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Issues of Trustworthiness and Writing Analyses - Coursework Example

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It is particularly crucial to decision-making and coming up with conclusions from data collected. It is however very urgent that a person makes the correct diagnosis from that data collected so as to understand…
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Issues of Trustworthiness and Writing Analyses
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Issues of Trustworthiness and Writing Analyses Issues of Trustworthiness and Writing Analyses Interpretation of data is an important part of the research process. It is particularly crucial to decision-making and coming up with conclusions from data collected. It is however very urgent that a person makes the correct diagnosis from that data collected so as to understand the phenomenon being studied. Qualitative techniques are essential in solving some types of questions. Data analysis involves the inspection, transforming and modeling data with the aim of getting useful information. The data analysis process is highly adequate because of reasons such as the following. Avoidance of bias is a major issue in the interpretation of data. It is necessary that the reader considers the importance of non-biased data. Adequate data analysis procedures should avoid bias at all cost. Not only is the reader supposed to discern the difference between coincidence and causality but analysis of the data will indicate how strong the relationship is. Hasty conclusions are harmful to any research work and should be for any interpretation to be useful (Morrow, 2005).
Data analysis takes into account matters of data collection, some of the questions asked answer the question of biases. It is because the data must be a representative of the whole population. Analysis of qualitative data is different from that of quantitative data. While quantitative data makes use of a statistical analysis tool, qualitative data will include constant comparisons with an aim to find consistency (Denzin & Lincoln, 2008).
A researcher might at some point in his research process be tempted to use his background knowledge in the interpretation of data. Background data may include but not limited to previous experiences from the research field. Experience gathered from data collected from a previous research similar to the one being conducted also considered background data. The use of such information may bring about questions of biases in the interpretation of data. (Denzin & Lincoln, 2008).
Quantitative and qualitative data analysis will lose its objectivity when data analysis is on the basis of previous experience. It is because although recorded activities might be similar their interpretation might vary depending on the people or situation that is being researched. Objectivity and non-bias should be by the use of analysis methods that pertain to the study in question. However, there might be research areas that may call for the use of previously gained experience in order to interpret gathered data. It may allow the researcher to choose suitable data sources, and the reliable conclusions are from qualitative data. Background knowledge should however not be used blindly as it is paramount that a proper understanding of the situation in question is present (Morrow, 2005).
Conclusion
Data analysis is a crucial process meant at coming up with conclusions from data gathered. Biases should be avoided at all costs as they hinder making of conclusions. The use of background data in the analysis of data might be questionable in some cases because of the diverse implications data may have. However, the qualitative analysis may make use of the researcher background information so as to come up with conclusive evidence.
References
Denzin, N. K., & Lincoln, Y. S. (Eds.). (2008). Collecting and interpreting qualitative materials (Vol. 3).Sage.
Morrow, S. L. (2005).Quality and trustworthiness in qualitative research in counseling psychology. Journal of counseling psychology, 52(2), 250. Instruction files Read More
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