As more researchers use online panels for studies, the need for standardized rates to evaluate these studies becomes paramount. There are currently many different ways and conflicting terminology used to compute various metrics for online panels. This paper discusses the sparse literature on how to compute response, refusal, and other rates and proposes a set of formulas and a standardized terminology that can be used to calculate and interpret these metrics for online panel studies. A description of and distinction between probability-based and volunteer opt-in panels is made since not all metrics apply to both types. A review of the existing discussion and recommendations, mostly from international organizations, is presented for background and context. In order to propose response and other metrics, the different stages involved in building an online panel are delineated. Metrics associated with these stages contribute to cumulative response rate formulas that can be used to evaluate studies using online probability-based panels. (Only completion rates can be calculated with opt-in panels.) We conclude with a discussion of the meaning of the different metrics proposed and what we think should be reported for which type of panel.

No abstract

No abstract

No abstract

No abstract

No abstract

Recently, the leading position of telephone surveys as the major mode of data collection has been challenged. Telephone surveys suffer from a growing nonresponse, partly due to the general nonresponse trend for all surveys and partly due to changes in society and technology influencing contactability and willingness to answer. One way to counteract the increasing nonresponse is the use of an advance letter. In mail and face-to-face surveys, advance letters have been proven effective. Based on the proven effectiveness in face-to-face and mail surveys, survey handbooks advise the use of advance letters in telephone surveys. This study reviews the evidence for this advice and presents a quantitative summary of empirical studies on the effectiveness of advance letters in raising the response rate for telephone surveys. The major conclusion is that advance letters are also an effective tool in telephone surveys, with an average increase in response rate (RR1) from 58 percent (no letter) to 66 percent (advance letter), and an average increase in cooperation rate (COOP1) from 64 percent (no letter) to 75 percent (advance letter).Telephone surveys have become more and more popular in the last 30 years, reaching their zenith in the 1990s. At the end of the twentieth century, telephone interviews were the major mode of

Summary. The opinion polls that were undertaken before the 2015 UK general election underestimated the Conservative lead over Labour by an average of 7 percentage points. This collective failure led politicians and commentators to question the validity and utility of political polling and raised concerns regarding a broader public loss of confidence in survey research. We assess the likely causes of the 2015 polling errors. We begin by setting out a formal account of the statistical methodology and assumptions that are required for valid estimation of party vote shares by using quota sampling. We then describe the current approach of polling organizations for estimating sampling variability and suggest a new method based on bootstrap Address for correspondence: Patrick Sturgis, Department of Social Statistics and Demography, University of Southampton, Highfield, Southampton, SO17 1BJ, UK. E-mail: P.Sturgis@soton.ac.uk 2 P. Sturgis et al.resampling. Next, we use poll microdata to assess the plausibility of different explanations of the polling errors. Our conclusion is that the primary cause of the polling errors in 2015 was unrepresentative sampling.