Common Data Analysis Mistakes You Ought to Avoid


Truth be told, it is the little things you do that will always matter in life. Things are not that different when it comes to the world of data analysis. If at all you fail to keep a close eye on the smallest details, then you might find yourself making do with bigger problems.

To prevent this from happening, you ought to be fully aware of the common data analysis mistakes organizations and how to overcome them. Of course, this can only be achieved if you can be able to tell what is data analysis. Here are top two data analysis mistakes that most researchers makes.

Failing to Prepare Data Well

With data analysis, it doesn’t matter how good your skills are, or the type of software you choose to settle on. If you fail to check and clean the data at your disposal, then it would be good as useless. For qualitative analysis, you need to start by examining the accuracy of all data carefully. This is mostly the case when you chose to outsource your transcription.

Things tend to be slightly different with quantitative data since you have to screen and clean the data before doing anything else. Even though it might seem like you’re wasting your precious time, you’ll never make do with errors later on.

Losing Track of the Research Question

When conducting a data analysis you need to be wary of the questions you’re trying to answer. While this might seem obvious, it’s amazing how many researchers lose track of this. Since the data collection stage might at times prove to be overwhelming, it is quite commonfor researchers to lose sight of the original question they were trying to answer.

To avoid finding yourself in this situation, you ought to revisit your research question (s) before starting your data analysis. This action goes a long way in making sure you have an easy ride with your data analysis. In fact, it would be better for you to keep your research question displayed in a place you can easily see.

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