How do Research Principles of Data Validity and Reliability tie into Data Collection in Project Management?

Data validity and reliability are integral to the integrity of scientific research. While data validity defines how the research measures what it was intended for, reliability refers to how stable and consistent research findings are. In project management, validation of data ensures that the data obtained are accurate, clean, and complete. The overarching principle behind ensuring data validity is to prevent data corruption and consequently protect the integrity of the research (Sohrabi et al., 2022). Data reliability ensures that the data collected is complete and accurate. Kyeong & Nam (2022) note that data reliability provides a crucial foundation for establishing trust. It provides a means for maintaining program integrity and ensures that the program is compliant with data security, quality, and regulatory standards. Evaluation, being crucial in scholarly projects, should not integrate data that does not meet validity and reliability standards. In this respect, data that do not have confirmed validity and reliability should not play a role in evaluation.

Data collection and management fetches considerable financial costs. Estimation of data collection costs and management can be done using data collection and management modeling (Dhudasia et al., 2021). This process starts by identifying the tasks to be performed, the timelines required to perform them, and the persons involved. Market surveys and environmental scanning are then done to determine the average cost of performing the identified tasks. Cost estimates are then arrived at through forecasting. An inflation margin can be given when determining the total costs. This is to cover any variations between the estimated cost and the actual cost.
Microcosting tools such as targeted questionnaires and on-site administrative databases can be used in project evaluation. These tools are effective in estimating the costs of evaluation processes (Chapel & Wang, 2019). They inform resource allocation decisions and give important insights into the cost of the interventions.

References

Chapel, J. M., & Wang, G. (2019). Understanding cost data collection tools to improve economic evaluations of health interventions. Stroke and Vascular Neurology, 4(4), 214–222. https://doi.org/10.1136/svn-2019-000301
Dhudasia, M. B., Grundmeier, R. W., & Mukhopadhyay, S. (2021). Essentials of data management: An overview. Pediatric Research, 93(1), 2–3. https://doi.org/10.1038/s41390-021-01389-7
Kyeong, N., & Nam, K. (2022). Mechanism design for data reliability improvement through network-based reasoning model. Expert Systems with Applications, 205, 117660. https://doi.org/10.1016/j.eswa.2022.117660