The purpose of this study is to estimate the relation between the number of students who are retained in the higher learning with the online institutions with that of the students who got graduated in the United States and how is that associated with that of the students studying over online (Agénor & Aynaoui, 2015).
Education has become one of the challenges in the present generation as it is affected by various factors, the factors which are influencing the changes that are occurring in the educational institutions of higher learning are the geographical, the economic, the social, the cultural, the historical, the religious, the political and the technological needs (Chatterjee & Eyigungor, 2015). Each of these factors has its own impact on educational changes. Hence, online learning is chosen which is made simple, and highly accessible by everyone and is the least expensive way of learning the techniques with the use of advanced technology.
One of the biggest challenges in the higher education sector has been the recent growth of online universities. The Online Education Database is an independent organization whose mission is to build a comprehensive list of accredited online colleges (Gopinath, Helpman & Rogoff, 2014). The relation between the retention rate and the graduation rates of online learning differs significantly when compared to the traditional methods. Here, the data consists of the retention rate (%) and the graduation rate (%) for 29 online colleges in the United States.
The sample size that is taken consists of the variables such as the rate of retention which is known to be independent of any of the factors while the rate of graduation is known to be dependent on various numbers of factors (Hallström, Röös & Börjesson, 2014). The variable that is independent is taken as the base and is operated so that it is used to understand the kind of response given by the dependent variable. The linear regression of the given data set is expressed as follows:
y = a+bx
in which,
x is the independent variable
y is the dependent variable
b is the slope that is the gradient of the line and
a is known to be the intercept on the y-axis
By this, it is understood that any change in the component of the variable that is independent it will have a direct effect on the component changes in the dependent variable and eventually the gradient (Kessler, 2014).
The R-squared is known to be the statistical measure of how adjacent is the given data to the fitted regression line. It is also known as the determination coefficient, or the coefficient of the multiple regression determination (Nakajima & Telyukova, 2016). The R-squared is used to find out the percentage of the kind of response given by the variable variation which is explained by the linear model. The formula denotes:
R-squared = Explained variation / Total variation
The results are as follows:
| RR% | GR% | |
| Mean | 57.41 | 41.76 |
| Standard Deviation | 23.24 | 9.866 |
| Minimum | 4 | 25 |
| Maximum | 100 | 61 |
The graduation rate = α + β x Retention rate
Where,
α denotes the constant and
β is denoted as the parameter
| Variables | GR% |
| RR | 0.285 |
| Constant | 25.42 |
| Inputs | 29 |
| R2 | 0.4491 |
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There are some errors in the parentheses, which are standards and is found to be *** p<0.01 ** p<0.05.
It is observed that if the rate of retention is increased by the percentage then it will be resulting in the increase of the graduation rate by over 0.285 per cent which is significant statistically from the significance level at over 0.1 per cent (Pashchenko & Porapakkarm, 2013). From the above table, it can be known that there is a connection between the retention rate and the graduation rate in an increasing manner (Pashchenko & Porapakkarm, 2013).
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In order to find out the relationship between the rates of retention and the graduation rate, the statistical scatter plot has been used along with the simple linear regression graph in comparison with the other tools for estimation that has been employed. Hence, the results that are obtained are known to suggest that a relationship that is positive has been found to exist between the graduation rates and the retention rates (Riff, Lacy & Fico, 2014). Other than that, it also says that the model does not fit the regression equation appropriately as the variance has been found to be determined by 50 per cent only in the graduation rates. By this, it is evident that there might be various other factors that are affecting the graduation rates for the online higher learning programs which have to be found out.
From the study, it has been identified that the model did not provide the results that are satisfactory as it is estimated that there might various other factors which will be influencing the rates of graduation in their online learning programs in comparison to the rate of retention. So it is recommended to analyze thoroughly by extensive research regarding the factors that are responsible for the low graduation rates. This can be done with the help of various resources from the previous studies reference. The kind of impact that the rates of retention and the rate of graduation have by the factors will be helping the online learning programs to increase the graduation rate. The factors that are expected to have influence over the retention and the graduation rate include the economy, the structural analysis, the domain, the geographical data, the educator role, and the outline of the educational criteria, etc.
Agénor, P.R. and El Aynaoui, K., 2015. Public Policy, Industrial Transformation, Growth and Employment in Morocco: A Quantitative Analysis. Revue d’économie du développement, 23(2), pp.31-69.
Chatterjee, S. and Eyigungor, B., 2015. A quantitative analysis of the US housing and mortgage markets and the foreclosure crisis. Review of Economic Dynamics, 18(2), pp.165-184.
Gopinath, G., Helpman, E. and Rogoff, K. eds., 2014. Handbook of international economics (Vol. 4). Elsevier.
Hallström, E., Röös, E. and Börjesson, P., 2014. Sustainable meat consumption: A quantitative analysis of nutritional intake, greenhouse gas emissions and land use from a Swedish perspective. Food Policy, 47, pp.81-90.
Kessler, R.C., 2014. Linear panel analysis: Models of quantitative change. Elsevier.
Nakajima, M. and Telyukova, I.A., 2016. Reverse mortgage loans: A quantitative analysis. The Journal of Finance
Pashchenko, S. and Porapakkarm, P., 2013. Quantitative analysis of health insurance reform: Separating regulation from redistribution. Review of Economic Dynamics, 16(3), pp.383-404.
Schwert, G.W. and Stulz, R.M., 2014. Gene Fama’s Impact: A Quantitative Analysis.
Riff, D., Lacy, S. and Fico, F., 2014. Analyzing media messages: Using quantitative content analysis in research. Routledge.
Wase, N., Black, P.N., Stanley, B.A. and DiRusso, C.C., 2014. Integrated quantitative analysis of nitrogen stress response in Chlamydomonas reinhardtii using metabolite and protein profiling. Journal of proteome research, 13(3), pp.1373-1396.
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