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2022, Data Science Foundation
For Analytics teams, working with Secondary Data is becoming increasingly common. In an ideal world, this would involve receiving structured/unstructured data and managing the analytics activities from that point onwards without having to worry about validity, accuracy and origins of the data received. Unfortunately, the world is far from ideal so prior to commencement of the data analytics activities, it is essential to ensure that Secondary Data (SD) at our disposal is fit for the purpose we require it for and not been tortured already. This article has been written to provide a brief overview of the SD validation issues as well as strategies for addressing those issues.
Technological advances have led to vast amounts of data that has been collected, compiled, and archived, and that is now easily accessible for research. As a result, utilizing existing data for research is becoming more prevalent, and therefore secondary data analysis. While secondary analysis is flexible and can be utilized in several ways, it is also an empirical exercise and a systematic method with procedural and evaluative steps, just as in collecting and evaluating primary data. This paper asserts that secondary data analysis is a viable method to utilize in the process of inquiry when a systematic procedure is followed and presents an illustrative research application utilizing secondary data analysis in library and information science research.
Singaporean Journal of Business Economics and Management Studies
Accessing Secondary Data : A Literature Review2018 •
2022 •
There is a misconception in the research community that the use of secondary data does not entail legal considerations. Secondary data is an intellectual property of the data owner, so any use of that data may require the data owner's permission or may be subject to certain terms and conditions. Therefore, this online workshop is specifically designed to highlight these issues and help you overcome these. This document includes presentation slides from the Day 1 of the event.
Data Science Foundation: White Paper Series
Data Analytics Integrity: Challenges to Implementation of the Automated Data Collection Processes2020 •
In recent months, my company (Baron Consulting) has been proactively involved in setting up Data Collection Systems for a range of Private and Public Organisations we are servicing. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: Principles and Challenges White Paper. While the RDC (Raw Data Collection) paper analysed the current state of the everchanging Data Collection requirements, it did not have the scope to address technicalities of the RDC processes along with the specific Data Collection tools and methods. The purpose of this paper is to fill the void by looking into implementation of the automated data collection processes.
Revista Ibero-Americana de Estratégia
Secondary Data in Research – Uses and OpportunitiesJournal of Physics: Conference Series
Construct Validation in Secondary Data: A Guideline for Medical Data MiningFrontiers in Environmental Science
Challenges in the Evaluation of Observational Data Trustworthiness From a Data Producers Viewpoint (FAIR+)Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Ta...
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