International Business Sample
November 14, 2021ECO82001 – Economics and Quantitative Analysis
November 14, 2021
Purpose
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).
Background
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.
Method
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
Result
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 scatter diagram represents the retention rate, which is an independent variable, and the graduation rate the dependent variable, it is noticed from the graph that as the rate f retention increases the graduation rate also increases.
- By using the independent variable that is the rate of retention, a simple equation of he linear regression will be helping us to calculate the graduation rates for the online programs of higher learning. The comparison of these elements is found o have the graduation rate as follows:
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).
- It has been observed from the graph and the calculations that the statistical relationship is found to exist between the rate of retention and the graduation rate which can be seen from the above table. The rate of graduation is found to increase with the increase in the rate of retention by the factor of 0.285 per cent, which is the significant value statistically at the significance level of 0.1 per cent (Riff, Lacy & Fico, 2014).
- It has been found that the regression equation did not provide the best fit of the model as the determination coefficient of the given data set is noticed to be 0.449 which means that in the completion rate only 50 per cent of the variance has been found to be defined by the holding rate.
- From the calculations that are carried out on the given sample size, it has been found that the rates of graduation do not depend totally on the rates of retention and therefore various other factors which might increase the rate of graduation has to be considered.
- After reviewing the results, the performance of the University of Phoenix compared to other online universities it has been noticed that the rate of retention to be low and the rate of graduation can be said to be lower than the average rate. The concern is from this part where the programs in the university have to be reviewed and should try to understand the reasons behind the low retention rate and how to fix it.
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Discussion
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.
Recommendations
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.
References
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.
