Modelling of Instructors Publication Factors in Ethiopia Public Universities: Advanced Count Regression Models

Abstract

Instructors’ publication (IP) is one of the major activity in higher education institutes. Currently, IP faced problem both high prevalence and severity in Ethiopia public universities. Even if the problem is common to both developed and developing countries, about 352 (73.9 %) of the instructors employed by public universities in Ethiopia have been affected by a lack of scholarly publications. Since the outcomes from IP factors are mostly discrete variable; they are often modelled using advanced count regression models. The purpose of this study was to model 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 Ethiopia. The data were collected between November 2015 through November 2016 from selected thirteen (13) public universities in Ethiopia through both questionnaires and interview. The cross-sectional study design was employed using IP data. A simple random sampling technique was applied to the population of Ethiopia public universities to obtain a sample of 13 universities or 476 individual instructors were selected. The average age of the 476 participants was found to be 30 years with 31(6.5%) being females and 445(93.5%) being males. The count outcomes obtained were modelled using count regression models which included Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP) and Poisson Hurdle regression models. To compare the performance and the efficiency of the listed count regression models concerning the IP data, the various model selection methods such as the Vuong Statistic (V) and Akaike’s Information Criterion (AIC) were used. The ZINB count regression model concerning the values of the Vuong Statistic and AIC was selected as the most appropriate and efficient count regression model for modelling 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 Ethiopia public universities.