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Research Data Management: Validate Data
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What is Data Validation?
Data validation is important for ensuring regular monitoring of your data and assuring all stakeholders that your data is of a high quality that reliably meets research integrity standards — and also a crucial aspect of Yale's Research Data and Materials Policy, which states "The University deems appropriate stewardship of research data as fundamental to both high-quality research and academic integrity and therefore seeks to attain the highest standards in the generation, management, retention, preservation, curation, and sharing of research data."
Data Validation Methods
Basic methods to ensure data quality — all researchers should follow these practices :
- Be consistent and follow other data management best practices, such as data organization and documentation
- Document any data inconsistencies you encounter
- Check all datasets for duplicates and errors
- Use data validation tools (such as those in Excel and other software) where possible
Advanced methods to ensure data quality — the following methods may be useful in more computationally-focused research :
- Establish processes to routinely inspect small subsets of your data
- Perform statistical validation using software and/or programming languages
- Use data validation applications at point of deposit in a data repository
Additional Resources for Data Validation
Data validation and quality assurance is often discipline-specific, and expectations and standards may vary. To learn more about data validation and data quality assurance, consider the information from the following U.S. government entities producing large amounts of public data:
- U.S. Census Bureau Information Quality Guidelines
- U.S. Geological Survey Data-Quality Management
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- Research Process
Why is data validation important in research?
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Table of Contents
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 !
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Research and Writing: Validation and Reliability Tests
Research findings must pass validation and reliability tests before they are accepted. Researchers use qualitative, quantitative, and mixed methods to carry out their studies. In all the methods, researchers will collect data, analyze it and interpret it. For a research interpretation to be credible, it must be transferrable to other contexts and settings. Thus, researchers have to inform their target audience of the context of their research to facilitate transferability. Qualitative research measures an ever-changing context. Thus, the researcher has to explain how any change in the research environment affects the research results. With every new research, some new facts appear. It is the task of the researcher to present the findings’ ways of corroboration with other studies (Trochim, 2006b).
Essentially the measurement of how research is reliable has to do with consistency and repeatability. Different research methodologies present challenges for measuring reliability. Therefore, reliability is only estimated. To confirm the validity of their studies, researchers use a variety of methodologies. First, researchers use triangulation to corroborate their findings and ensure that the results are as generalizable as possible. With triangulation, a researcher concurrently uses different methodologies such as qualitative and quantitative for complimentary purposes (Trochim, 2006a).
Barkema, Baum, and Mannix (2002) conducted qualitative research on research developments taking place in the 21st century. The researchers collected their data from existing research findings. The research methodology employed in this research is qualitative. The inquiry is ontological because it refers to an existing generalization about the research topic. The researchers use an explanatory approach to establish their findings. The research establishes themes emerging from a literature review of previous research to come up with an interpretation of the trends that emerge. Similarly, the researchers use the findings to explain the implications to the management research. Thus, the research is purely ontological.
Scandura and Williams (2000) conducted quantitative research to compare management research in two periods. The authors read and coded methods used for analysis in the research reports that they studied. Then the researchers grouped similar techniques. Lastly, they conducted data analysis on the categories. Furthermore, the researchers also tested their results to measure their reliability. They conducted a difference in proportions test and came up with heterogeneity indexes. They presented their findings as a triangulation using different variables. In addition, they validated their data for internal, external, construct, and statistical purposes. Using the quantitative methodology, the researchers interpreted their data to approve their hypothesis. Therefore, this research is epistemological.
Barkema, Baum, and Mannix (2002) validate their study by describing the context to make it generalizable. They also use other research findings to corroborate their findings. The thorough description of the context serves to make the research dependable. The use of other researches to explain the context of the research and validate the findings makes the research transferable to other similar contexts. The researchers explain the current setting of management research using globalization, which makes the research findings applicable to current realities. This study uses a casual comparative design and thus makes a credible extrapolation.
On the other hand, Scandura and Williams (2000) triangulate their findings and evaluate validity on different aspects using a quantitative formula. The use of quantitative formulas makes the research repeatable in another context. The researchers also present their quantitative methodology used to obtain their findings to validate the research externally. The study settings are other researches done in two different periods. Therefore, they represent knowledge available on the subject of research in the given periods. Lastly, this study is experimental. Its epistemological approach serves to validate its findings internally.
Barkema, H. G., Baum, J. A., & Mannix, E. A. (2002). Management challenges in a new time. Academy of Management Journal, 45 (5), 916-930.
Scandura, T. A., & Williams, E. A. (2000). Research methodology in management: current practices, trends and implications for future reseach. Academy of Management Journall, 43 (6), 1248-1264.
Trochim, W. M. (2006a). Introduction to validity . Web.
Trochim, W. M. (2006b). Reliability & validity . Web.
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StudyCorgi. (2023, November 11). Research and Writing: Validation and Reliability Tests. Retrieved from https://studycorgi.com/research-and-writing-validation-and-reliability-tests/
StudyCorgi. (2023, November 11). Research and Writing: Validation and Reliability Tests. https://studycorgi.com/research-and-writing-validation-and-reliability-tests/
"Research and Writing: Validation and Reliability Tests." StudyCorgi , 11 Nov. 2023, studycorgi.com/research-and-writing-validation-and-reliability-tests/.
1. StudyCorgi . "Research and Writing: Validation and Reliability Tests." November 11, 2023. https://studycorgi.com/research-and-writing-validation-and-reliability-tests/.
StudyCorgi . "Research and Writing: Validation and Reliability Tests." November 11, 2023. https://studycorgi.com/research-and-writing-validation-and-reliability-tests/.
StudyCorgi . 2023. "Research and Writing: Validation and Reliability Tests." November 11, 2023. https://studycorgi.com/research-and-writing-validation-and-reliability-tests/.
StudyCorgi . (2023) 'Research and Writing: Validation and Reliability Tests'. 11 November.
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