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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“Wang et al. further demonstrates that incorporating patient-reported outcome (PRO) data into clinical practice can enhance symptom recognition and guide more effective management strategies [32].”
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“Third, the cross-sectional design limits the ability to draw causal inferences among (X. Wang & Cheng, 2020) job demands, resources, calling, well-being, and intent to stay.”
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“…employed research designs in nursing is the descriptive survey study, among which cross-sectional studies are particularly useful for examining participants’ characteristics, health status, attitudes, and behaviors at a specific point in time and for exploring relationships among variables [11,12].”
Song, Y., Wang, Z., Lin, J., Li, B., Zhang, H., Qiao, G., & Zhang, L. (2026) · BMC Psychology · doi:10.1186/s40359-026-04260-8
Ongoh, M., Abekah‐Carter, K., A-iyeh, E., Awuviry‐Newton, K., & Agyemang‐Duah, W. (2026) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-026-05320-7
Non-Conventional and Emerging Autoantibodies in Sjögren’s Disease
Lee, A. K. Y. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s593107
Song, G., Bhatti, R., Falk, J., et al. (2026) · American Journal of Pharmaceutical Education · doi:10.1016/j.ajpe.2026.101962
Hamrouni, M., Gupta, A., Middleton, S., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344210
“The present study is also susceptible to Neyman (prevalence-incidence) bias [36], as it is a referral-based, cross-sectional analysis restricted to non-hospitalised individuals.”
Nyamekye, E., Mutawakil, A.-R., Nketsiah, J., et al. (2026) · Journal of Multilingual and Multicultural Development · doi:10.1080/01434632.2026.2638857
Hassan, N. S., Osman, R., Sabri, A. S. A., et al. (2026) · Journal of Health and Allied Sciences NU · doi:10.25259/jhasnu_131_2025
Dorgham, S. R., Alqahtani, F. M., Almahmoud, S. A., et al. (2026) · Nursing Forum · doi:10.1155/nuf/5424190
Zahra, A. F., Yuniarni, U., & Laksono, B. T. (2026) · Pharmacy Reports · doi:10.51511/pr.109
Touzet, H., Giry, B., & Ward, J. K. (2026) · Sociology of Health & Illness · doi:10.1111/1467-9566.70170
“Firstly, it has the usual limits of cross‐sectional studies, well exposed by Wang and Cheng (2020).”
Green, M. N., Clinkscales, A., & Caballero, A. L. (2026) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-026-02875-6
Wibowo, B. A., Hadi, V. Z. S., Widjaja, S., & Wibowo, A. J. I. (2026) · International Journal of Sociology and Social Policy · doi:10.1108/ijssp-01-2025-0058 · Cited by 1
Zhang, W., Cao, X., Wu, Z., Liu, J., Li, Y., Zhang, L., Wang, Y., Jackson, T., Xiang, Y., & Yang, B. (2026) · Child Neuropsychology · doi:10.1080/09297049.2026.2638879 · Cited by 2
“First, due to the cross-sectional study design, inferences cannot be drawn about causal effects of executive functions versus functional impairments in the network model (Chew et al., 2024; X. Wang & Cheng, 2020; M. Wang et al., 2024).”
Loneliness Is Becoming an Epidemic Among Youth in Urban Nepal: A Cross-Sectional StudyPreprint
Bhusal, J., & Bhatta, K. (2026) · Research Square · doi:10.21203/rs.3.rs-9014423/v1
Zhang, J., & Lam, L. T.-M. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1693367
“Given the cross-sectional design, the study focuses on associations between potential factors and mental well-being, rather than causal or mediational pathways, which require longitudinal or experimental data to establish temporality (24, 25).”
Goto, J., George, C., & Lautenbach, G. (2026) · Discover Education · doi:10.1007/s44217-026-01312-4
Cronwright, A. R., Lascu, A., Hinck, K., Kenneally-Dabrowski, C., Dwyer, D., & Feros, S. A. (2026) · International Journal of Sports Science & Coaching · doi:10.1177/17479541261421423
Gomes, C. D. O., Nogueira, L. B., Goulart, P. C., et al. (2026) · European Archives of Paediatric Dentistry · doi:10.1007/s40368-026-01171-5
“…benefits both patients and clinicians, as timely identification enables a The primary limitations of this study are inherent to its cross-sectional design (Wang and Cheng 2020), particularly the inability to establish causal relationships or assess long-term outcomes associated with SST or MST.”
Unlocking customer actual behavior: exploring omnichannel dimensions with brand perceived value
Asif, M., Hashim, M., Baig, S. A., & Sibtain, M. M. (2026) · Future Business Journal · doi:10.1186/s43093-026-00779-9 · Cited by 1
Edwards, O. V., & DeMott, M. R. (2026) · Journal of College Student Retention Research Theory & Practice · doi:10.1177/15210251261425187 · Cited by 1
Korfii, U., Ajayi, O., Oluwasanmi, O. O., et al. (2026) · Journal of Public Policy and Administration · doi:10.11648/j.jppa.20261001.20
Ng, J. Y., Anheyer, D., & Cramer, H. (2026) · Pain Practice · doi:10.1111/papr.70140 · Cited by 1
Kero, J., Koivisto, J., Stolt, M., et al. (2026) · BMC Nursing · doi:10.1186/s12912-026-04540-z
Mariaca, H. A. L., Blokhuis, C., & Zhu, Y. (2026) · European Journal of Nutrition · doi:10.1007/s00394-026-03940-w
SAĞLIK İLETİŞİMİ SORUNLARININ HEKİM ALGISINA YANSIMASINDA SİSTEM GÜVENSİZLİĞİNİN ARACI ROLÜ
Yalçın, B. (2026) · Hacettepe sağlık idaresi dergisi · doi:10.61859/hacettepesid.1740252
Graham, M. A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1705173
Adu, I. K., Owusu, V. B., Amoako, E. K., Attoh, G. N. A., Asiedu, E. K., Banyeh, M., Alhassan, I. Z., & Amidu, N. (2026) · American Journal of Men s Health · doi:10.1177/15579883261430887
“Cross-sectional designs are sensitive to measurement and recall bias, especially when relying on self-reported data, and they provide only a snapshot in time, limiting their usefulness for assessing changes, trends, or long-term effects (Wang & Cheng, 2020).”
Adoma, P. O. (2026) · Research Connections · doi:10.1093/rescon/vmag024
Development and validation of the Running Injury Epidemiology Questionnaire (RIEQ)
Nyawose, Z. Z. (2026) · African Journal for Physical Activity and Health Sciences (AJPHES) · doi:10.37597/ajphes.2026.32.1.7
Ghilardi, S., Colombo, F. M., Bagardi, M., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0335420
“In contrast, longitudinal studies track changes within individuals over time, capturing dynamic processes, temporal ordering, and causal inference with greater precision [43–46].”
Spiteri, S., Teychenne, M. J., Porter, J., Brown, W. J., & van der Pligt, P. (2026) · Women s Reproductive Health · doi:10.1080/23293691.2026.2637757
Eltaib, F. A., Alenezi, I. N., Mersal, F. A., et al. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1799059
Ruzhani, F., & Mushunje, A. (2026) · Agrekon · doi:10.1080/03031853.2026.2638284
“We collected data from both beneficiary and non-beneficiary farmers of maize input subsidies using a cross-sectional survey design, which involves gathering data from different individuals at a single point in time (Wang and Cheng 2020).”
Maddock, J. E., Eyler, A. A., Yeh, S.-Y., Chen, Y., Quartey, J., & Johnson, S. S. (2026) · American Journal of Health Promotion · doi:10.1177/08901171261437950 · Cited by 1
Adaptation of Bachelor’s and Master’s Students in Engineering to a Changing Environment
Nugmanova, D. R., Kozlova, I., & Kupriyanov, R. V. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014190
Briot, K., Lemoine, S., Cortet, B., et al. (2026) · JBMR Plus · doi:10.1093/jbmrpl/ziag050
“39 While it may be susceptible to recall bias, this was not an issue in the current study because medical history data werecapturedretrospectivelyfrompatientsmedicalrecords.”
Granular Insights From Local Data: Identifying Health Education Needs of Appalachia Kentucky
Omotilewa, O. O., Adedokun, O. A., & Norman‐Burgdolf, H. (2026) · Health Education & Behavior · doi:10.1177/10901981261435493
Vig, S., & Tewary, T. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03043-w · Cited by 1
Combo, A. K., & Moon, C. (2026) · Current Psychology · doi:10.1007/s12144-026-09324-x
“However, we recognize that this design restricts the ability to draw firm conclusions about causality in the mediation model (Winer et al., 2016; Wang & Cheng, 2020 ).”
The Relationship Between Pattern Skills and Computational Thinking Skills in Young Children Aged 5–6
Bayık, R. N., & Ceylan, M. (2026) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-026-10662-2
Passion's ripple effect: team entrepreneurial passion impacts well-being via performance
Maryami, S., Pattnaik, S., Loi, M., & Opizzi, M. (2026) · Journal of Small Business and Enterprise Development · doi:10.1108/jsbed-09-2025-0620
COVID-19 Anxiety and Death Anxiety: The Predicting Role of Death Anxiety
Demirtaş, Z., Arslan, N., Arslan, S., Kartol, A., & SARIŞIK, S. (2026) · Cyprus Turkish Journal of Psychiatry and Psychology · doi:10.35365/ctjpp.26.1.02
Prevalence and predictive factors of long COVID in nurses in Brazil
Júnior, J. F. M., Sousa, L. R. M., Pedroso, A. O., et al. (2026) · BMC Infectious Diseases · doi:10.1186/s12879-026-13176-y
Kathare, J. M., Mbaria, J., Kaluwa, C., Mapenay, I., & Moriasi, G. A. (2026) · International Journal of Pharmacy Practice · doi:10.1093/ijpp/riag045
Torbey, A., Kahal, F., Kadri, S., et al. (2026) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2026.03.047
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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