17 November 2023 05:24 |
Updated: November 17, 2023 05:39
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Interpretation of results is one of the key stages in writing scientific journal articles. This stage not only demands clarity and precision in presenting findings, but also requires the application of higher order thinking skills outlined in the revised Bloom’s Taxonomy. This stage involves knowledge, understanding, application, analysis, evaluation, and creation, all of which contribute to forming a comprehensive and insightful interpretation of the data that has been analyzed.
#Knowledge
The basis of the interpretation of results is a solid knowledge of the research subject and the methodology used. Researchers must have a deep understanding of the theoretical and practical context of their studies, including knowledge of previous research and the contributions they hope to make. This knowledge helps in understanding the significance of the findings and how they relate to the broader field of study.
#Understanding
A deep understanding of the results obtained is essential. This involves more than just knowing what the outcome will be; it’s about understanding what those results mean in a larger context. Researchers need to be able to explain the results in relation to the hypothesis and research questions, as well as understand the implications of the findings for existing knowledge in the field of study.
#Application
Application in the context of interpreting results relates to the ability to use knowledge and understanding in a practical context. This includes applying the results to answer research questions and hypotheses. Researchers must be able to relate their findings to existing theory and previous research, as well as show how their results expand or challenge existing understanding.
#Analysis
Analysis here involves breaking down findings into components to understand their meaning. Researchers must consider not only what the results are, but also why they occur. This may involve investigating factors that may influence the results, such as bias in the sample or data collection methods. Researchers must be able to separate relevant data from that which is not and understand the relationships between various data elements.
#Evaluation
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