I recently completed a survey regarding Gophers After Dark, a program at the University of Minnesota that puts on student event throughout the school year. This survey was posted by one of my peers on Facebook, asking students at the University to please compete it for her as well as share it among their own friends at the University.
As a student currently active in a research course, I felt an obligation towards this girl to help her with her research collection. I shared the survey with my friends. In this way, I participated in snowball sampling. We learned about this method in research class. It involves using members of a network to introduce you to other members of the network.
So you see, I am a student at the University of Minnesota who introduced the survey to other students at the University of Minnesota in order to give my peer a larger sample for her data collection.
In addition to helping her collect more data for her survey by sharing it, I participated in it myself. While taking the survey, I was sure to pay attention to some of the details and strategies that often go into developing a successful survey.
I noticed the use of semantic scales. These scales contain opposite terms and the participant must determine their position on this scale. The example of a semantic scale below uses the terms "not likely" and "likely" to determine how likely it is for the study participant to attend a University of Minnesota event if it is not advertised as a Gopher's After Dark event.
The survey continued with similar methods, asking questions pertaining to the participants' personal experience in attending Gopher's After Dark events and their own feelings towards them.