This paper discusses quantitative and qualitative research methods with relevant examples as the major research approaches in social sciences. It objectively analyses these research methods, highlighting their types, characteristics, strength and weaknesses. The paper concludes with summations that much can and have actually been achieved when researchers in the various fields of social sciences themselves use their ingenuity to determine carefully when to use them individually and when to use in combination.
Research, the scientific quest for knowledge discovery and dissemination, is done in accordance with specific research methods. Using documental analysis research method and a randomly drawn sample of 1557 from a population of 46,376 lecturers, results showed that single-subject research is the least adopted method in Nigeria; probably because it has the greatest dearth of readily available information. Single-subject research method is however very appropriate for establishment of cause-and-effect relationships between variables. It is a special experimental methodological approach for investigation of the effects of intervention measures on an individual’s clinical behavior; using the very subject as his own control by changing the intervention treatment conditions presented to him and carefully assessing via repeated measurements, the impact of the changes on the subject as he exhibits certain new behavior a number of times. This paper has provided elaborate information on five single-subject designs (A→B→A→B, multiple-baseline, interaction, alternating-treatment, and changing-criterion); and four single-subject data analysis techniques (visual analysis, therapeutic criterion study, interrupted time-series statistics, and statistical analysis) to fill the existing knowledge gap.
Keywords: Alternating-treatment design, A→B→A→B design, Cause-and-effect relationships; Single-subject designs, Changing-criterion design, Documental analysis, Interaction design, Multiple base-line design, Research Methods, Single-subject data analysis techniques, Single-subject research
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