How Can You Handle Missing Values in a Dataset Using SPSS Help?
Introduction
SPSS is a trialware tool which first records the data and then analyses it. SPSS help is made up for complex data structures and missing values. It is used by market researchers, surveyors, analysts, statisticians, etc. to analyse their data. Due to its easy-to-understand graphical and syntactical interface, it has gained popularity and trust amongst users.
The problem of missing value is quite common in many real-life databases where it can bias the results of the machine learning models or may reduce the accuracy of the model. Hence, it is essential for the researchers to reduce the number of missing values and develop an effective model for further in-depth qualitative research analysis and evaluation. Missing data is mainly defined as the values or the data that is not sorted for some variables in the given data set. In the database, missing values can be represented as blank rows and for example, in Pandas, missing values are represented by NaN.
Missing values & their solutions in SPSS help
There are different reasons for such missing values in the data set. Past data might get corrupted due to improper maintenance for which missing data may exist in the data set and additionally, sometimes the users have not provided the values intentionally. The observations are not recorded sometimes for certain fields due to some reasons and there might be a failure in recording the values due to human error. Hence, due to negligence in putting and sorting the data as well as human error, there might be missing values in the database.
In the real-world database, there are lots of missing values, and the major causes of issuing values are data corruption and failure of recording the data. Handling the missing values is important during the pre-processing of the dataset as many of the machine learning algorithms do not support the missing values. For in-depth statistics data analysis and evaluation, it is important to tackle the missing values for ensuring misleading data and reducing biases in data analysis. The major strategies to tackle the missing values are described further,
Deleting the rows with missing values:
Missing values can be handled by deleting the rows or columns having null values. A null value can be ignored and deleted for further qualitative data analysis and evaluation. A model trained with the removal of all the missing values can create a robust model. However, loss of a lot of information and working poorly if the percentage of missing values that is excessing in comparison to the complete database is the major consequence of deleting the rows.
Imputing missing values for continuous variables:
Columns in the data set having continuous numeric values can be replaced with the mean, median and model of the remaining values in the column. It is also a suitable way to handle the missing values in a large data set and it is considered the best statistical approach for handling missing data. It prevents data loss which results in depleting the rows or columns as well as it also works well with a small database an easy to implement. However, this method can be used only in the numerical continuous variables and may cause data leakage.
Imputing the missing value for categorical variables:
While performing quantitative research in case of a missing value in a categorical column, it is replaced with the most frequent category. It prevents data loss and works well with a small database by adding unique categories in the data set. The consequences are that it is working only with the categorical values and the addition of new features to the model with coding may result in poor performance and baseness.
Applying imputation method:
There is another imputation method depending on the nature of the data and data type to handle the missing values in the data set. For example, the last observation carried forward (LOCF) method is being utilised for using the last valid observation as a data value.
Using algorithms supporting missing values:
Machine learning algorithms do not support the missing values. The k-NN algorithm is highly utilised that can ignore the column from a distance measure when a value is missing. The learn implementations of naive Bayes and k-Nearest Neighbours in Python also do not prefer missing value and thus it adapts qualitative data in statistics taking into consideration of high variance or bias, practising better results. ML algorithm is effective to handle the data set efficiently without handling the missing values in the data set.
Predicting missing values:
Predicting the missing values is important. In this regard, the regression or classification model is effective for predicting the missing values depending on the feature's nature. It gives better results along with covariance between the missing values.
Imputation by deep learning library, Data wig:
This is an effective method with categorical, continuous and non-numerical features. Data wig is mainly a library that learns ML models using a Deep Neural Network to impute the missing values in the datagram. It supports the CPUs and GPUs and the researchers utilise this widely or handle the missing values.
Conclusion
Hence, all the above-mentioned methods are beneficial for handling missing values as per the data type. For conducting a qualitative SPSS data analysis to progress your research to the next level, it is crucial to remove or replace the missing values with the correct measurements. SPSS can be the best and smart choice for this purpose.
