While doing secondary data analysis, the analyst often makes several mistakes without even noticing. It is because, in secondary analysis, you rely on data from other resources. So there is a huge chance that you incorrectly use these resources in your analysis. Because of this, there is a risk that your analysis could go to waste as your solution will not be reliable. Therefore, it is vital to know the most common mistakes that could happen in secondary data analysis. This article will show eight common mistakes analysts make while doing secondary data analysis.
Secondary Data Analysis:
In this type of analysis, you use the data from existing studies and research to get answers to a research question. You collect secondary data, which can be data from large-scale survey research or data from your own personal research. You might face some ethical issues while using secondary data for your analysis. Therefore, you should use proper citations and references in your secondary data analysis. With the advent of new technologies, secondary analysis has become very easy as these technologies give an easy way to compile, store and share data. But there are also some concerns regarding the safety and security of the data.
Using Only A Single Source Of Data:
Using only one source of data for secondary analysis is a common mistake that every student makes. It will not give you the relevant and reliable outcomes you need to make your research credible. Choosing multiple sources for your data can help you find different perspectives on a topic. This will improve your analysis and give you meaningful insights. On the other hand, if you rely on a single source, it will look like you have just made a summary of that source. If you are unable to find multiple resources, you can get dissertation help online from qualified experts.
Using Unreliable Data:
The quality of secondary data analysis depends on the data you will collect. While collecting data from a source, students often do not get time to check the accuracy of the data. In that case, there is a high chance that you can collect unreliable data. Many studies have incomplete data, weak results and invalid assumptions. Make sure to check all these things before using data from a source. Before collecting data, invest some time to read the whole research.
Comparing Results With Inappropriate Benchmarks:
A common mistake related to secondary data analysis is that researchers compare their results with inappropriate benchmarks. While comparing, you should ensure that your comparison can be from different periods. An incorrect benchmark could hide the impacts you are trying to discover. This way, you can get poor results in your analysis, decreasing the value of your findings.
Using Stats And Trends Without Any Context:
Producing tables and charts without proper context is not a proper analysis. You have to use a lot of facts and figures in a secondary analysis. It is important to note that there is a story behind every piece of data. There could be significant events or information behind any stats and figures you need to mention in your analysis. Sometimes a little bit of information can change the entire context of your analysis. Therefore, it is very important how you use your data in the secondary analysis.
Using Average Values:
Average values can be very easy to use, but there are some drawbacks. Many analysts do not think using average values is the best way of doing secondary analysis. A good secondary data analysis relies on accurate and precise data. If you are using mean average, then it is best to use grouping values, median, or other important values with it. This will give you more accurate results for your analysis. However, if you still have any confusion, hire a dissertation writing service to overcome it.
Readers are highly attracted by the sharp falls and rises in data or charts. It is because they are impactful to show that you have found something significant in your analysis. Therefore, they are very keen to understand these sharp rises or falls. You need to explain these significant changes with good commentary in your analysis. It could be because of a specific event in your data points or changes in methodology calculation. You need to explain the story behind the significant changes in your so that your reader can understand your analysis better.
Failed To Deliver A Compelling Final Message:
For your secondary data analysis, the final message is very important. Your final message can be useful for your readers in many ways. They could apply this message in their businesses, studies or research. That is why; many readers only read the results of an analysis. In contrast, many analysts do not focus on delivering a compelling final message. They usually use complex descriptions, graphs, charts, tables, and calculations. It is because using a complex range of trends and stats is the way to hide technical loopholes in the results. By doing this, readers cannot find the information in the results they are looking for. You should deliver a simple message in your results without using complex stats and figures. This can help you show useful information to your readers.
Secondary data analysis is usually susceptible to bias. It is because it is usually driven by the analyst’s experience, perspectives and knowledge. To avoid this mistake in your analysis, you need to properly observe your own personal opinions and views before writing your research question. Avoid using your own views on the issues; try to the information from other resources first.
After going through the mistakes listed in this article, you can write a healthy secondary data analysis without being afraid that you have errors in the analysis. Ensure you use multiple accurate resources to get quality information and stats for your analysis. It is also important to give the proper context to every stat and trend you use. In a secondary analysis, you should focus on secondary resources avoiding personal bias as much as possible. Lastly, your final message in your analysis should be simple and useful for your readers.
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