Table Of Content
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- What is the difference between a cross-sectional and longitudinal study?
- Multiple variables and outcomes can be researched and compared at once
- Prevalence Ratio/Risk Ratio and Excess Prevalence/Risk Difference
- General characteristics
- Advantages and disadvantages of cross-sectional studies
Third, the exclusion of pregnant women and individuals with speech and/or hearing impairments may have biased the study results. Culturally, Bangladeshi women do not go outdoors during pregnancy and experience a sense of embarrassment in conversations with unknown people, especially with adult men. An interviewer-administered survey involves verbal communication between interviewers and respondents, which is challenging for individuals with speech and hearing impairments. Finally, we included only those residents who were available at home during data collection.
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If the RR was less than 1, it implies that the exposure had a protective effect in that obese HIV participants were less likely to be sedentary than the unexposed group (not obese). All rights are reserved, including those for text and data mining, AI training, and similar technologies. Epidemiology is a branch of public health that views a community as the “patient” and various health events as the “condition” that needs treatment, according to the Centers for Disease Control and Prevention (CDC). Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented. These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.
What is the difference between a cross-sectional and longitudinal study?
The design provides a ‘snapshot” of the sample, and investigators can describe their study sample and review associations between the collected variables (independent and dependent). The observational nature makes it relatively quick to complete a study and provides data to support future studies that might lead to methods to treat or prevent diseases or conditions. The similarity in the ranking of simulation design sub-factor scores in this study and previous studies suggests that efforts are needed to strengthen the simulation design capabilities of Korean nurse educators. To increase fidelity, simulation technology support should be provided at the school level.
Multiple variables and outcomes can be researched and compared at once
The majority of research designs in psychology are cross-sectional designs as defined above. Typically, however, they are only explicitly referred to as such when contrasted with longitudinal designs. Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting.
When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice. A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study. A reporting guideline for cross-sectional studies is available for investigators and consumers of research to use.
During the debriefing process, learners who participated in the simulation acquire knowledge and skills about the simulation situation and can learn through reflection [7]. Well-designed simulation-based education improves clinical decision-making, promotes learning engagement, and increases satisfaction and confidence in learning [15,16,17]. A cross-sectional study is a type of observational research design that involves collecting data from a group of participants at a single point in time to assess various characteristics or variables of interest.
General characteristics
Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes. Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.
Create and Analyse a Cross-Sectional Study Survey
To prevent duplicate survey participation, only one response could be submitted per IP, and only one response per item was allowed to exclude multiple responses. The mobile phone numbers collected for sending rewards were used to check for duplicate participation. Cross-sectional studies don’t need a control group as the selected population is not based on exposure. A cross-sectional study is essential when researching the prevailing characteristics in a given population at a single point in time. Cross-sectional studies are often used to analyze demography, financial reports, and election polls. You could also use them in medical research or when building a marketing strategy, for instance.
Advantages and disadvantages of cross-sectional studies
Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM ... - Nature.com
Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM ....
Posted: Tue, 22 Aug 2023 07:00:00 GMT [source]
These responses indicate the frequency of individuals' feelings during the preceding two weeks, ranging from 0 to 3. The cumulative score spans from 0 to 27, with a higher value denoting a greater intensity of depression symptoms. A PHQ09 score of 10 or more was used to determine the presence of serious depression.
While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals connected by a common trait. In the present study, most of the residents (44.9%) preferred a smoke-free building policy for MUH. Although restricting smoking within buildings may help smokers quit smoking, it does not guarantee that they can stop smoking overnight. However, no existing study has focused on the relationship between BMI and mental health among nurses during COVID-19 pandemic. Therefore, we performed a cross-sectional study on a large population of nurses in China.
Evidence-based practice includes the integration of best available evidence, clinical expertise, and patient values and circumstances related to patient and client management, practice management, and health policy decision-making. A cross-sectional study does not need to have a control group, as the population studied is not selected based on exposure. Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease. Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects. Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment. Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.
However, determining which variable is the dependent and independent variable or cause and effect is difficult to determine. For example, the association between obesity and hours spent in sedentary behavior among HIV patients (see Table 1). Did the participant become obese due to sedentary behavior, or was the participant inactive due to obesity? According to Cummings et al., 2013, determining which variable to label as dependent or independent “depends on the cause-and-effect hypotheses of the investigator” (p. 85) or the biological plausibility rather than on the study design.
The survey was conducted by the nursing department, and it included a total of 3,450 nurses who were employed at the hospital. In the end, a grand total of 2,811 individuals supplied valid and useful responses, leading to an effective response rate of 81.49%. An ensemble of web-based surveys that individuals completed themselves was utilized. Participants successfully filled out a well-organized questionnaire within a time frame of 20 to 25 min. Figure 1 provides a visual representation of the specific information using a flow chart. 3) The relationship between simulation educational satisfaction, simulation design, and flow was analyzed by Pearson’s correlation coefficient.
In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies. This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.
After the entry into the study, the participants are measured for outcome and exposure at the same time [Figure 1]. It is also possible that the investigator will recruit the study participants and examine the outcomes in this population. The investigator may also estimate the prevalence of the outcome in those surveyed.
Primary outcome measure Multinomial logistic regression was used to identify the determinants of the choice of smoke-free policies for MUH. The data were analyzed using SPSS 22.0 for Windows, developed by SPSS Inc. in Chicago, IL, USA. The continuous variables were reported as the median together with the interquartile range. The mean of two continuous variables that follow a normal distribution was compared using the Student's t-test. The Mann–Whitney U test was employed to compare the average values of two continuous variables that do not follow a normal distribution.
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