surveys may be used in any field of application provided that the elements in the population have Internet access and that they have some basic computer skills. In some cases, as described in detail in Chapter 12, a probability‐based sample from the general population is selected. Then some people without a computer may receive one (with Internet access), together with basic instructions for use. This solution has typically been adopted for general population web panels.
If the web data collection is possible for all potential respondents, a web survey can be a very useful data collection tool, combining low costs and high quality.
Unfortunately, often not all elements in the target population have Internet access, or computers with adequate processing power to process questionnaires, or sufficient computer literacy. This problem holds true for general population surveys as well as for many other possible target populations. Even if Internet penetration is growing, differences may exist between countries and between groups within populations. Large differences in computer equipment, screen settings, and technical literacy may have a substantial impact. Thus, to carry out a good web survey, a statistical sound approach is needed, which attempts to minimize a possible bias as much as possible. And if a bias cannot be avoided, there should be statistical techniques applied to correct for this bias afterward.
In practice, despite the methodological challenges, many surveys (especially commercial surveys) are conducted on the web without properly taking into consideration the impact on the reliability of the results arising from the lack of Internet coverage and/or lack of computer literacy. Such surveys are administered exclusively via the web and therefore reach only one part of the target population. If the survey is not blocked, the web survey is a mobile web survey and will reach the participants also through the mobile device. Nevertheless, only who is using Internet is contacted. When using web survey results, one should be aware of potential problems. Therefore, it is important to assess the quality of the web survey by analyzing the methodological description in the documentation.
EXAMPLE 2.3 Reliable web surveys
Certain surveys are not affected by the instrument bias a web survey may cause. When measuring job satisfaction among high‐tech workers, the bias will be minimal. Getting feedback from employees on a benefit package can have a slightly higher bias if not all employees have computer access. However, an attempt to determine what role of the United States should have been in the Libyan war of 2011 would probably produce highly biased estimates, because one would only obtain the opinions from computer‐literate people with Internet access. They will not be representative for the whole population.
Web surveys may be conducted for profiling purposes. Examples are member surveys, audience profiling, and donor profiles. Web surveys may also be used for data collection by asking people to provide information about themselves. Other interesting applications are socioeconomic research, planning support, and social behavior studies. A web survey can also be used for attitude polls, opinion polls, program evaluation, and community cultural planning surveys. Other possible topics are economic aspects and performances, as well as market trends and customer/employee satisfaction.
EXAMPLE 2.4 The Kauffman Firm Survey (KFS)
The Kauffman Firm Survey (KFS) is a panel study of new businesses founded in 2004. They are tracked over their early years of operation. The survey focuses on the nature of new business formation activities, characteristics of the strategy, offerings, employment patterns of new businesses, the nature of the financial and organizational arrangements of these businesses, and the characteristics of their founders.
The KFS created the panel by using a random sample from a Dun & Bradstreet (D&B) database list of new businesses started in 2004. The list contained in total about 250,000 businesses.
The KFS oversampled these businesses based on the intensity of research and development employment in the businesses' primary industries. The KFS sought to create a panel that included new businesses created by a person or team of people, purchases of existing businesses by a new ownership team, and purchases of franchises. To this end, the KFS excluded D&B records for businesses that were wholly owned subsidiaries of existing businesses, businesses inherited from someone else, and not‐for‐profit organizations. Previous research on new businesses showed also variability in how business founders perceive the operation of their starting businesses. Therefore, a series of questions was asked to business owners about indicators of business activity and whether these were conducted for the first time in the reference year (2004). These indicators were payment of state unemployment (UI) taxes, payment of Federal Insurance Contributions Act (FICA) taxes, presence of a legal status for the business, use of an Employer Identification Number (EIN), and use of Schedule C to report business income on a personal tax return.
A self‐administered web survey and computer‐assisted telephone interviewing (CATI) were used for data collection.
Over time several changes in the original sample of businesses occurred and went for different reasons out of the panel. Due to panel attrition the number of units is becoming slightly smaller each year. Since only the 2004 cohort is under study, no continuous refreshment is planned. At the time being the Kauffman Firm Survey (KFS) is a panel study of 4,928 businesses founded in 2004 and tracked over their early years of operation. Until September 2020, the University of Chicago NORC Data Enclave has been managing a secure remote access to the KFS confidential microdata file access for researchers. Free access was allowed. After September 2020, access to the KFS is shifted towards a fee-based model.
Objective of the panel survey is to provide information about creation and development aspects on new businesses (especially of high‐technology and women‐owned businesses). Firm characteristics, revenue and expenses, profit and loss, owner characteristics, and, since 2007, information about predominant markets and Internet sales are also collected.
2.2.4 TRENDS IN WEB SURVEYS
Dillman, Smyth, and Christian (2014) describe some changes in the survey environment from the 1970 to 2000, focusing on the factors like human interaction, trust that the survey is legitimate, time involvement for each respondent, attention given to each respondent, respondent control over access, and respondent control over whether to respond. These observations indicate that during the 1990s human interaction and individual contact relevance are decreasing due to the use of IT (i.e., computer‐aided and web surveys) and to massive use of e‐mails. Trust on survey relevance and legacy is very low, and the possibility of refusal and of filtering against surveys (anti‐spam, disclosure rules) is very high. These observations are in line with developments with respect to web surveys.
More recently, Biffignandi (2010a, 2010b) has been focusing on major trends with respect to web surveys. The author underlines that survey methodology has quite recently undergone a paradigm shift: the causes of survey errors and how to prevent them. This new paradigm stresses the total error concept, which includes sampling error (the central error in the traditional approach) and non‐sampling errors. Non‐sampling errors can be numerically larger than sampling errors. The flowchart of Chapter 3 describes the survey process. Errors might occur at each step of the process. The new paradigm focuses on the following aspects:
All kinds of events and behavior occurring during the survey process are considered.
The overall response rate is only a very simple measure of survey quality, although it is frequently used as an indicator. To some extent this measure
