Tag Archives: Sampling

On The Comparison of Some Methods of Allocation in Stratified Random Sampling for Skewed Population (Published)

A study to evaluate and compare some methods of allocation in stratified random sampling suitable for the estimation of population total of a skewed population was carried out in this paper. We looked at three methods of allocation in the above scheme namely; Optimum allocation, N-proportional allocation, and variable (X) proportional allocation methods. We investigate the condition under which one method of allocation is better than the other using three sets of real life data on staff and student enrolment, collected from the record of the Teaching Service Commission (TESCOM) Oyo State Nigeria. The third set of data is on Income and expenditure of Industrial and General Insurance (IGI) Plc.   We found out that optimum allocation is the least and the best despite variation observed in the sizes of nh within the strata.  

Keywords: Allocation, Estimation, Sampling, Skewness, Stratification

THE IMPERATIVE OF POPULATION SAMPLING IN SOCIAL SCIENCE RESEARCH (Published)

The quality of any research and its findings is connected and /or shaped by the process diligently followed. This suggests an unending link between the methodology (process) and the quality / outcome of a research. This paper explains the imperative of population and sampling and the value it adds to the quality of research and its findings. The paper is an explanatory one that analyses documented views of experts in the field of research and correlate same with the experience of the researcher. Observations and assessment of researches conducted by students of undergraduate studies and most times the graduate students’ revealed research procedural lapses. These lapses are mostly methodological that affect considerably the quality/outcome of their researches. The emphasis hinges on the need to pay required attention to the population and sampling procedure to ensure accurate research findings not speculative outcome.

Keywords: Population, Quality, Sampling, findings

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

Keywords: Probability, Research, Research Methods, Sampling

Determinants of Growth Of Microfinance Organisations in Kenya. (A Case Study of Small Micro Enterprise Programme – Smep, Voi.) (Published)

The main objective of this study was to determine the key factors that determine the growth of the microfinance institutions. The target population was the people that participated in these MFI’s which in many cases were found to be women groups, middle and low income earners in Voi, however concentration was mainly on the individuals / groups that were registered with the SMEP in Voi.

The method used for this research was exploratory survey. Data collection methods such as questionnaires, observation and interviews were used. Sampling technique was used and results analyzed qualitatively and quantitatively in terms of descriptive statistics. Pie charts, bar charts and frequency distribution tables were incorporated in data presentation. Finally, conclusion on the factors determining the growth of microfinance institutions in Kenya were arrived at and areas for further research pinpointed. Recommendations for better operation and handling of the microfinance were noted

 

Keywords: Descriptive, Earners, Exploratory Survey, Microfinance Institutions, Sampling