The hallmarks or main distinguishing characteristics of scientific research

The hallmarks or main distinguishing characteristics of scientific research

- Purposiveness . The research is started with a definite aim or purpose.
- Rigor- Rigor connotes carefulness, scrupulousness and the degree of exactitude in research investigations. Rigorous research involves a good theoretical base and a careful thought-out methodology.
- Testability- Scientific research lends itself to testing logically developed hypotheses to see whether or not the data support the educated conjectures or hypotheses that are developed after after a careful study of the problem situation.
- Replicability- The results of the tests of hypotheses should be supported again and yet again when the same type of research is repeated in other similar circumstances
- Precision and confidence- Precision refers to the closeness of the findings to ‘reality’ based on a sample. It reflects the degree of accuracy or exactitude of the results on the basis of the sample, to what really exists in the universe. Also referred to as confidence interval in statistics.
Confidence refers to the probability that our estimators are correct. That is, it is not merely enough to be precise, but it is also important that we can confidently claim that 95% of the time our results will be true and there is only a 5% chance of our being wrong. It is also known as the confidence level. The narrower the limits within which we can estimate the range of our predictions (i.e., the more precise our findings) and the greater the confidence level we have in our research results, the more useful and scientific the findings become. In social science research, a 95% confidence level- which implies a 5% probability that tha findings may not be correct-is acceptable as conventional, and is usually referred to as significance level of 0.05 (p=0.05). the greater the precision and confidence we aim at in our research, the more scientific is the investigation and the more useful are the results.
- Objectivity- The conclusions drawn through the interpretation of the results of data analysis should be objective i.e they should be based on the facts of the findings derived from actual data and not our own subjective or emotional values.
- Generalizability- Generalizability refers to the scope of applicability of the research findings in one organizational settings to other settings. For wider generalizability, the research sampling design has to be logically developed and a number of other details in the data collection methods need to be meticulously followed.
- Parsimony. Simplicity in explaining the phenomena or problems that occur and in generating solutions for the problems, is always preferred to complex research frameworks that consider an unmanageable number of factors. Economy in research models is achieved when we build into our research framework a lesser number of variables that explain the variance more efficiently than a complex set of variables that only marginally add to the variance explained. Parsimony can be introduced with a good understanding of the problem and the important factors that influence it.

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Titany answered the question on October 21, 2021 at 07:33

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