Data collection

Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed. Regardless of the field of or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintain research integrity. The selection of appropriate data collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood of errors. scientific method Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling. A formal data collection process is necessary as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid data. The process provides both a baseline from which to measure and in certain cases an indication of what to improve. Data collection system Data management platform Data management platforms (DMP) are centralized storage and analytical systems for data, mainly used in marketing. DMPs exist to compile and transform large amounts of demand and supply data into discernible information. Marketers may want to receive and utilize first, second and third-party data. DMPs enable this, because they are the aggregate system of DSPs (demand side platform) and SSPs (supply side platform). DMPs are integral for optimizing and future advertising campaigns. The main reason for maintaining data integrity is to support the observation of errors in the data collection process.

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Identification of typical district configurations: A two-step global sensitivity analysis framework

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Identification of typical district configurations: A two-step global sensitivity analysis framework