The process of detecting and improving corrupt or inaccurate information or records to increase your data quality is called data cleaning. It includes the screening of the data set prior to analyzing to remove unwanted data entries or erroneous data that can prove to derail the study. The software for cleaning can be chosen depending on the type of data analysis that a study requires.
Types of data cleaning in research
Data cleaning is the process of identifying and removing invalid data from a data set. Erroneous data can cause major problems in research. They might lead to faulty conclusions, incorrect or misleading research findings, and even be impossible to remove. Other types of data entry errors include: –
- Incorrect data type
- Incorrect data value
- Incorrect data format
- Incorrect data entry procedure
- Improper data management
- Lack of attention to detail
- Poor communication
- Lack of effective feedback
- Unwanted data
- Over-interpreting observed data
- Improper allocation of resources.
Identifying and removing invalid data
When data cleaning is done well, it is possible to identify and remove invalid data entries from a data set. This includes the following: –
- Identifying and removing erroneous data
- Improving the quality of existing data
- Scoring data for reliability and validity
- Bias and gleaning from unanticipated events
Improving the quality of existing data
When data is not cleanly created or gathered, it is sometimes possible to improve the quality of existing data. The software for this can be chosen depending on the type of data analysis that a study requires. Any mix of the following steps can be used to improve the quality of existing data: – Identify all missing values. – Remove all zero values. – Calculate the average and standard deviation (aka the mean) for each value. – Divide the values into groups that are comparable. – Split a data set into groups with similar distributions and/or distributions that are not normally distributed. – Create and label key values. – Create legend boxes for each group. – Apply Statistics to distinguish data types. – Choose a final statistical technique based on the type of data. – Create a transformation matrix. – Apply transformation to clean data. – Generate new data. – Clean the new data. – Apply transformation to the cleaned data. – Apply transformation to the new data to create a new set with the same distribution as the original set. – Repeat the process for each missing value and each other item that is not normally distributed.
Scoring data for reliability and validity
When determining how to score a data set, you must take into account both the data quality and the application. For example, if you are scoring a set of blood pressure readings, it is important to note that each number reported is actually lower than average. However, the study also states that most users report having a reading of 100 or above on a regular basis. This means that the data set is actually able to provide a calculated value of only 91.5 or higher. The scoring method must take these differences into consideration when determining the actual value.
Bias and gleaning from unanticipated events
When processing data, it is important to maintain a high level of accuracy and thoroughness. However, occasional inaccuracies or even unintentional leniencies may occur in the data collection process. These may be referred to as “bias” events. The ideal is to find ways to reduce the likelihood of these kinds of events occurring in the first place by using appropriate procedures and tools. To reduce the likelihood of bias, you can: –
- Use consistent data collection procedures
- Limit the number of individuals involved in data collection
- Use reliable and valid instruments for measuring variables
- Follow standard formatting and data entry procedures
- Follow standard labeling and disposal procedures
- Follow good documentation and analysis processes
- Follow up with a thorough data check to determine the cause of errors
Why is data cleaning important?
It is important to keep in mind that data cleaning is a routine process that happens after data collection. Data cleaning is not an “add on” function that is added to the data analysis workflow. Data cleaning is a prerequisite to the proper and accurate analysis of data. This includes the following: –
- Ensuring the data is fully compliant with all relevant regulations and policies
- Ensuring that the data is properly collected and deposited
- Ensuring that the data collection and analysis process is fair and balanced
- Ensuring that the data is properly transformed and cleansed
- Ensuring the new data collected is not starkly different from the earlier findings
- Ensuring that the new data meets the requirements of the model