Inference for Nonprobability Samples

Elliott, Michael R.; Valliant, Richard

doi:10.1214/16-sts598

Cited by 229 publications

(193 citation statements)

References 54 publications

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“…There is a growing appreciation that, rather than being restricted to convenience samples that often exclude harder-to-recruit socio-economic groups, research studies should strive to increase the diversity of their samples (Keiding and Louis, 2016). Consistent with this aim of achieving greater “external validity” for both experimental and observational study findings, there is a growing body of statistical literature and methods that aim to maximize the population representativeness even when study conditions do not permit a full application of traditional probability sampling methods (see Dugoff et al 2014; O’Muircheartaigh and Hodges, 2014; Stuart et al, 2015; Elliott and Valliant, 2017). While there now exists institutional commitment to ensuring sex differences are incorporated into study designs (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15–102.html) the same is not yet true for other factors such as race and ethnicity, SES and urbanicity.…”

Section: Adolescent Diversity and National Representativenessmentioning

confidence: 99%

Recruiting the ABCD sample: Design considerations and procedures

Garavan

Bartsch

Conway

et al. 2018

Developmental Cognitive Neuroscience

862

804

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The ABCD study is a new and ongoing project of very substantial size and scale involving 21 data acquisition sites. It aims to recruit 11,500 children and follow them for ten years with extensive assessments at multiple timepoints. To deliver on its potential to adequately describe adolescent development, it is essential that it adopt recruitment procedures that are efficient and effective and will yield a sample that reflects the nation's diversity in an epidemiologically informed manner. Here, we describe the sampling plans and recruitment procedures of this study. Participants are largely recruited through the school systems with school selection informed by gender, race and ethnicity, socioeconomic status, and urbanicity. Procedures for school selection designed to mitigate selection biases, dynamic monitoring of the accumulating sample to correct deviations from recruitment targets, and a description of the recruitment procedures designed to foster a collaborative attitude between the researchers, the schools and the local communities, are provided.

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Section: Adolescent Diversity and National Representativenessmentioning

confidence: 99%

Recruiting the ABCD sample: Design considerations and procedures

Garavan

Bartsch

Conway

et al. 2018

Developmental Cognitive Neuroscience

862

804

View full text Add to dashboard Cite

show abstract

“…It thus becomes essential that inferential methods allow for missing data from nonresponse. Alongside the decline in response rates have been significant changes in survey practice, such as greater use of nonprobability sampling (Elliott and Valliant, 2017) associated, particularly, with web surveys; see Schonlau and Couper (2017).…”

Section: Nonprobability Sampling and Nonresponsementioning

confidence: 99%

“…They both involve forms of sample selection which are not fully under the control of the survey designer and to proceed, both require modeling assumptions. Elliott and Valliant (2017) provide an in-depth discussion of two broad approaches in this context, and we here briefly introduce these ideas.…”

Section: Nonprobability Sampling and Nonresponsementioning

confidence: 99%

“…Under the resulting quasi-random representation of the sample selection, design-based methods may be employed, for example, in the construction of survey weights. In the case of non-probability sampling, Elliott and Valliant (2017) give particular attention to a case where an additional probability sample from the population is available for use in determining pseudo-weights for the nonprobability sample.…”

Section: Nonprobability Sampling and Nonresponsementioning

confidence: 99%

“…The second broad method is referred to as a superpopulation model approach by Elliott and Valliant (2017) and involves the model-based prediction approach outlined in Section 2.1.2. This depends critically on the auxiliary information available.…”

Section: Nonprobability Sampling and Nonresponsementioning

confidence: 99%

See 2 more Smart Citations

Introduction to the Design and Analysis of Complex Survey Data

Skinner¹,

Wakefield²

2017

Statist. Sci.

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Abstract. We give a brief overview of common sampling designs used in a survey setting, and introduce the principal inferential paradigms under which data from complex surveys may be analyzed. In particular, we distinguish between design-based, model-based and model-assisted approaches. Simple examples highlight the key differences between the approaches. We discuss the interplay between inferential approaches and targets of inference and the important issue of variance estimation.

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Combining Estimates from Multiple Surveys

Elliott

Raghunathan

Schenker

2018

Wiley StatsRef: Statistics Reference Online

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Combining estimates from multiple surveys can be very useful, especially when the question of interest cannot be addressed well by a single, existing survey. In this article, we provide a brief review of the methodology for combining estimates, with a focus on dual frame, weighting‐based, joint‐modeling, missing‐data, and small‐area methods. Many such methods are useful in situations outside the realm of combining estimates from multiple surveys, such as combining information from surveys with administrative data and combining probability‐sample data with nonprobability sample, or “big” data. We also provide examples of comparability issues that must be kept in mind when information from different sources is being combined.

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Inference for Nonprobability Samples

Cited by 229 publications

References 54 publications

Recruiting the ABCD sample: Design considerations and procedures

Recruiting the ABCD sample: Design considerations and procedures

Introduction to the Design and Analysis of Complex Survey Data

Combining Estimates from Multiple Surveys

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