American Journal of Theoretical and Applied Statistics

| Peer-Reviewed |

A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities

Received: Jul. 10, 2019    Accepted: Aug. 05, 2019    Published: Sep. 23, 2019
Views:       Downloads:

Share This Article

Abstract

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.

DOI 10.11648/j.ajtas.20190805.12
Published in American Journal of Theoretical and Applied Statistics ( Volume 8, Issue 5, September 2019 )
Page(s) 169-178
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

IP, ZINB, ZIP, Poisson Hurdle, V

References
[1] Dent, D. (2011). Innovation and Growth-the role of R & D. Dent Associates White Papers 11-02. Retrieved from http://www.dentassociates.co.uk/wpdivi/wp-content/uploads/2015/09/Innovation-and-Growth-the-role-of-RD.pdf
[2] Rosenberg, N. (2004). Innovation and economic growth. Retrieved from https://www.oecd.org/cfe/tourism/34267902.pdf
[3] Cassiolato, J. E., Lastres, H. M., & Maciel, M. L. (2003). Systems of innovation and development. UK: Edward Elgar Publishing.
[4] 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 http://www.bloomberg.com/news/articles/2015-06-04/look-who-s-driving-r-d-now
[5] Arnaut, D. (2010). Towards an Entrepreneurial University. International Journal of Euro- Mediterranean Studies, 3 (1), 135-152. Retrieved from http://www.emuni.si/press/ISSN/1855-3362/3_135-152.pdf
[6] 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.
[7] Cowan, K. (2013). Higher Education's Higher Accountability. American council on education leadership and advocacy. Retrieved from http://www.acenet.edu/the-presidency/columns-and-features/Pages/Higher-Education's-Higher-Accountability.aspx
[8] 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 http://eulp.co.uk/wp-content/uploads/2014/05/EULP-LEADERS-PAPER.pdf
[9] Clark, B. R. (1998). Creating entrepreneurial universities: Organizational pathways of transformation. Oxford: IAU PRESS.
[10] 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.
[11] Mullahy J. (1986). Specification and Testing of Some Modified Count Data Models. J Econometrics 33: 341-65.
[12] Lambert D. (1992). Zero-inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics 34: 1-14.
[13] 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.
[14] Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. Proc. 2nd Inter Symposium Information Theory 1973: 267-81.
[15] 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.
[16] 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.
[17] 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.
[18] Lee AA, Xiang L, Fung WK (2004). Sensitivity of score tests for Zero inflation in count data. Stat Med. 23: 2757-69.
Cite This Article
  • APA Style

    Alebachew Abebe. (2019). A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities. American Journal of Theoretical and Applied Statistics, 8(5), 169-178. https://doi.org/10.11648/j.ajtas.20190805.12

    Copy | Download

    ACS Style

    Alebachew Abebe. A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities. Am. J. Theor. Appl. Stat. 2019, 8(5), 169-178. doi: 10.11648/j.ajtas.20190805.12

    Copy | Download

    AMA Style

    Alebachew Abebe. A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities. Am J Theor Appl Stat. 2019;8(5):169-178. doi: 10.11648/j.ajtas.20190805.12

    Copy | Download

  • @article{10.11648/j.ajtas.20190805.12,
      author = {Alebachew Abebe},
      title = {A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {8},
      number = {5},
      pages = {169-178},
      doi = {10.11648/j.ajtas.20190805.12},
      url = {https://doi.org/10.11648/j.ajtas.20190805.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20190805.12},
      abstract = {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.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - A Comparison of Count Regression Models on Modeling of Instructors Publication Factors: Application of Ethiopian Public Universities
    AU  - Alebachew Abebe
    Y1  - 2019/09/23
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajtas.20190805.12
    DO  - 10.11648/j.ajtas.20190805.12
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 169
    EP  - 178
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20190805.12
    AB  - 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.
    VL  - 8
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics, College of Computing & Informatics, Haramaya University, Dire Dawa, Ethiopia

  • Section