What evidence level is a cross sectional study?

cross-sectional design

For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. A study by Sardana et al. evaluated the antibiotic resistance in isolates of Propionibacterium acnes in a tertiary care hospital in India. They recruited 80 patients of acne vulgaris, collected specimen for isolation from open or closed comedones. These specimens were then cultured, the growth identified, and antibiotic susceptibility and resistance were assessed.

How to perform a cross-sectional study

Pregnancy Concerns as Predictors of Sleep Quality in Primigravid Women: A Cross-Sectional Study - Cureus

Pregnancy Concerns as Predictors of Sleep Quality in Primigravid Women: A Cross-Sectional Study.

Posted: Sun, 03 Mar 2024 08:00:00 GMT [source]

Both systems place randomized controlled trials (RCT) at the highest level and case series or expert opinions at the lowest level. RCTs are given the highest level because they are designed to be unbiased and have less risk of systematic errors. I. A well-designed randomized controlled trial, where feasible, is generally the strongest study design for evaluating an intervention’s effectiveness. A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time. The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Measurement of covariates characteristics

The standards explain that simulation design influences simulation experience within the overall context of simulation education [4], which supports the findings of this study. Several studies with domestic and international nursing students have demonstrated that the better the simulation design, the higher the flow and satisfaction [15, 17, 30], suggesting that simulation design is critical to achieving positive outcomes in simulation-based education. The average score of 4.17 ± 0.45 was greater than the results of several studies on Korean nursing students [12, 16, 20, 29]. Thus, it suggests that Korean nursing education is moving toward simulation-based instructional design. The simulation educational satisfaction score in this study was 62.90 ± 6.93, which was higher than the scores of 57.12 ± 8.21 [8] and 57.26 ± 6.53 [17] in previous studies using the same scale. The higher the satisfaction with the major, the higher the satisfaction with clinical practice [25], and the higher the satisfaction with university life, the higher the simulation educational satisfaction.

Data collection and ethical considerations

The INACSL guidelines, revised in 2023, added professional development, suggesting that instructors should strive to provide high-quality simulation education that reflects the needs of learners with new knowledge related to simulation [4]. Therefore, in addition to theoretical preparation for professional development before starting simulation education, it seems necessary to participate in practical training to experience the role of learner and educator. In practice, cross-sectional studies will include an element of both types of design. Limitations of cross-sectional studies include the inability to make causal inferences, study rare illnesses, and access incidence. Are you considering whether a cross-sectional study is an ideal approach for your next research? Check out some of the key advantages and disadvantages of cross-sectional studies.

Cross-sectional study examples

Studies on nursing students in Korea have reported that nursing students who participated in simulation-based education felt nervous and anxious, overwhelmed and embarrassed, and guilty about making mistakes [5, 8, 9]. Stress and low self-confidence due to simulation-based education [10] and the idea that others are watching them lead to anxiety, resulting in passivity when participating in simulations with peers [11]. Additionally, high anxiety levels experienced during the simulation process can drive learners into a panic, which leads to negative learning effects and decreases simulation educational satisfaction [12, 13]. 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.

Characteristics of cross-sectional studies

A cross-sectional study (cross-sectional analysis or transverse study) offers an overview of a specific population in terms of an outcome at a given point in time. Cross-sectional studies are used in the fields of healthcare, social sciences, and economics. In this article, we cover cross-sectional study definition, its purpose, characteristics, and types of cross-sectional studies with examples.

Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget. An analytical cross-sectional study investigates the relationship between two related or unrelated parameters. Outside variables may affect the study while the investigation is ongoing, however. A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. Once the researcher has selected the ideal study period and participant group, the study usually takes place as a survey or physical experiment. We’ll explore examples, types, advantages, and limitations of cross-sectional studies, plus when you might use them.

Cross-Sectional Study Definitions, Uses & Examples

cross-sectional design

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. Analytic cross-sectional studies can provide the groundwork to infer preliminary evidence for a causal relationship (Mann, 2012). This design allows investigators to identify a population or sample and collect prevalence data to evaluate outcome differences between exposed and unexposed participants on a disease, phenomena, or opinion (Wang & Cheng, 2020). This design compares the proportion of participants exposed to the disease or phenomena of interest with the proportion of participants non-exposed with the disease or phenomena of interest (Alexander, 2015a).

Can Assess Multiple Variables

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group. As demonstrated in table 2, 94.8% of the respondents (97.8% non-smokers vs 82.6% smokers) wanted smoke-free housing, whereas 5.2% (2.2% non-smokers vs 17.4% smokers) did not want smoke-free housing. Work-related variables including employment, field of expertise, weekly working hours, and night shifts. Exposure to the COVID-19 pandemic pertains to nurses who may come into touch with patients suspected or confirmed to have COVID-19, or find themselves in a situation that necessitates COVID-19 quarantine.

The increasing obesity rates may have modestly increased the prevalence of depressive symptoms in the general population [18]. However, there is currently no data to explore the association between BMI and mental health among nurses during the COVID-19 pandemic. To fill this gap, we conducted a large cross-sectional study to explore the association between BMI and mental health among nurses in China during the COVID-19 pandemic. However, simulation-based education does not only bring the aforementioned positive outcomes to participating students.

A reporting guideline’s primary goal is to ensure that published clinical research studies provide transparency in reporting a study’s conduct (what was done) and results. The guideline is a tool investigators can use to develop their manuscripts and offers a checklist of inclusion items for a published paper (Equator.network). The recommended items will help ensure that a reader can understand the manuscript, follow the study’s planning and how the research was conducted, the findings, and the conclusions (von Elm et al., 2014). Whereas epidemiology is the study of disease occurrence and transmission in a human population, epidemiological studies focus on the distribution and determinants of disease. Epidemiology may also be considered the method of public health—a scientific approach to studying disease and health problems.

You can also use this type of research to map prevailing variables that exist at a particular given point—for example, cross-sectional data on past drinking habits and a current diagnosis of liver failure. The people in that extended family are used to determine what is happening in real-time at the moment. Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events. We encourage the readers to go through some of these studies to understand the design and analysis of cross-sectional studies.

Key terms in this definition reflect some of the important principles of epidemiology. Cross-sectional studies can be either qualitative or quantitative, depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

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