Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values. The best techniques to handle missing data are: Use Deletion Methods to Eliminate Missing Data The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets. Use Regression Analysis to Systematically Eliminate Data Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset. Use Data Imputation Techniques Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant. Conclusion The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances.
Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values. The best techniques to handle missing data are: Use Deletion Methods to Eliminate Missing Data The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets. Use Regression Analysis to Systematically Eliminate Data Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset. Use Data Imputation Techniques Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant. Conclusion The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances.
Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values.
The best techniques to handle missing data are:
Use Deletion Methods to Eliminate Missing Data
The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets.
Use Regression Analysis to Systematically Eliminate Data
Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset.
Use Data Imputation Techniques
Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant.
Conclusion
The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances.
