To improve the quality of your data, you need to find and fix any mistakes or inconsistencies in your data. This means you have to clean your data. An error is any value (like the weight recorded) that doesn’t match the real value (like the actual weight) of what is being measured.
During this step, you review, analyze, find, change, or get rid of “dirty” data to get a “clean” dataset. Cleaning or scrubbing data are other names for data cleansing.
Why is it important to clean up data?
In quantitative research, you collect data and use statistical analysis to answer a research question. With hypothesis testing, you find out if your research predictions are supported by your data.
When data aren’t cleaned or calibrated correctly, it can lead to several types of bias in research, especially information bias and omitted variable bias.
Most of the time, mistakes are unavoidable, but cleaning your data can help you make less of them. If you don’t fix or get rid of these mistakes, you might come to a wrong or invalid conclusion about your study.
If your data is wrong or not true, you could make a Type I or Type II error in your conclusion. These kinds of wrong conclusions can be important in the real world because they can lead to bad investments or missed opportunities.
Clean and dirty data.
There are mistakes and inconsistencies in dirty data. These data can come from any part of the research process, such as a bad plan for the research, using the wrong tools to measure, or making mistakes when entering the data.
Clean data meet some quality standards, while dirty data are wrong in one or more ways.
Valid data.
Valid data meet certain standards for certain kinds of information (e.g., whole numbers, text, dates). Data that aren’t valid don’t match up with the accepted values for that observation.
Your data analysis procedures might not make sense if you don’t have valid data. Before you analyse your data, it’s best to use data validation techniques to make sure they are in the right format.
Accurate data.
In terms of measurements, accuracy is how close the value you see is to the real value. The form of an observation is what makes it valid, while the content of an observation is what makes it accurate.
Consistent data.
Clean data are consistent across a dataset. For each person in your sample, the data for each variable should fit together in a way that makes sense.
Unique data.
During data collection, you might record the same participant’s information twice by accident.
During data cleansing, it’s important to check your data for duplicate entries and get rid of any that you find. Otherwise, your data might be skewed.
Uniform data.
The same units of measure are used to report the same kinds of data. If the data aren’t all in the same units, they need to be changed to a standard measure.