imperfections in the questionnaire may have serious consequences in terms of data quality. Questionnaire design issues are addressed in Chapter 7.
Chapter 8 examines strategies for data collection with adaptive/responsive survey design. In this case, strategies are not defined in advance, but instead are adapted, if necessary, during fieldwork. These designs may contribute to countering growing problems of nonresponse. This chapter was written by Annamaria Bianchi and Barry Schouten, who applied their particular expertise in this field to the subject examined.
A web survey may not always be the best solution for providing reliable and accurate statistics, with quality being affected by problems of under‐coverage and low response rates. An interesting alternative is to set up a mixed‐mode survey, in which several data collection methods are combined either sequentially or concurrently. This approach is less expensive than a single‐mode interviewer‐assisted survey (conducted either face to face or by telephone) and solves under‐coverage problems, but at the same time it poses other difficulties, known as mode effects, with one of the most significant of these being measurement error. Mixing modes is also of critical importance, as is the fact that in practice, a web survey is always mobile, unless questionnaire access via mobile device is restricted. All these aspects, as well as others concerning mixed‐mode surveys, are discussed in Chapter 9.
Chapter 10 is devoted to the problem of under‐coverage. This remains an important problem in many countries due to poor Internet coverage and the fact that Internet access is often unevenly distributed throughout the population. The chapter demonstrates how this can lead to survey estimates being biased. A number of techniques that may reduce under‐coverage bias are discussed.
Chapter 11 examines self‐selection. The correct and scientifically well‐founded principle is to use probability sampling in order to select survey subjects and therefore allow reliable estimates regarding population characteristics to be calculated. Nowadays, it is easy to set up a web survey. Even those without any survey knowledge or experience can create one through dedicated websites. Many of the resulting web surveys do not apply probability sampling, but instead rely on self‐selection of respondents. This causes serious problems with estimation. Self‐selection and its consequences in terms of survey results are discussed in this chapter, demonstrating that correction techniques are not always effective, and there are many reasons why web‐survey‐based estimates are biased.
Nonresponse, under‐coverage, and self‐selection are typical examples, and adjustment weighting is often applied in surveys in order to reduce such biases. Chapter 12 describes various weighting techniques, such as post‐stratification, generalized regression estimation and raking ratio estimation. The effectiveness of these techniques in reducing bias caused by under‐coverage or self‐selection is examined.
Chapter 13 introduces the concept of response probabilities, describing how they can be estimated through response propensities. If estimated accurately, response probabilities can be used to correct biased estimates. Here, two general approaches are described: response propensity weighting and response propensity stratification. The first attempts to adjust the original selection probabilities, while the second is a form of post‐stratification.
Chapter 14 is devoted to web panels. There are many such panels, particularly in the field of commercial market research. One crucial aspect is how the panel members (households, individuals, companies, and shops) are recruited. This can be carried out via a proper probability sample, or through self‐selection. There are consequences for the validity of the results of the specific surveys conducted with the panel members. The chapter discusses several quality indicators.
The accompanying website, www.web‐survey‐handbook.com, provides the survey data set for the general population survey (GPS), which has been used for many examples and applications in the book. The data set is available in SPSS (SPSS Corporation, Chicago, IL) format.
Silvia Biffignandi
Jelke Bethlehem
The editors acknowledge the contributions of:
Lon Hofman (Manager Blaise, Statistics Netherlands) and Mark Pierzchala (owner of MMP Survey Services, Rockville, USA) who wrote Section 1.3.1.
Annamaria Bianchi (University of Bergamo) and Barry Schouten (Statistics Netherlands) who wrote Chapter 8.
Chapter One The Road to Web Surveys
1.1 Introduction
Web surveys are a next step in the evolution process of survey data collection. Collecting data for compiling statistical overviews is already very old, almost as old as mankind. All through history, rulers of countries used statistics to take informed decisions. However, new developments in society always have had their impact on the way the data were collected for these statistics.
For a long period, until the year 1895, statistical data collection was based on complete enumeration of populations. The censuses were mostly conducted to establish the size of the population, to determine tax obligations of the people, and to measure the military strength of the country. The idea of sampling had not emerged yet.
The year 1895 marks a fundamental change. Populations had grown bigger and bigger. It was the period of industrialization. Centralized governments required more and more information. The time was ripe for sample surveys. The first ideas emerged around 1895. There was a lot of discussion between 1895 and 1934 about how to select samples: by means of probability sampling or some other sample selection technique.
By 1934, it was clear that only surveys based on probability sampling could provide reliable and accurate estimates. Such methods of data collection were accepted as a scientific. In the period from 1940s to the 1970s, most sample surveys were probability based. Questionnaires were on paper forms. They were completed in face‐to‐face, telephone, or mail.
Somewhere in the 1970s another significant development started. The fast development of microcomputers made it possible to introduce computer‐assisted interviewing (CAI). This made survey data collection faster, cheaper, and easier and increased data quality. It was time in which acronyms like CATI (computer‐assisted telephone interviewing) and CAPI (computer‐assisted personal interviewing) emerged.
The next major development was the creation of the Internet around 1982. When more and more persons and companies got access to the Internet, it became possible to use this network for survey data collection. The first Internet surveys were e‐mail surveys. In 1989 the World Wide Web was developed. This software allowed for friendly graphical user interfaces for Internet users. The first browsers emerged and the use of Internet exploded. In the middle of 1990s, the World Wide Web became widely available, and e‐mail surveys were increasingly replaced by web surveys.
Web surveys are attractive because they have a number of advantages. They allow for simple, fast, and cheap access to large groups of potential respondents. Not surprisingly, the number of conducted web surveys has increased
