in improving the performance of the online university whereas the graduating rate depends a lot on the retention rate. Any educational centre like University of Phoenix may face future problem in their field of operation due to the extreme low level of retention rate.

The educational industry is dynamic in nature. It has been constantly changing its outlook with the advancement of time. Students are also getting focused on multiple things at a time and hence could not give time to daily education. As a consequence to complete their degree they are opting for online universities and studying in accordance with their own schedule. Therefore from this entire report one concept came very clear and that is the retention rate of these open universities play a significant role in grabbing the attention of the potential student. Researchers are constantly engaged in finding out ways to bring in more students into online processes. Along with it there has been a constant increase in the number of online colleges which possess a tough competition to the existing ones. Hence it is recommended that the existing universities should try to constantly upgrade their facilities and make changes in their curriculum in accordance with the industrial needs. At the initial level they can retain their customer by providing them with discounts in their course fee. Then they may also retain the students by providing them with all time guide services through which the students may get guidance about their study material at any time of the day through online service. Also, providing a good career counseling and placement facility is surely going to attract and channelize the target customer towards these existing universities.


Allen, I.E. and Seaman, J., 2013. Changing Course: Ten Years of Tracking Online Education in the United States. Sloan Consortium. PO Box 1238, Newburyport, MA 01950.

Asteriou, D. and Hall, S.G., 2015. Applied econometrics. Palgrave Macmillan.

DeBoer, J., Ho, A.D., Stump, G.S. and Breslow, L., 2014. Changing “course” reconceptualizing educational variables for massive open online courses. Educational researcher, p.0013189X14523038.

Gujarati, D., 2014. Econometrics by example. Palgrave Macmillan.

Jordan, K., 2014. Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1).

Sowell, T., 2014. Basic economics. Basic Books.

Spada, K., 2014. Higher Education in the Digital Age [Book Review]. IEEE Transactions on Professional Communication, 57(2), pp.150-153.

Wooldridge, J.M., 2015. Introductory econometrics: A modern approach. Nelson Education.

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