Stratification in Representative Sampling

Stephan, Frederick F.

doi:10.1177/002224294100600107

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…Although stratification appeared in some earlier sampling work [28,109], Neyman helped establish it for a wider audience [18]. Stratification bridged the purposive-random tension, providing a way to increase the precision of estimates by incorporating knowledge of group differences into random sampling [47,148]. It was also part of the emerging theory of probability sampling, in which unequal chances of selection were allowed and sometimes preferable.…”

Section: Stratification: a Representative Methodsmentioning

confidence: 99%

“…The "first essential" of sampling, wrote Bowley in 1910, is "that every member of the group considered should have nearly the same chance of being included in the sample" [27, p. 56]. It was standard early on to emphasize this "equal chance" as a defining feature of mechanical approaches [18], perhaps because it suggests a form of procedural fairness (some even spoke of "fair samples" [29,93,107,148]) and neutrality. After all, random samples are only guaranteed to be accurate and nonpreferential on average.…”

Section: Achieving Representativeness: Mechanical and Purposive Selec...mentioning

confidence: 99%

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Representativeness in Statistics, Politics, and Machine Learning

Chasalow

Levy

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more general sense of accuracy, generalizability, coverage, or inclusiveness. Moreover, the concept has long been contested. In statistics, debates about the merits and methods of selecting a representative sample date back to the late 19 th century; in politics, debates about the value of likeness as a logic of political representation are older still. Today, as the concept crops up in the study of fairness and accountability in machine learning, we need to carefully consider the term's meanings in order to communicate clearly and account for their normative implications. In this paper, we ask what representativeness means, how it is mobilized socially, and what values and ideals it communicates or confronts. We trace the concept's history in statistics and discuss normative tensions concerning its relationship to likeness, exclusion, authority, and aspiration. We draw on these analyses to think through how representativeness is used in FAccT debates, with emphasis on data, shift, participation, and power. CCS CONCEPTS • Applied computing → Law, social and behavioral sciences; • Mathematics of computing → Probability and statistics.

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Section: Stratification: a Representative Methodsmentioning

confidence: 99%

Section: Achieving Representativeness: Mechanical and Purposive Selec...mentioning

confidence: 99%

Representativeness in Statistics, Politics, and Machine Learning

Chasalow

Levy

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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“…Achieving a representative sample requires prior knowledge of the population being investigated and creates a challenge for researching the IRS who often have little opportunity, interest, or compulsion to document their activities or have them documented by others (Linzner and Lange, 2013). Even if characteristic data are available for an IRS population, it may not always be suitable for the strati cation of samples (Stephan, 1941). For example, Sabedot and Pereira Neto (2017) used the age variance of 150 waste pickers to select a sample of 29 informal workers structured interviews on composition and productivity.…”

Section: Insights For Future Data Collectionmentioning

confidence: 99%

Informal recycling sector contribution to plastic pollution mitigation: A systematic scoping review and quantitative analysis of prevalence and productivity

Cook,

Cano,

Velis

2024

Preprint

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Ongoing negotiations for a ‘Legally Binding Instrument on Plastic Pollution’ recognise the substantial contribution made by the informal recycling sector (IRS - waste pickers) to plastic pollution mitigation as part of just transition. Negotiating parties will require baseline evidence of the sector’s activities to inform the development of local and national actions plans. To this, we carried out a review of IRS prevalence and productivity following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) method followed by extensive (statistical) data analytics. Waste pickers represent median 0.2% (interquartile range – IQR: 0.1-0.5%) of the urban population worldwide, collecting between 20 kg and 80 kg of engineered materials for recycling each day, of which 30% (mean wt. ar) are plastics. We identify substantial shortcomings in most methodologies used to gather data on the IRS, introducing epistemic uncertainty into some previous estimates of the sector’s activity. We recommend development of a standardised resource-efficient method of sampling and data gathering, suitable for implementation at municipal/local scale. Our work offers verifiable quantitative knowledge on the sector’s activities to date, suitable for use in plastic pollution quantification models and local/national action plans required to baseline and monitor progress towards multilateral targets.

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“…The breadth of Stephan's (1941Stephan's ( , 1948Stephan's ( , 1957 contributions can be gauged by articles on sampling procedures and surveys in the Journal of the American Statistical Association (1948), the Journal of Marketing (1941) and Public Opinion Quarterly (1957). Individuals such as Stephan were notable not just for their original contributions to develop methods and tools, but for their ability to straddle and connect different disciplinary and professional fields.…”

Section: Infrastructuring the Business Of Market Researchmentioning

confidence: 99%

Markets, infrastructures and infrastructuring markets

Araújo¹,

Mason

2021

AMS Rev

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Despite a growing understanding of market infrastructures—the rules and socio-material arrangements that enable agreements on the properties of goods, and the calculation of value, equivalence and exchange—we know little of what lies beneath the arrangements that underpin and are implicated in exchange. The socio-material lens has done much to explain how specific assemblages circulate information and goods, but has done little to explain how different infrastructures configure relations between dispersed market practices. Using the history of the development of the market for market research we show how knowledge-based infrastructures constitute markets as knowledge objects: new expertise emerged through alliances between academia, government, and private actors form a new occupation embodied in specialist agencies that set themselves up in an infrastructural relation to marketing practices. Our conceptualization of markets as knowledge objects extends extant understandings of markets by showing how: (1) extant knowledge-based infrastructures are drawn on to construct new markets; (2) infrastructural relations emerge between different markets to constitute multiple systems of provision and demand, leading to an increasingly valuable knowledge infrastructure; and (3) organized practices in one market are often heavily reliant on connections to other markets, including knowledge-based infrastructures such as market research services.

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Stratification in Representative Sampling

Cited by 12 publications

References 6 publications

Representativeness in Statistics, Politics, and Machine Learning

Representativeness in Statistics, Politics, and Machine Learning

Informal recycling sector contribution to plastic pollution mitigation: A systematic scoping review and quantitative analysis of prevalence and productivity

Markets, infrastructures and infrastructuring markets

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