The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index; none of the 2 sources its claims cite is checked for support yet.
Cross-Sectional Studies
Open accessAbstract
Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.
Overview
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Claims the paper makes
11 Own interpretation · 1 Sources not found · 1 No full text of the sources
In the paper
“Prevalenceincidence bias (also called the Neyman bias) is also particularly common in cross-sectional studies.13”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Bias
What the sources say
- Not checked — no full text of the sourcecites [13] Tripepi, 2010, Selection bias and information bias in clinical research, Nephron Clin Pract source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They cannot be used to look at individual change (as in a cohort study).”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Description of Subtypes of Cross-Sectional Studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the outcome and exposure variables are measured at the same time, it is relatively difficult to establish causal relationships from a cross-sectional study.”
- Also stated in: § Description of Subtypes of Cross-Sectional Studies · plain
“The exposures and outcomes are measured simultaneously; therefore, it is difficult to determine whether the exposures preceded or followed the outcomes in an analytical cross-sectional study.”
- Also stated in: § Benefits and Downside of Cross-Sectional Studies · plain
“The weaknesses of cross-sectional studies include the inability to assess incidence, to study rare diseases, and to make a causal inference.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > General Overview of Cross-Sectional Study Design
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Description of Subtypes of Cross-Sectional Studies · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, repeated cross-sectional studies can be used for analyzing population changes over time (also known as aggregate change over time).”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Description of Subtypes of Cross-Sectional Studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“If one wants to compare two prevalence rates in an analytical cross-sectional study, the commonly used sample size formula is the same as is used when designing a cohort study.6”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Sample Size Determination
What the sources say
- Not matchedcites [6] Fleiss, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unlike studies starting from a series of patients, cross-sectional studies often need to select a sample of subjects from a large and heterogeneous study population.Thus, they are susceptible to sampling bias.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Benefits and Downside of Cross-Sectional Studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The subjects are neither deliberately exposed nor treated; thus, there are seldom ethical difficulties.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Benefits and Downside of Cross-Sectional Studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 4
- It may state two separate claims as one. 3
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Stepwise selection methods and P value-based criteria are discouraged due to overfitting and because they often allow too many parameters to be included, which reduces the generalizability of the model.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Additional information
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Confounding could result in a distortion of the association between exposure and outcome.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Confounding
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that the sample size in a descriptive study does not depend on statistical power because this concept only applies to statistical comparisons.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Sample Size Determination
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The weaknesses of cross-sectional studies include the inability to assess incidence, to study rare diseases, and to make a causal inference.”
- Also stated in: § 1. The study population and the research question. · plain
“Unable to measure the incidence”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Benefits and Downside of Cross-Sectional Studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Also stated in: § 1. The study population and the research question. · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only with full and transparent reporting of information on all aspects of a cross-sectional study can potential usefulness of its findings and risk of bias be adequately assessed.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because exposure and outcome are measured simultaneously in a cross-sectional study, prior knowledge of the condition might influence the ascertainment of the exposure or the outcome, which results in recall bias.”
§ Cross-Sectional Studies Strengths, Weaknesses, and Recommendations > Bias
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Cross-sectional studies are usually quick and inexpensive to conduct.
“They are usually inexpensive and easy to conduct.”
“The main strength of cross-sectional studies is that they are relatively quick and inexpensive to conduct.”
The paper relies on this without support.
Cross-sectional studies are the best way to determine prevalence.
“They are the best way to determine the prevalence and can study the associations of multiple exposures and outcomes.”
The paper relies on this without support.
Cross-sectional studies can study the associations of multiple exposures and outcomes.
“They are the best way to determine the prevalence and can study the associations of multiple exposures and outcomes.”
The paper relies on this without support.
Cross-sectional studies are useful for establishing preliminary evidence in planning a future advanced study.
“They are useful for establishing preliminary evidence in planning a future advanced study.”
The paper relies on this without support.
Cross-sectional studies are suitable for generating hypotheses and may provide information about the prevalence of outcomes and exposures that informs other study designs.
“They are suitable for generating hypotheses and may provide information about the prevalence of outcomes and exposures that informs other study designs.”
The paper relies on this without support.
Using questionnaires to reach a large sample of the population of interest is relatively inexpensive but can result in low response rates.
“Using questionnaires to reach a large sample of the population of interest is relatively inexpensive but can result in low response rates.”
The paper relies on this without support.
Interviews are more expensive and time-consuming than using questionnaires, potentially limiting the sample size but leading to a higher response rate.
“Interviews are more expensive and time-consuming than using questionnaires, potentially limiting the sample size but leading to a higher response rate.”
The paper relies on this without support.
Cross-sectional studies cannot study rare diseases.
“The weaknesses of cross-sectional studies include the inability to assess incidence, to study rare diseases, and to make a causal inference.”
The paper relies on this without support.
Probability sampling methods are more accurate and rigorous than nonprobability sampling methods.
“In general, probability sampling methods are preferred over nonprobability ones, as the former are considered to be more accurate and rigorous.”
The paper relies on this without support.
Nonresponse bias is usually encountered in cross-sectional survey studies with mailed questionnaires.
“A common type of selection bias is the nonresponse bias, which is usually encountered in crosssectional survey studies with mailed questionnaires.”
The paper relies on this without support.
Recall bias and detection bias are two common information biases.
“Recall bias and detection bias are two common information biases.”
Citations
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“24 A potential lack of external validity of the study was also considered, as this study was limited to data from Canada and only 1 of the Canadian provinces.”
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“Although cross-sectional data are useful for examining relationships among variables, they do not allow the temporal ordering of these relationships to be tested directly (X. Wang & Cheng, 2020).”
Janikian, M., & Benekos, I. (2026) · Journal of Child and Family Studies · doi:10.1007/s10826-026-03337-7
“The cross-sectional design prevents any causal inference regarding the directionality of associations between PSMU, family support, and SRH (Wang & Cheng, 2020).”
Barbry, A., Coquart, J. B., Verhoeven, C., et al. (2026) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk11030256
“Based on the main aim of this cross-sectional study, a sample size calculation was performed using G*Power 3.1 (University of Düsseldorf, Düsseldorf, Germany) for correlation analysis (bivariate normal model) [24,25].”
InfluentialFrom Research Questions to Study Design: Methodological Decisions in Multilingual Dementia Research
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“The sample size of 404 participants was determined using the modified Daniel’s formula for cross-sectional studies [19] with a population proportion of 50%, the 95% confidence level, and 5% margin of error.”
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“This combined analytical approach, involving both bivariate (chi-square) and multivariable (multinomial logistic regression) analyses, is widely used in cross-sectional studies to examine associations between variables and to control for potential confounding effects (18, 19).”
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“First, as this was a cross-sectional study, causal relationships between dietary patterns and health indicators could not be established [19].”
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Erdoğan, H., & Yakar, H. K. (2026) · Brain and Behavior · doi:10.1002/brb3.71573
“Furthermore, due to the cross-sectional design of this study, causal inferences cannot be made (Wang and Cheng 2020 ).”
van Diemen, L., Pépin, G., & Knight, R. (2026) · Australian Occupational Therapy Journal · doi:10.1111/1440-1630.70103
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KENTSEL SİYASET VE DEMOKRASİ: 1997–2024 YILLARI ARASI BİBLİYOMETRİK BİR ANALİZ
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“The cross-sectional design precludes causal inference, and self-reported well-being measures may intro - duce response biases (Wang & Cheng, 2020).”
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“The lower descriptive frequency of dual use among workers with MetS should not be interpreted as evidence of a protective or inverse association, as the cross-sectional design precludes establishing temporality or causality [26].”
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“However, these conclusions should be interpreted with caution given the cross-sectional and correlational design of the study (Wang & Cheng, 2020), which precludes causal inferences regarding whether masturbation enhances sexual functioning within partnered contexts.”
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“Therefore, causal interpretations are limited, and identifying changes over time cannot be explored (Wang & Cheng, 2020).”
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“A cross-sectional design was considered appropriate because the study aimed to examine associations among the study variables at a single point in time rather than changes over time (X. Wang & Cheng, 2020).”
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“Whilst cross-sectional studies are inexpensive and time efficient [122], they cannot establish temporal relationships between exposures and outcomes, precluding inferences on causality [123].”
Metadata
- Authors
- Wang, X., & Cheng, Z.
- Affiliations
- Cleveland Clinic (US)
- Zhongnan Hospital of Wuhan University (CN)
- Corresponding author
- Wang, Xiaofeng
- Venue
- CHEST
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 158
- Issue
- 1
- Pages
- S65-S71
- ISSN
- 0012-3692 (ISSN-L)
- OpenAlex
- W3039445169
- Cited by
- 2,214
- References
- 29
- Citations by year
- 2026: 636 · 2025: 638 · 2024: 423 · 2023: 282 · 2022: 162 · 2021: 61 · 2020: 8
- FWCI
- 90.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHEST Journal (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Nursing › Research and Theory › Methodologies in Health Research and Practice
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- cross-sectional study, observational study design, prevalence, health determinants, methodological issues, statistical analysis, respiratory medicine, intensive care, research design recommendations, peer review
- MeSH terms
- Cross-Sectional Studies, Humans, Guidelines as Topic
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