We believe IS researchers can and should do a better of job of improving (assuring) the validity of their findings by minimizing nonresponse error. To demonstrate that there is, in fact, a problem, we first present the response rates reported in six well-regarded IS journals and summarize how nonresponse error was estimated and handled in published IS research. To illustrate how nonresponse err...

All surveys with less than full response potentially suffer from nonresponse bias. Poststratification weights can only correct for selection into the sample based on observables whose distribution is known in the population. Variables such as gender, race, income, and region satisfy this requirement because they are available from the U.S. Census Bureau, but poststratification based on these va...

This paper summarizes the major findings from a nonresponse bias analysis of the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) first grade data. The analysis examines the potential for bias in estimates from the ECLS-K due to attrition (nonresponse) between the base year (rounds 1 and 2) and first grade (rounds 3 and 4). This work builds on a base year nonresponse b...

Recent research on the impact of cell phones has largely focused on coverage and nonresponse error with few exceptions (Kennedy et al 2009, Brick et al 2011). In this work the authors focus on nonsampling error in the American Time Use Survey (ATUS). This nationally representative survey is conducted by the U.S. Census Bureau on behalf of the Bureau of Labor Statistics. The sample for the ATUS ...

It is well-known that nonresponse affects the results of surveys and can even cause bias due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys , response behaviour in business surveys has been examined rarely in the literature. This paper is one of the first which analyses a large business survey on micro data level for unit nonresponse. The data b...

Introduction Longitudinal design in surveys provides a unique opportunity to study the causes and effects of nonresponse, particularly when participants in a given round are recruited from a cohort of respondents from the prior rounds. The availability of survey data for respondents and nonrespondents in a given round allows one to directly estimate nonresponse bias and its components, as well ...

The Medical Expenditure Panel Survey (MEPS) is sponsored by the Agency for Healthcare Research and Quality (AHRQ). MEPS, a complex national probability sample survey, is conducted to provide nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. It comprises three component surveys w...

This article presents the results of an experiment designed to compare item-level nonresponse rates to paperand web-based versions of a survey questionnaire, focusing on attitudinal variables, that was administered to highly accomplished teachers. A sample of teachers reported their perceptions of professional community; half were assigned to a web-based version of the questionnaire, and the ot...

Survey researchers and practitioners use nonresponse adjustment weights to mitigate the effects of survey nonresponse on sample estimates. One challenge in creating these weights is finding useful auxiliary data that predict both the probability of participating in the survey and the survey variables of interest. This article reviews the use of paradata for nonresponse adjustment. Five differen...

Valid estimates of caries experience are needed to monitor oral population health. Obtaining such estimates in practice is often complicated by nonresponse and missing data. The goal of this study was to estimate caries experiences in a population of children aged 5 and 11 years, in the presence of nonresponse and missing data. Four estimation methods are compared. Each method makes implicit as...