April 2018 PMID 29453895

Cross-sectional studies - what are they good for?

Ulrik S Kesmodel

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Mentions

Peak: 12 in Aug 2020

across 3 sources since Aug 2020

Extended reach

42,190

potential audience

Total likes

193

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33,623

Mentions per month

Aug 2020 – Jul 2021

Where it's discussed

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AI synopsis

What people are saying

The discussion emphasizes the limitations of cross-sectional studies for causal inference, using terms like 'unsuitable for causal inference' and referencing alternative methods such as SEM, which reflects a deeper engagement with research methodology. The tone balances technical clarity and acknowledgment of the subject's complexity, indicating a moderate level of interest and impact among academics familiar with these concepts.

AgreementModerate agreement

Most comments acknowledge the limitations of cross-sectional studies in establishing causality, reflecting a consensus among social and medical scientists.

InterestModerate interest

Discussions indicate moderate interest, mainly focused on clarifying methodological points rather than seeking new knowledge.

EngagementModerate engagement

Several comments delve into technical methods like SEM, showing some in-depth engagement with research techniques.

ImpactModerate impact

The comments highlight awareness of the importance of study design biases, suggesting the discussion has some influence on understanding research validity.

Abstract in brief

Crosssectional studies are useful for determining the prevalence of diseases, attitudes, and knowledge among patients and health personnel, and for comparing measurement tools in validation and reliability studies.
They are especially relevant in fields like obstetrics and gynecology for descriptive purposes, but should be used cautiously when exploring associations between exposures and outcomes, as they cannot establish causality.
Researchers should be aware of potential biases such as selection bias, information bias, and confounding, and should avoid making causal inferences unless the exposure is stable and not affected by the outcome.]

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