Data Collection

Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system. Accurate collection enables one to answer relevant questions and evaluate outcomes. Regardless of the field of or preference for defining data (quantitative or qualitative). Accurate data is essential to maintain research integrity. The selection of appropriate collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood of errors.

There is a necessity for a clearly defined communication structure as a precondition for establishing monitoring systems. Uncertainty about the flow of information is not recommended as a poorly organized communication structure leads to lax monitoring. This will surely limit the opportunities for detecting errors. Quality control is also responsible for the identification of actions necessary for correcting faulty collection practices and minimizing such future occurrences. A team is more likely to not realize the necessity to perform these actions if their procedures are written vaguely. These procedures should be based on feedback or education for accurate and timely results.

Methods:

Our data collection podcast walks you through methods, tips, and techniques for collection and crunching small to large volumes of data. We also talk about the challenges of collecting and presenting high-speed, high-volume data in real time. Deciding what data to collect will change over time and the podcast speaks to how to not get cornered when processing collected data.

Retention:

We also spend time talking on data retention. There are rules and regulations for retention and that could become a problem when collecting massive amounts of data. This topic has infinite possibilities so if you have questions after listening, we have done our job. Processes and procedures for collecting data will evolve organically. You should expect presentation methods to change as well to meet future needs of the organization.

Data Collection
Data Collection

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