Cross-Sectional Study in Research Examples & Design

Table Of Content

• Study limitation
• Prevalence Ratio/Risk Ratio and Excess Prevalence/Risk Difference
• Cross-sectional studies allow researchers to look at numerous characteristics at once (age, income, gender, etc.)
• Sciencing_Icons_Independent-Dependent Variables Independent/Dependent Variables
• What is a cross-sectional study in research?

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. 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.

Study limitation

By collecting relevant data from the sample, researchers can generalize their findings to describe the characteristics of the broader vaping population. One of the inherent limitations of cross-sectional studies is their inability to establish causality. Since data is collected at a single point in time, it is challenging to ascertain whether a relationship between two variables is causal or merely correlational. This limitation necessitates cautious interpretation of results, as establishing temporal precedence is essential for causal inference, which cross-sectional designs cannot provide. One of the primary benefits of cross-sectional studies is their cost-effectiveness compared to longitudinal studies.

Prevalence Ratio/Risk Ratio and Excess Prevalence/Risk Difference

Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [1, 2]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information.

Cross-sectional studies allow researchers to look at numerous characteristics at once (age, income, gender, etc.)

It is important to note that the results may be affected by “people entering or leaving the population due to births, deaths, and migration” (Cummings, 2013, p.88). Cross-sectional designs help determine the prevalence of a disease, phenomena, or opinion in a population, as represented by a study sample. Prevalence is the proportion of people in a population (sample) who have an attribute or condition at a specific time point (Mann, 2012) regardless of when the attribute or condition first developed (Wang & Cheng, 2020).

Overview: Cross-Sectional Studies

The online data forms for filling data had several quality checks including dosage, units, and that any data with inconsistence variables or errors was instantly rejected. Data analysis was done by experts at the University of Antwerp hence quality is guaranteed. Secondary AST data records for both in-patients and out-patients whose majority are cases that never responded to first line antibiotic treatment during the period of 2019 to 2023, were analyzed in this study.

Evidence Based Practice: Study Designs & Evidence Levels

You would not attempt to influence either group of individuals to modify their behavior or concerns. In other words, with cross-sectional research, the researcher tries to gather data without interfering in the results. In cross-sectional studies, researchers are responsible for designing and creating the tools involved in collecting cross-sectional data, but they do not manipulate the study environment. Explanatory cross-sectional studies go beyond identifying associations; they aim to explain why certain patterns or relationships are observed. These studies often incorporate theoretical frameworks or models to analyze the data within a broader context, providing deeper insights into the underlying mechanisms or factors.

A Cross-Sectional Study on Self-Medication Prevalence and Usage Patterns: An Alarming Concept Among the Saudi ... - Cureus

A Cross-Sectional Study on Self-Medication Prevalence and Usage Patterns: An Alarming Concept Among the Saudi ....

Posted: Wed, 14 Jun 2023 07:00:00 GMT [source]

This research allows scholars and strategists to quickly collect cross-sectional data that helps in decision-making and offering products or services. Let’s take a look in a little more detail at some of the key characteristics of cross-sectional studies. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Ines Maione brings a wealth of experience from over 25 years as a Marketing Manager Communications in various industries. And it never gets boring, because with the rapid evolution of the media used and the development of marketing tools, you always have to stay up to date.

The findings underline the need for strengthening antimicrobial stewardship programmes such AMR surveillance. According to the findings of the PPS, ceftriaxone and metronidazole emerged as the leading antibiotics prescribed in the wards. Monitoring of antibiotic use and resistance patterns underpin the effective implementation of AMS interventions in combatting AMR [20]. To date, few studies have assessed antibiotic use and antimicrobial resistance pattern in secondary referral hospitals in Malawi. In comparison with tertiary hospitals, secondary hospitals have inadequate staff and fewer resources to support precise diagnosis and rational use of antibiotics. However, there is limited data to provide insights on the antibiotic medicine related problems in secondary hospitals in Malawi.

Characteristics of Cross-Sectional Studies

If a significant number of men from a particular age group are more prone to have the disease, the researcher can conduct further studies to understand the reasons. A longitudinal study is best used, in this case, to study the same participants over time. However, it is important to remember that surveys designed to capture information about certain aspects of people's lives may not always result in accurate reporting and could cause biases.

Longitudinal studies involve monitoring the same individuals over time, which can be expensive and time-consuming due to the need for large sample sizes to account for churn rates over the study period. It can also take a while to get results because of the extended data collection phase. The POR is calculated similarly to the odds ratio (OR) (Alexander, 2015b) and referred to as POR when prevalence is used (Tamhane et al., 2016). OR measures the association between exposure and outcome (see Table 1) and denotes the chances that an outcome happens with a specific exposure, compared to the chances of an outcome happening in the absence of the exposure (Szumilas, 2010). This information helps both clinicians and investigators determine if certain factors (i.e., clinical characteristics, medical history) are a risk for a particular outcome (i.e., disease, condition).

Simulated education is an integrated process of knowledge, skill, and experiential learning for nursing students and is a teaching and learning strategy to facilitate nursing competencies [1]. The National Council of State Boards of Nursing recommends simulation use in nursing education since it can safely replace up to half of clinical practice training hours [2]. The Korean Accreditation Board of Nursing Education also allows simulation-based education to replace four credits of clinical practice credits in the third phase and up to six credits in the fourth phase [3]. In the first stage, a short screening survey was administered to a national sample of U.S. adults to collect basic demographics and determine a respondent’s eligibility for the extended survey of Asian Americans.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it. Prominent examples include the censuses of several countries like the US or France, which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites. When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice. Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.