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The Role of Validity, Relevance and Generalizability - Coursework Example

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The aim of this paper is to critically investigate the role that reliability, validity, and generalizability plays in both the quantitative and qualitative data. They enable researchers to draw meaningful conclusions and they affect the accuracy of information…
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The Role of Validity, Relevance and Generalizability
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Download file to see previous pages Validity, reliability, and generalizability are important because they give any measure of credibility. Quantitative data basically refers to data expressed in numerical form. It ranges from age to height of individuals and it is measured by the use of ratio, ordinal and interval scales.
However, qualitative data cannot be expressed in numerical form and is measured by the use of a nominal scale. A good example of qualitative data is gender and complexion. The aim of this paper is to critically investigate the role that reliability, validity, and generalizability plays in both the quantitative and qualitative data.
Validity in both quantitative and qualitative is encompassed on how truthful the data collected is. However, there is a great difference in terms of validity in quantitative and qualitative data. To begin with, quantitative research validity is based on the objectivity and neutrality of the data collected. It is also encompassed by the predictability of the data and how random the samples collected are (Lecompe, 2000). The forms of the validity of data include construct, face, predictive, concurrent and theoretical among many others.
Validity tells us more on how the instrument used for research is used to enable researchers to achieve the research’s objective. Validity in quantitative data determines whether the true intention of a research is achieved and the level of the truthfulness of the results. In most cases, researchers usually estimate the validity of quantitative data by asking a range of questions and in also search for the solutions in the research carried out by others. Validity in quantitative data is based on whether the instrument strikes the “bull’s eye” of the research object (Smaling, 2002). ...Download file to see next pagesRead More
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