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.g., racial/ethnic minorities in the U.S.) are associated with systemically higher ARS, it causes concerns for research involving those groups. For this reason, it may be necessary to classify respondents as acquiescers or a nonacquiescers, which allows independent analysis or accounting for this stylistic artifact. However, this classification is challenging, because ARS is latent, observed only as a by-product of collected data. OBJECTIVES: To propose a screener that identifies respondents as acquiescers. METHODS: With survey data collected for ARS research, various ARS classification methods were compared for validity as well as implementation practicality. RESULTS: The majority of respondents was classified consistently into acquiescers or nonacquiescers under various classification methods. CONCLUSIONS: We propose a method based on illogical responses given to two balanced, theoretically distant multi-item measurement scales as a screener.

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. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43-62 (2016)], adding two recently published statistics: the so-called 'standardized measure of unadjusted bias (SMUB)' and 'standardized measure of adjusted bias (SMAB)', which explicitly quantify the extent of bias (in the case of SMUB) or non-ignorable bias (in the case of SMAB) under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new sensitivity diagnostic is more correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect.

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. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.

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

Objectives. To examine measurement comparability of a Spanish version of self-rated health (SRH) with pasable as an alternative to regular for the response category "fair" in the English version.Methods. We translated "fair" into 2 Spanish versions: regular and pasable. 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. Within each survey, we randomly assigned Spanish-interviewed Latino respondents to 1 of the 2 SRH versions. The total sample included 3261 Latino and 738 non-Latino White adults in the United States.Results. Spanish-interviewed Latinos reported substantively more favorable health on SRH with pasable than with regular. When pasable instead of regular was used for SRH, we observed a larger difference between respondents reporting positive versus negative SRH on objective health measures, including the frequency of doctor's visits. Furthermore, when we accounted for correlates of health, Latino-White disparities were attenuated with pasable.Conclusions. We recommend using pasable instead of regular for SRH Spanish translations to improve measurement equivalence in cross-lingual and cross-cultural research.

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. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.