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Book Review: Online Panel Research: A Data Quality Perspective

Online Panel Research. A Data Quality Perspective by Mario Callegaro, Reg Baker, Jelke Bethlehem, Anja S. Göritz, Jon A. Krosnick, and Paul J. Lavrakas is an edited volume that brings together state-of-the-art findings on various aspects of online panel research. It presents evidence on a diverse set of research questions on detecting and correcting for different kinds of errors arising in online panels. The book also gives advice on practical aspects of conducting online panels and new developments regarding web panel software. The book is a valuable addition to and extension of the existing literature on online surveys. Other books in this area typically focus on survey design and on the practical implementation of web surveys (see for example Couper 2008 and Tourangeau et al. 2013). Online Panel Research is different in two main respects: firstly, while previous literature has a broad focus on all kinds of web survey research, this book concentrates exclusively on survey methodological research on online panels; secondly, this book focuses particularly on errors and biases in online panels. In structural terms the book follows a Total Survey Error logic (see Groves et al. 2009). It is a compact collection of findings on the most important issues in online panel research including studies from various countries. The book is very comprehensive and highly instructive for survey methodological research, and is particularly valuable for survey practitioners either already conducting or still aiming to build an online panel. However, there are some caveats that the reader should be aware of: first, the book is generally written from a commercial data collection rather than an academic perspective. This becomes apparent in the language used in several chapters and section introductions throughout the book where “customer” and “client” interests are emphasized and survey “companies” (see p. 9) are addressed. Second, most chapters apply a limited definition of representativeness, that is, the authors assume that online panels need only be representative of the online population. The reason for this might be that most online panels simply do not include non-Internet users. Some probability-based online panels aim to be representative of the general population and include previously offline persons (so-called offliners) by providing them with the necessary equipment. This aspect of increasing representativeness by including offliners is not discussed in the book, not even