Handbook of Web Surveys. Jelke Bethlehem

Handbook of Web Surveys - Jelke Bethlehem


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frame) is available, then it is possible to proceed with the web‐only survey. However, if the list of e‐mail addresses is incomplete (bad sampling frame) or does not exist, the surveyor must decide if an alternative sampling frame is available, for example, a sampling frame of telephone numbers or postal addresses. If the alternative sampling frame is available, a mixed‐mode approach should be adopted. The surveyor should select a contact mode (telephone or mail) and approach the sampled interviewees to ask them to participate in the survey and if they can provide an e‐mail address or not. If the researcher intends to conclude the survey via web, he can provide a personal computer and Internet access (with e‐mail address) to those without Internet and e‐mail address. In this case, the data collection takes place via a web or mobile web survey. Thus, from this step of Figure 3.2, it is possible to follow the decision steps of the flowchart in Figure 3.1. Whether the researcher don't want to provide Internet access or interviewees do not agree to participate via the web or if they do not provide an e‐mail address, the interview should be administered using an alternative mode. In such a case, the surveyor must run a mixed‐mode survey with a web component (see Chapter 9). In this case also, from this step of Figure 3.2, it is possible to follow the decision steps of the flowchart in Figure 3.1.

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      There are considerable advantages associated with web/mobile web survey compared with face‐to‐face, mainly in term of cost and timeliness. However, web mode does perform more poorly in terms of coverage and participation. For this reason, researchers sometimes consider using a mixed‐mode approach including web, even if no problem exists in term of sampling frame (see experiments and comments in Jäckle, Lynn, and Burton, 2015).

      If the conditions to a web survey are present, the surveyor will return to the steps and sub‐steps of the Figure 3.1 flowchart.

      According to Callegaro (2013), in web surveys, it is possible to distinguish between device‐type paradata and questionnaire navigation paradata. Device‐type paradata provide information regarding the kind of device used to complete the interview (i.e., tablet or desktop). They provide information about the technical features of the device (browser, screen resolution, IP address, and several other characteristics). Questionnaire navigation paradata describe the full set of activities undertaken in completing the questionnaire, for example, mouse clicks, forward and backward movements along the questionnaire, number of error messages generated, time spent per question, and question answered before dropping out (if dropout exists). Other authors (for example, Heerwegh, 2011) distinguish between client‐side paradata (they include click mouse and everything related to the activities of the respondent) and server‐side paradata (they include everything collected from the server hosting the survey). The literature proposes also other classifications, and the technology evolution is going to offer new types of paradata. Capturing paradata is one of the main challenges. Software industry improves greatly and constantly. Traditionally most programs were collecting only a few server‐side paradata. Not every program is registering client‐side paradata. Technological innovation and commitment on this important task have contributed to enlarge the offer of programs collecting paradata and transmitting the often unintelligible strings into useful data sets. Due to high innovation in this field, software is fast over; some discussion about the topic is found in Olson and Parkhurst (2013) and in Kreuter (2013).

      Currently, it is clear that paradata are useful data types for several different functions, such as monitoring nonresponse and measurement profiles, checking for measurement error and bias, improving questionnaire usability, and fixing many other problems. Due to their potential usefulness in helping to understand relationships between different errors, improving the data collection process, and the quality of results, paradata require a decisional sub‐step to plan their structure and data collection.


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