From the course: UX Research: Overcoming Data Anxiety

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Cleaning quantitative data

Cleaning quantitative data

From the course: UX Research: Overcoming Data Anxiety

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Cleaning quantitative data

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- We're at the point in this course where we're going to start getting to the technical nitty gritty of your analysis. One of the major challenges in most research projects is data quality. In the last video, we talked about cleaning qualitative data. Now we turn our attention to how to do this for quantitative data. Real talk. This process takes time. In fact, most teams spend 60 to 80% of total project time cleaning their data. However, you can't skip this step because it ensures that your results are accurate and reliable. Here are three steps to cleaning quant data. First up, data validation. This is a step where you remove duplicate data as it can skew your results. This is also the point when you determine what to do about missing data. Do you plan to remove full missing rows of the data set, use partial data, replace missing data with substitute values, or do you plan to predict missing values using…

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