Volume 8, Issue 5, September 2019, Page: 169-178
A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities
Alebachew Abebe, Department of Statistics, College of Computing & Informatics, Haramaya University, Dire Dawa, Ethiopia
Received: Jul. 10, 2019;       Accepted: Aug. 5, 2019;       Published: Sep. 23, 2019
DOI: 10.11648/j.ajtas.20190805.12      View  309      Downloads  108
Instructors’ publication (IP) is one of a major activity in higher education institutes. Currently, IP faced problem both high prevalence and severity in Ethiopian public universities. Publication was affected approximately around 352 (73.9%) instructors have not done publication in Ethiopian public universities even if there is a problem in both developing and developed countries. Since, the outcomes from IP factors are mostly discrete variable; they are often modeled using advanced count regression models. It is therefore, the purpose of this study was to determine the appropriate count regression model that efficiently fit the IP data and further to identify the key risk factors contributing significantly to IP in public universities in Ethiopian. The data were collected between November 2015 through November 2016 from selected thirteen (13) public universities in Ethiopian through both questionnaires and interview. A cross sectional study design was employed using IP data. A simple random sampling technique was applied to the population of Ethiopian public universities to obtain a sample of 13 universities or 476 individual instructors were selected. The average age of the 476 participants were found to be 30 years with 31 (6.5%) being females and 445 (93.5%) being males. The count outcomes obtained were modeled using count regression models which included Poisson, Negative Binomial, Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP) and Poisson Hurdle regression models. In order to compare the performance and the efficiency of the listed count regression models with respect to the IP data, the various model selection methods such as the Vuong Statistic (V) and Akaikes Information Criterion (AIC) were used. The ZINB count regression model with reference to the values of the Vuong Statistic and AIC were selected as the most appropriate and efficient count regression model for modeling IP data. Based on the ZINB model the variables age, experience, average work-load, association member and motivation to work were statistically significant risk factors contributing to IP in Ethiopian public universities.
IP, ZINB, ZIP, Poisson Hurdle, V
To cite this article
Alebachew Abebe, A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities, American Journal of Theoretical and Applied Statistics. Vol. 8, No. 5, 2019, pp. 169-178. doi: 10.11648/j.ajtas.20190805.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Dent, D. (2011). Innovation and Growth-the role of R & D. Dent Associates White Papers 11-02. Retrieved from
Rosenberg, N. (2004). Innovation and economic growth. Retrieved from
Cassiolato, J. E., Lastres, H. M., & Maciel, M. L. (2003). Systems of innovation and development. UK: Edward Elgar Publishing.
Philips, M., Coy, P. (2015). Look Who‘s Driving R & D Now: Companies spend to boost productivity, while government cuts back on research. Bloomberg. Retrieved from
Arnaut, D. (2010). Towards an Entrepreneurial University. International Journal of Euro- Mediterranean Studies, 3 (1), 135-152. Retrieved from
Etzkowitz, H. (2003). Innovation in Innovation: The Triple Helix of University-Industry-Government Relations. Social Science Information, 42 (3), 293-337. doi: 10.1177/05390184030423002.
Cowan, K. (2013). Higher Education's Higher Accountability. American council on education leadership and advocacy. Retrieved from's-Higher-Accountability.aspx
Gibb, A., Haskins, G., Hannon, P., & Robertson, I. (2012). Leading the Entrepreneurial University: Meeting the entrepreneurial development needs of higher education institutions. University of Oxford. Retrieved from
Clark, B. R. (1998). Creating entrepreneurial universities: Organizational pathways of transformation. Oxford: IAU PRESS.
Rothaermel, F., Agung S., & Jiang, L. (2007). University entrepreneurship: a taxonomy of the literature. Industrial and Corporate Change, 16, (4), 691-791 doi: 10.1093/icc/dtm023.
Mullahy J. (1986). Specification and Testing of Some Modified Count Data Models. J Econometrics 33: 341-65.
Lambert D. (1992). Zero-inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics 34: 1-14.
Lee, Y., & Gaertner, R. (1994). Technology Transfer from University to Industry. A Large-Scale Experiment with Technology Development and Commercialization. Policy Studies Journal, 22 (2), 384-399. doi: 10.1111/j.1541-0072.1994.tb01476.x.
Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. Proc. 2nd Inter Symposium Information Theory 1973: 267-81.
Siegel, D. S., Waldman, D. A., Atwater, L. E., & Link, A. N. (2003). Commercial knowledge transfers from universities to firms: improving the effectiveness of university-industry collaboration. The Journal of High Technology Management Research, 14 (1), 111-133. doi: 10.1016/s1047-8310(03)00007-5.
De Coster, R., & Butler, C. (2005). Assessment of proposals for new technology ventures in the UK: characteristics of university spin-off companies. Technovation, 25 (5), 535-543. doi: 10.1016/j.technovation.2003.10.002.
Lee, S. S., & Osteryoung, J. S. (2004). A Comparison of Critical Success Factors for Effective Operations of University Business Incubators in the United States and Korea. Journal of Small Business Management, 42 (4), 418-426. doi: 10.1111/j.1540-627x.2004.00120.x.
Lee AA, Xiang L, Fung WK (2004). Sensitivity of score tests for Zero inflation in count data. Stat Med. 23: 2757-69.
Browse journals by subject