ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS.

Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples.

A simulation study of diagnostics for selection bias.

A non-probability sampling mechanism arising from non-response or non-selection is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is 'non-ignorable', i.e.

Developing a short screener for acquiescent respondents.

Background: Acquiescent response style (ARS) refers to survey respondents' tendency to choose response categories agreeing to questions regardless of their content and is hypothesized as a stable respondent trait. While what underlies acquiescence is debatable, the effect of ARS on measurement is clear: bias through artificially increased agreement ratings. With certain population subgroups (e.

Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms.

Impact of the Terms "" or "" as Spanish Translation for "Fair" of the Self-Rated Health Question Among US Latinos: A Randomized Experiment.

To examine measurement comparability of a Spanish version of self-rated health (SRH) with as an alternative to for the response category "fair" in the English version. We translated "fair" into 2 Spanish versions: and . We implemented a split-half experiment in 3 surveys independently conducted from October 2015 to January 2016, from April to November 2016, and from August to November 2017.