Why not capture weather data, too?
According to a significant string of research, winter weather (or lack of sunshine) and extreme weather can increase mental health problems like social anxiety. It can also bring out stronger feelings of empathy for others. Violent crime rises with heat, and warm climates are protective against death. Allergies, often seasonal, have a significant impact on health. Think about those weather conditions.
As social scientists, these phenomena — mental health, social anxiety, moods, empathy, crime, and health — are all generally important variables we explore, and often control for, in our studies.
In a recent study completed in France, Guéguen and Jacob found that on sunny days, respondents cooperated with a survey request coming from an interviewer at about a 10% higher rate than when asked to do so on a cloudy day. Did their levels of mental well-being, combined with or caused by the weather, affect this rate?
So I wonder…
Why do we NOT routinely capture data about respondents’ local weather conditions – current temperatures and cloud cover, recent or upcoming extreme weather, regional pollen counts, and other related data – while we collect survey data? Or if not during the survey, why do we not merge in such data from an external source before analysis?
It would be easy to do. Ask the respondent for their zip code, and capture the date and time they are taking the survey. With this (or similar geographic location data) there are numerous sources for data on weather conditions available (Weather Underground, National Weather Service, AccuWeather).
Data linking too much? How about a simple question:
Q. Which of the following best describes how sunny or cloudy it is at your location?
- Sunny
- Partly sunny/cloudy
- Cloudy
- It is nighttime
Has anyone done this? If so, did it help the science?
What’s a Research Wonder? Read this to find out…