IJD
Indian Journal of Dermatology
  Publication of IADVL, WB
  Official organ of AADV
Indexed with Science Citation Index (E) , Web of Science and PubMed
 
Users online: 1898  
Home About  Editorial Board  Current Issue Archives Online Early Coming Soon Guidelines Subscriptions  e-Alerts    Login  
    Small font sizeDefault font sizeIncrease font size Print this page Email this page
IJD® MODULE ON BIOSTATISTICS AND RESEARCH METHODOLOGY FOR THE DERMATOLOGIST - MODULE EDITOR: SAUMYA PANDA
Year : 2016  |  Volume : 61  |  Issue : 3  |  Page : 261-264

Methodology series module 3: Cross-sectional studies


Department of Epidemiologist, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India

Correspondence Address:
Dr. Maninder Singh Setia
MGM Institute of Health Sciences, Navi Mumbai, Maharashtra
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-5154.182410

Rights and Permissions

Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case–control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed32166    
    Printed532    
    Emailed0    
    PDF Downloaded460    
    Comments [Add]    

Recommend this journal