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To have a better understanding of how secondary analysis works in research, one must have a clear understanding of what secondary analysis is. Secondary analysis is “the reanalysis of data collected by another researcher or organization, including the analysis of data sets collated from a variety of sources to create time-series or area-based data sets” (Shi, 2008, p. 461). The article that I have found in the Keiser Online Library regarding secondary analysis as it relates to the ethical, logistical, and cost of true data needed for research purposes to compare or process new data as other questions are needing answers and the data can either be qualitative or quantitative (Ruth, 2018). While secondary data can be cost effective in terms of starting from scratch there are several other advantages such as reanalyzing the data to come up with other variables to answer other important questions and the fact that secondary data is non-reactive and subjects will act more naturally as opposed to initially starting the process. Another plus is that secondary data analysis can be used in conjunction with other data to reach a conclusion or it can be used by itself (Ruth, 2018). Just like all things secondary data analysis in research has its disadvantages such as the researcher having no control over how the data needed is to be collected as they would in the initial process, the quality of the data may not be efficient enough to conduct a secondary analysis, and not knowing if the data collected is really random or if it is biased (Ruth, 2018). Meta-analysis is another type of secondary data that is used to synthesize the results of existing multiple qualitative research studies, so “even when the results of a study show statistical significance, the hypothesis still may not be true” (Ruth, 2018). Which is why it is a good idea to use both secondary analysis and combine it with other primary analysis data to get the desired outcome.
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