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Data collection and analysis is one of the most important aspects of conducting research. High-quality data allows researchers to interpret findings accurately, act as a foundation for future studies, and give credibility to their research. As such, research often needs to go under the scanner to be free of suspicions of fraud and data falsification. At times, even unintentional errors in data could be viewed as research misconduct. Hence, data integrity is essential to protect your reputation and the reliability of your study.
Owing to the very nature of research and the sheer volume of data collected in large-scale studies, errors are bound to occur. One way to avoid “bad” or erroneous data is through data validation.
What is data validation?
Data validation is the process of examining the quality and accuracy of the collected data before processing and analysing it. It not only ensures the accuracy but also confirms the completeness of your data. However, data validation is time-consuming and can delay analysis significantly. So, is this step really important?
Importance of data validation
Data validation is important for several aspects of a well-conducted study:
- To ensure a robust dataset: The primary aim of data validation is to ensure an error-free dataset for further analysis. This is especially important if you or other researchers plan to use the dataset for future studies or to train machine learning models.
- To get a clearer picture of the data: Data validation also includes ‘cleaning-up’ of data, i.e., removing inputs that are incomplete, not standardized, or not within the range specified for your study. This process could also shed light on previously unknown patterns in the data and provide additional insights regarding the findings.
- To get accurate results: If your dataset has discrepancies, it will impact the final results and lead to inaccurate interpretations. Data validation can help identify errors, thus increasing the accuracy of your results.
- To mitigate the risk of forming incorrect hypotheses: Only those inferences and hypotheses that are backed by solid data are considered valid. Thus, data validation can help you form logical and reasonable speculations.
- To ensure the legitimacy of your findings: The integrity of your study is often determined by how reproducible it is. Data validation can enhance the reproducibility of your findings.
Data validation in research
Data validation is necessary for all types of research. For quantitative research, which utilizes measurable data points, the quality of data can be enhanced by selecting the correct methodology, avoiding biases in the study design, choosing an appropriate sample size and type, and conducting suitable statistical analyses.
In contrast, qualitative research, which includes surveys or behavioural studies, is prone to the use of incomplete and/or poor-quality data. This is because of the likelihood that the responses provided by survey participants are inaccurate and due to the subjective nature of observational studies. Thus, it is extremely important to validate data by incorporating a range of clear and objective questions in surveys, bullet-proofing multiple-choice questions, and setting standard parameters for data collection.
Importantly, for studies that utilize machine learning approaches or mathematical models, validating the data model is as important as validating the data inputs. Thus, for the generation of automated data validation protocols, one must rely on appropriate data structures, content, and file types to avoid errors due to automation.
Although data validation may seem like an unnecessary or time-consuming step, it is absolutely critical to validate the integrity of your study and is absolutely worth the effort. To learn more about how to validate data effectively, head over to Elsevier Author Services!