DATA ANALYSIS PLATFORM
In the analysis of epidemiological data, researchers often look for correlation among two or more variables. For instance, when the values of air pollution rise, human attention decreases. However, it is more complex than that! Researchers know very well that different factors, other than the two variables under study, can influence a correlation. For instance, our attention might decrease because we slept badly the previous night. Thus, the analysis of data must include these influencing factors in order to ensure that the correlations we observe are not influenced by those factors. But, what are relevant factors that should be included in the analysis of correlations?
Involving citizens in this phase allows researchers to include new viewpoints on the analysis, taking advantage of the situated knowledge of affected citizens who can provide their first-hand experience of the problem and shed light on new scenarios.
This tool allows you to foster citizens’ debates around different aspects of data analysis. Specifically, through this tool you will be able to identify the variables to be included in the correlation data analysis, according to the experience and perspective of citizens.
Read the case study and understand how this tool has been used in a real citizen science project.
CitieS-Health Barcelona Pilot
The tool was used during a workshop attended by citizens who took part in the data collection of the CitieS-Health Barcelona pilot. Before the workshop, citizens received a report with a rough analysis of their data. The data were about individual exposures to air and noise pollution, as well as their daily responses to the survey that collect information about sleep quality, attention, stress and daily habits. The goal of the workshop was 1) to discuss the type of data analysis researchers planned to perform, 2) to provide an overview of all variables created from the data collected, 3) to identify with participants the variables they think can play an important influence in the correlation investigated and 4) to raise other research questions that could enrich the data analysis.
To identify confounding variables to be included in correlation analysis.
To identify other research questions that could help understand and enrich the analysis of the data collected.
To leverage the first-hand experience of citizens of the problem at stake.