secondary data are data that were collected by another person or party for a different purpose. Therefore, some important information for your research question may not have been collected. You may also encounter imbalances in subject distribution or incomplete data that could present within the analysis or the interpretation of results from secondary data research. The study population that provided the original data may not always represent the overall population of your study’s target population. The demographics of the study population could be the intended result of original study design or unintended results of respondents’ preference. For example, the proportion of female participants in the overall population may be higher or lower than its proportion in the study population.
In secondary data analysis, you cannot solve these issues by changing the original study design or returning to the study participants. Instead, you need to manage these issues as part of your own study design and within the analysis of the secondary data. The techniques of handling these problems include weighting, imputing the missing data, and dropping records. To apply these techniques, you need to assess if sample weighting is feasible for your study design (Boslaugh, 2007).
Post an explanation about the importance of weighting in secondary data. Then, provide two examples of how you might use weighting and explain why. Support your response with literature