CoChat
Sign Up
Cited by 1,213Survey Sampling and Estimation TechniquesSurvey Methodology and NonresponseAdvanced Causal Inference Techniques

An Introduction to Sample Selection Bias in Sociological Data

Berk, Richard A.·1983·American Sociological Review
DOI 10.2307/2095230
Open this paper in CoChat — free account

Abstract

White, Douglas R. and Karl Reitz ForthGraphs and semigroup homomorphisms comon networks of relations. Neting works. White, Harrison C., Scott A. Boorman and Ronald L. Breiger 1976 Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology 81:730-80. Winship, Christopher 1976 Roles and relations. Unpublished paper, Department of Sociology, Harvard University. Winship, Christopher and Michael J. Mandel ForthRoles and positions: a critique and extencomsion of the blockmodelling approach. In ing Samuel Leinhardt (ed.), Sociological Methodology 1983/84. San Francisco: JosseyBass. Wu, Lawrence L. 1980 Roles structures in networks of trade and economic interdependence: a local blockmodel algebraic approach. Seniors Honors Thesis, Department of Sociology, Harvard University. 1982 Local blockmodel algebras. Unpublished paper, Department of Sociology, Stanford University.

Integrity signals

Registry

Crossref · 10.2307/2095230

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Sociological surveys of sample selection bias tend to focus on cases where selection is affected by treatment or outcome, i.e., after treatment.

    “Surveys of sample selection bias in sociology tend to emphasize examples where selection is affected by the treatment or the outcome, i.e., where sample selection occurs after treatment”

    From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Berk 1983

  • Methodologists have long warned that conditioning on an intermediate variable affected by treatment can lead to bias.

    “Methodologists have long warned that conditioning on an intermediate variable that has been affected by the treatment can lead to bias (e.g., Heckman 1976; Berk 1983; Rosenbaum 1984; Holland 1988; Robins 1989, 2001, 2003; Smith 1990; Angrist & Krueger 1999; Wooldridge 2005; Sobel 2008).”

    From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Berk 1983

  • The sign and magnitude of endogenous selection bias cannot generally be predicted without additional assumptions.

    “sign and magnitude of endogenous selection bias cannot generally be predicted without additional assumptions”

    From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Berk 1983

Cited by

Sorted by citations

Authors
Berk, R. A.
Affiliations
University of California, Santa Barbara (US)
Corresponding author
Berk, Richard A.
ORCID
Berk, Richard A.
Venue
American Sociological Review
Publisher
SAGE Publications
Published
1983-06
Volume
48
Issue
3
Pages
386
ISSN
0003-1224 (ISSN-L)
DOI
10.2307/2095230
OpenAlex
W1968059489
Cited by
1,213
References
0
Citations by year
2026: 9 · 2025: 16 · 2024: 21 · 2023: 12 · 2022: 25 · 2021: 35 · 2020: 30 · 2019: 41 · 2018: 45 · 2017: 32 · 2016: 35 · 2015: 33 · 2014: 34 · 2013: 38 · 2012: 43
FWCI
19.55
Type
journal article
Language
en
Open access
closed
Locations
American Sociological Review (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
sample selection bias, sociological data, survey sampling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review