Sampling, Channels, and Contact Strategies in Internet Survey

Sampling, Channels, and Contact Strategies in Internet Survey

Ester Macrì (University of Florence, Italy) and Cristiano Tessitore (National Statistical Institute of Italy, Italy)
Copyright: © 2013 |Pages: 14
DOI: 10.4018/978-1-4666-3918-8.ch003
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The global diffusion of the Internet involves economic, political, cultural, and geographical factors, making it a very interesting subject for sociologists and policy makers. In the last few years, big changes in Internet usage have occurred. In particular, during the last decade, new Social Networks have social scientists reconsidering their research methodology and developing new survey techniques (Internet Survey Techniques). One of the main challenges presented by Internet Surveys is the sampling procedure, as it must be reconsidered in order to avoid the risk of bias and a lack of scientific accountability. The main questions are: (1) Are classic sampling methods an effective way to investigate new Web reality? (2) How can we conduct a valid survey using the Internet? In this chapter these questions are addressed with methodological attention, starting with the problem of defining population in Internet Surveys. The authors also illustrate the main channels for the conduct of Internet Surveys and their specific characteristics. Finally, they discuss some sampling procedures and contact strategies used in Internet Surveys, with a particular attention to a new and important channel: Social Networks (especially Facebook and Twitter).
Chapter Preview
Top

2. Internet Surveys Sampling

One of the main problems encountered by a researcher when designing an Internet survey is the sampling procedure. In this case the classic sampling methods are not effective.

The construction of a good sample is critical to creating a valid survey. According to Kish (1987) and Groves (1989), it is possible to define four different types of populations1:

  • Population of Inference (PI): The set of individuals that are the objects of the study, in a defined time interval (e.g. the population living in Italy in the first semester of 2011).

  • Target Population (TP): The finite set of individuals target of the study. It is possible that some units belonging to the PI are not included in the TP (e.g. people in the hospital at the moment of the survey). The difference between the PI and TP is defined by the researcher and is generally driven by practical purposes (e.g. the researcher can choose to not survey people living on high mountains).

  • Frame Population (FP): A list of units used for drawing the sample. It should be a list of elementary units, for example individuals, or a group of elementary units like families.

  • Survey Population (SP): The set of individuals that will be, if selected, polled. Obviously, the individual inclination to participate at the survey is unrelated with sampling strategies.

There are differences in the size of each of the populations produced by some biases that can be present at the beginning, at the end or during the survey. Without pretending to be exhaustive, some sampling biases are:

  • Noncoverage: The list used to identify sampling units is incomplete; this situation generates the “noncoverage error” (the difference between TP and FP).

  • Total Nonresponse: Some units do not respond to the survey (unit nonresponse) and this situation generates the “unit nonresponse error” (the difference between FP and SP).

It is very important to separate the four populations in order to minimize errors. Obviously, noncoverage error is more serious than unit nonresponse error because the latter is measurable.

Complete Chapter List

Search this Book:
Reset