Data that has been already made available is known as secondary data. Two ways could enhance its validation. The first of which is cross-checking with multiple sources. The second is conducting field validation. The following studies could act as exemplars for detailed insights.

Cao, Y., Carrillo, A. S., Jankowska, M. M., & Shi, Y. (2020). Validation of secondary data sources for enumerating marijuana dispensaries in a state commercializing marijuana. Drug and Alcohol Dependence, 215, 108183. https://doi.org/10.1016/j.drugalcdep.2020.108183

Clary, C. M., & Kestens, Y. (2013). Field validation of secondary data sources: A novel measure of representativity applied to a Canadian food outlet database. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 77. https://doi.org/10.1186/1479-5868-10-77

Emanuelson, U., & Egenvall, A. (2014). The data—Sources and validation. Preventive Veterinary Medicine, 113(3), 298-303. https://doi.org/10.1016/j.prevetmed.2013.10.002

Good luck,

Dear Jiban K. Pal,

There are various tests to confirm whether the study has valuable data to investigate a problem and to support hypothesis testing and achieve research objectives.

The following links give you more insight into your query.

https://www.xenonstack.com/insights/what-is-data-validation-testing/

https://stats.idre.ucla.edu/spss/faq/what-does-cronbachs-alpha-mean/

https://data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/

Hope the above will help you. Good luck with your studies.

Regards,

S. Senthilnathan.

In simple words, data validation is a part of Database testing, in which individual checks that the entered data valid or not according to the provided business conditions.

Dear All,

Thanks for your thoughtful answers.

I am really curious to know about the validation of bibliographic data collected for the quantitative studies. Most of the bibliometric or scientometric studies are used to collect datasets from citation databases (like Scopus, Web of Science), which needs validation with rationality for better insights.

Certainly, cross-checking with multiple sources (as replied by Dr. Mohialdeen Alotumi) may be a possible way, though not practical for bibliographic datasets. While selecting the data for a particular objective, there must have a rational justification. I am not interested to learn about the hypothesis testings.

Only, I want to learn about the validation of collected (bibliographical) data for quantitative studies (often used in evaluative scientometrics)...

The experimental dataset should be validated against a similar work; the method and the model of the numerical or mathematical work should compare with experimental work.

In simple words, data validation is a part of Database testing, in which individual checks that the entered data valid or not according to the provided business conditions.

The validation of a dataset in quantitative studies varies with the purpose and objectives; but it should be done empirically for testing of hypothesis. Otherwise, depends on the typos of datasets you're using rather it can be done by simple cross-checking to database-driven, or numerical methods to inferential statistics as well. So the validation of data is subject to an approval of the researcher that falls within the acceptable range of values within the field.

In quantitative studies it can also be done by definig the context and also by expressing on how accurately the study answers the questions and hypotheses it was commissioned to answer combining sample-size, sample-type, exploratory factors and reliability analysis (avoiding bias).