What is a cross-sectional study example?
Cross-sectional study example 2: Men and cancer Another example of a cross-sectional study would be a medical study examining the prevalence of cancer amongst a defined population. The researcher can evaluate people of different ages, ethnicities, geographical locations, and social backgrounds.
What is the meaning of cross-sectional data?
Cross-sectional data are the result of a data collection, carried out at a single point in time on a statistical unit. With cross-sectional data, we are not interested in the change of data over time, but in the current, valid opinion of the respondents about a question in a survey.
What is cross-sectional data with examples?
Cross-sectional data refer to observations of many different individuals (subjects, objects) at a given time, each observation belonging to a different individual. A simple example of cross-sectional data is the gross annual income for each of 1000 randomly chosen households in New York City for the year 2000.
Why is cross-sectional study good?
Cross-sectional studies serve many relevant purposes, and the cross-sectional design is the most relevant design when assessing the prevalence of disease or traits, prevalence of attitudes and knowledge among patients and health personnel, in validation studies comparing, for example, different measurement instruments.
Why do we use cross-sectional study in research?
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.
Why do we use cross-sectional study?
Cross-sectional studies are used to assess the burden of disease or health needs of a population and are particularly useful in informing the planning and allocation of health resources. A cross-sectional survey may be purely descriptive and used to assess the burden of a particular disease in a defined population.
How is cross-sectional study done?
In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. After the entry into the study, the participants are measured for outcome and exposure at the same time [Figure 1]. The investigator can study the association between these variables.
What is wrong with cross-sectional studies?
The weaknesses of cross-sectional studies include the inability to assess incidence, to study rare diseases, and to make a causal inference. 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.
What do you mean by cross sectional study?
What is a cross-sectional study? Published on May 8, 2020 by Lauren Thomas. Revised on June 5, 2020. A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.
Can a causal inference be made in a cross sectional study?
Generally, as there is no time dimension involved in cross-sectional studies and therefore no time interval between “exposure” and “outcome”, causal inferences should not be made.
When to use cross sectional data for analytical purposes?
When cross-sectional data is used for analytical purposes, authors and readers should be careful not to make causal inferences, unless the exposure may safely be assumed to be stable over time. Cross-sectional studies are characterized by the collection of relevant information (data) at a given point in time.
What is survival bias in cross sectional studies?
Survival bias: occurs in cross-sectional studies when the exposure influences survival time, and the distribution of that exposure will be distorted among a sample of survivors. (a.k.a. Neyman bias, incidence-prevalence bias, or selective survival bias)