In wake of the growing trend of online university education in US, the given report aims to carry out a quantitative analysis based on the data provided so as to present findings in relation to these universities performance. Two key variables that have been used for performance analysis are Retention Rate (RR) and Graduation Rate (GR). The underlying relationship between these two variables would also be analysis. Based on this analysis, prudent recommendations can be offered in order to improve the US online university education so that this can contribute to the economic growth in a significant manner.
In the backdrop of technological revolution leading to developments of communication aids which are internet based, the delivery of product and services has seen a fundamental change. With regards to education, this has resulted in growing popularity of online courses. These courses tend to be flexible and offer a large degree of convenience to the students when compared to the classroom based education system. Also, owing to assess over the internet, the cost of these courses is comparatively a fraction of the offline courses (Craig, 2015). But online courses do have their concerns particularly in the context of dedication of the students which is apparent in higher dropouts of online courses as compared of offline courses (Hill, 2015). As a result, the objective of the given report is to highlight the underlying relationship between RR and GR for US based online universities so that inference can be made with regards to their relevance to the overall education system.
Taking into consideration the sample data of 29 US based online colleges, the underlying analysis is based on applying relevant statistical techniques. The summary statistic for the given variables has been outlined so that an overall picture of the industry (online education) can be gauged from the sample provided. In order to highlight the underlying relationship between RR and GR, scatter plot has been drawn with the former being the independent variable and latter being the dependent variable. Also, a linear regression model has been worked out based on the sample data so as to obtain estimates of GR based on the different values of RR across online colleges across US. The regression analysis is used for providing the colleges with useful recommendations in order to boost their overall performance and improve service quality.
The scatter plot above clearly highlights a positive relationship between RR % and GR%. Also, the magnitude of this linear relationship seems to be moderately strong considering that deviations of the scatter points from the line of best fit are not very high (Hair et. al., 2015).
From the above regression equation, it is clear that the slope comes out as 0.28 which indicates that a change in the RR by 1 percentage point would lead to corresponding change in the GR by 0.28 percentage point. Also, considering that the magnitude of the slope is positive, it reflects that the direction of change for the two variables would be same (Hillier, 2016).
Let the significance level for the test be 5% or 0.05.
For the slope coefficient of RR, as indicated in the regression output, the t stat is 4.69 with a corresponding p value of 0.00.
As the computed p value for the slope coefficient (i.e. 0.00) is lower that the level of significance, hence, it would be correct to conclude that the available evidence warrants rejection of null hypothesis and acceptance of alternate hypothesis (Flick, 2015). As a result, it is fair to conclude that the significance of slope coefficient is proven which also implies that RR and GR have a significant linear relationship.
(Hastie, Tibshirani and Friedman, 2014).
The results highlight that average RR% is about 55% while the average GR% is about 41% which seems reasonable. A higher dispersion is noticed in the RR% considering the wide range for this variable. The scatter plot highlights a positive and strong linear relationship between RR% and GR%. Further, the regression model highlights that 1% change in RR% would change GR% by 0.28% in the same direction. Also, the slope coefficient is found to be significant and the model a good fit (Hair et. al.,2015). Further, low GR% is a matter of concern for South University while low RR% is the concern for University of Phoenix.
A key strength of the analysis is that it is based on statistical analysis which lends credibility and enhances objective analysis. A limitation is that there is no information with regards to the sampling technique deployed to select the 29 colleges and also the market share in online education represented by these. However, the results obtained in the results section are supported from similar studies which enhances the relevance of the results. The results do have policy implications with regards to regulation of content and other measures that tend to have an impact on the quality of these online courses.
Considering the analysis carried out below, following recommendations may be offered to the online colleges in USA.
References
Craig, R.(2015),A Brief History (And Future) Of Online Degrees, [Online] Available at https://www.forbes.com/sites/ryancraig/2015/06/23/a-brief-history-and-future-of-online-degrees/#5be1eb3a7e37 [Accessed January 26, 2019]
Flick, U.(2015), Introducing research methodology: A beginner’s guide to doing a research project, New York: Sage Publications, pp. 67-71
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of business research methods, New York: Routledge, pp. 103-106
Hastie, T., Tibshirani, R. and Friedman, J.(2014), The Elements of Statistical Learning, New York: Springer Publications, pp. 89-93
Hill, P. (2015),No Discernible Growth in US Higher Ed Online Learning, [Online] Available athttps://mfeldstein.com/no-discernible-growth-us-higher-ed-online-learning/ [Accessed January 26, 2019]
Hillier, F. (2016), Introduction to Operations Research, New York: McGraw Hill Publications, pp. 143-147
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