Data validation

Data validation

Let’s assume we have a dataset that keeps a record of the number of hours worked by employees during the different days of the week. Normally, an employee can work a maximum of 8 hours a day, except if there is an overtime. When recording the data, there is always a challenge that there might be those values filled in the data that exceed eight or there could be less than zero  The spreadsheet has no way of confirming that the data entered ranges between that limit. In this case, a data analyst needs to define methods where they can ensure that the values in the data analysis file are within the acceptable range.  Such methods are known as data validation methods. We shall explore more about data using SPSS in this article.

What is data validation?

Data validation refers to the various methods applied to a dataset to ensure its validity. Often, it’s applied before the data analysis process. Preferably during the data cleaning.

The accuracy of the data analysis processes is very important. But the accuracy is not in the method of data analysis alone or in the end result. It is also affected by the data itself. That is why a data analyst must ensure the validity of the data.

Something which is worth remembering at all times is that data validation does not ensure that the data entered is correct. It only ensures that what is entered is only rational and acceptable. Simply put, validation is a technique that tries to lessen the errors in the data analysis process.

The methods of data validation

In this section, we shall refer to the analogy given above to clarify some issues here.

Range checking. This method is applied to numerical data. And as the name suggests, it validates if the data is within the acceptable limit. For our case, does the working hour range from 0 to 8.

Type checking. This is rather different from the one listed above. It checks to ensure that the correct data type has been input. Let’s take the above example where we are dealing with numerical values. Won’t it be absurd if there is a row filled with letters? That is the error this method seeks to eradicate.

Length checking. This is a method used to limit the length of the characters entered.  It can be used for both numeric and non-numeric data. The importance of this method is that it helps reduce those errors caused as a result of faulty entry of the data pertaining to the length of the data string.  For our case, this data validation method will ensure only a single character is entered. A perfect example of where it’s applied is when filling passwords. Your password length should not exceed or drop below a certain limit.

Format checking. This ensures that the input has the right format.

Presence checking. This form of data validation ensures that the important information is filled in compulsorily. In this method, if anyone attempts to leave the essential field blank, it generates an error.

What are the advantages of data validation?

Data is structured to meet the organization’s requirements. Data validation will help verify if the data collected has met the business requirements.

Accuracy in the data. The main goal of data validation is to remove the possible errors in the data, which ensures it’s accuracy leading to improved decision making in the long run.

Continued expansion. If the data used in decision making is accurate, it will enhance the process of decision making. The net effect is a turnover in profits and, consequently, the expansion of the business.

Data validation in SPSS

Data validation can be carried out in SPSS. However, this is not as straightforward as one might think. It requires you to know the general syntax of SPSS programming prior to using it.

Challenges in data validation

There is one major challenge that data analysts face, especially in the process of data validation of a database. The process is time-consuming and cumbersome. In the case of sampling, things are made easier.

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