LibGuides: Quantitative study designs: Cross-Sectional Studies (2024)

FAQs

Can a quantitative study be cross-sectional? ›

An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206).

Within a cross-sectional study a sample size of at least 60 participants is recommended, although this will depend on suitability to the research question and the variables being measured. A suitable number of variables.

These studies are conducted relatively faster and are inexpensive. However, due to the nature of study design, in general, it is difficult to derive causal relationships from cross-sectional analysis.

Most cross-sectional studies are quantitative. They gather data through interviews, questionnaires, and focus groups over a certain period in time which may be in the past or the present, and then analyze the results.

Cross-sectional research can be both qualitative and quantitative.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study.

If the research has a relational survey design, the sample size should not be less than 30. Causal-comparative and experimental studies require more than 50 samples. In survey research, 100 samples should be identified for each major sub-group in the population and between 20 to 50 samples for each minor sub-group.

If you test with too many, you're essentially throwing your money away. We want to strike the perfect balance — collecting enough data points to be confident in our results, but not so many that we're wasting precious research funding. In most cases, we recommend 40 participants for quantitative studies.

Small populations (less than 500): A larger sample size is generally recommended, aiming for at least 50% of the population. Large populations (greater than 5000): Smaller percentages suffice, typically between 17% and 27%.

Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study.

Why are cross-sectional studies unsuccessful? ›

However, it is important to be aware of the predictive limitations of cross-sectional studies: “the primary limitation of the cross-sectional study design is that because the exposure and outcome are simultaneously assessed, there is generally no evidence of a temporal relationship between exposure and outcome.”

The key features of a cross-sectional study are as given below: The cross-sectional study is carried out at a single moment by comparing different population . The research can examine a wide range of population-based on different factors such as age, earnings, and sex.

Cross-sectional studies can be used for both analytical and descriptive purposes: An analytical study tries to answer how or why a certain outcome might occur. A descriptive study only summarizes said outcome using descriptive statistics.

The number of people in a population with diabetes who are obese and the number of people in a population with diabetes who are not obese could be assessed using a cross-sectional design, this would be an example of an analytical cross- sectional study.

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.

This research then helps generate hypotheses for future study and ultimately leads to improvements in health and health systems. Within health care, most qualitative studies are cross-sectional.

Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables.

Revised on June 22, 2023. 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.