Evaluation of the Impact of E-Laboratory on Engineering Research and Development in Nigeria: Emphasis on Universities in Delta State (Published)
The study evaluated the impact of electronic laboratory to engineering research and development in Universities in Delta State. Three research questions and three corresponding hypotheses guided the study. Design of the study was descriptive survey. Population of the study comprised 12, 482 (8,338 academic and 4,144 administrative) while the sample for the study consisted of 747 (382 academic and 365 administrative) staff who were sampled for the study using random sampling technique. Instrument used for collecting data for the study was an 18 item questionnaire titled. The instrument was validated by three experts; one each from the Departments of Mechanical Engineering, Measurement and Evaluation, and Educational Management who assisted in determining the face and content validities of the questionnaire. The reliability of the questionnaire was determined using Cronbach Alpha with an index of 0.88. The research questions were answered using mean and standard deviation while the hypotheses were tested using z-test at 0.05 level of significance. The study revealed that online simulation laboratory, three-dimensional laboratory and computerized science laboratory all impact on engineering research and development. There was no significant difference in the opinion of academic staff and administrative staff on the impact of online simulation laboratory and three-dimensional laboratory but a significant difference existed on the impact of computerized science laboratory. Based on these findings, it was recommended that more technological facilities should be provided for the conduct of engineering research activities and the staff who carry put this research should be trained on modern technological research skills from time to time.
This paper sought to establish the state of Human capital (HC) in selected public universities in Zimbabwe in terms of prevalence of senior academics such as associate professors and professors as well as the impact on the academic and economic development of the universities and the country at large. A good number of key literary sources were consulted on which the theoretical underpinnings of the paper were grounded. A sample of five state universities was chosen. Convenient sampling was adopted whereby the universities were chosen on the basis of availability of quality and completeness of data on the university website. Data was collected on the qualifications and grade of faculty deans and department chairpersons, and lecturing staff from the universities’ websites. All in all, the study covered a total of 18 faculties and 77 departments. The results of the study showed that there was a serious absence of senior academics in selected universities compared to their counterparts in the region and that this was having a negative impact on the quality of the universities’ academic and economic activities.
Demystifying Probability Sampling designs in Research (Review Completed - Accepted)
The purpose of this paper is to improve the quality of published research papers by demystifying the concept of probability sampling designs in research. The paper describes how to decide and present probability sampling designs in research and how to determine the sample size. It was motivated by the observation that, researchers in published journal articles guided by quantitative methods either present misconceptions of probability sampling or are silent about the sampling design. The study is guided by qualitative methodologies. Data was collected by documentary analysis of research and mathematics textbooks as a basis for the ideal concept of probability sampling designs and determination of sample size. This was followed by an analysis of a purposive sample of 57 research papers in 9 different journals, 45 dissertations by masters’ students and 92 research projects submitted by undergraduate students. These were analyzed for their presentation of probability sampling. The study found that, researchers and students are not including how they established the sample size. They confused random sampling for any haphazard activity associated with selecting participants. They are not sure of the conditions under which simple random sampling, systematic sampling, stratified sampling and cluster sampling must be applied. Population analysis in terms of variable distribution is missing. In addition, their descriptions of how the sampling is done (process) needs improvement. These errors are traced to research methods textbooks which are not presenting probability sampling techniques clearly for novice researchers. This study recommends that probability sampling is suitable when the total population is known. Simple random sampling should be applied when the variable is uniformly distributed. Systematic sampling is proper when the variable follows a linear dependency. Stratified sampling is appropriate when the variable is in strata and cluster sampling is fitting when variables emerge in groups. Sample size can be determined from table provided. An illustrative example is included for researchers’ and students’ discussion