Exploring Cross Sectional Study: A Comprehensive Guide with Examples
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Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book." Like any research design, cross-sectional studies have various benefits and drawbacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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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). Additionally, each study participant’s evaluation is completed at one time-point with no follow-ups (Cummings, 2013), providing a ‘snapshot’ of the sample. Cross-sectional designs can be implemented as an interview or survey and may also collect physiological data and biological samples.
Measurements in a Cross-sectional Study
Cross-sectional studies are often used to generate hypotheses, identify trends, and inform public health policies and interventions. Whether you’re building a marketing strategy or performing a cutting-edge medical study, you can get started by creating an intuitive survey from QuestionPro. Please choose from one of our 350+ survey templates, or build your own and leverage our reporting tools to discover deep insights to apply to your best work.
WHAT IS A DESCRIPTIVE STUDY?
This timeliness is particularly beneficial for informing immediate policy decisions or for studies in fields where trends may change rapidly, such as technology or public health. Educational researchers often use a cross-sectional design to evaluate student performance across different grades or age groups at a single point in time. Such a study could compare test scores to analyze trends and disparities in educational achievement.
Subgroup Analysis
These are very good for measuring the prevalence of a disease or of a risk factor in a population. Thus, these are very helpful in assessing the disease burden and healthcare needs. QuestionPro provides various tools for analyzing your collected data, cross-tabulation, and more. Whether you’re a researcher, marketer, or business professional, QuestionPro can help you gather the data you need to make informed decisions. 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 are the limitations of a cross-sectional study?
For example, a sample of college students may allow comparisons within males and females in that sample, but it will be difficult to say the results apply to older populations or non-college students. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Cross-sectional studies equip scholars and policymakers with actionable data that can be acquired quickly, facilitating informed decision-making and the development of products or services. By Kendra Cherry, MSEdKendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book." Cross-sectional studies are popular because they have several benefits that are useful to researchers.
Cross sectional study on food safety knowledge, attitudes, and practices of food handlers in Lahore district, Pakistan - ScienceDirect.com
Cross sectional study on food safety knowledge, attitudes, and practices of food handlers in Lahore district, Pakistan.
Posted: Wed, 17 Nov 2021 21:02:08 GMT [source]
What are the advantages and disadvantages to consider when using a Cross-Sectional study design?
In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time. A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas cohort studies follow individuals over extended periods.
Researchers usually use descriptive and analytical research methods in real-life cross-sectional studies. A cross-sectional study allows researchers to make comparisons among different groups within the sample, but is not particularly useful for analyzing changes over time. The majority of research designs in psychology are cross-sectional designs as defined above.
Similarly, if the OR was less than 1, it implies that the exposed (obese) group, were less likely to be sedentary (outcome) compared to the non-obese group (unexposed) (Tenny & Hoffman, 2019). In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease. Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.
The reason is that most of the data here are from self-report surveys by a suitable participant group. Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available.
Moreover, researchers maximize their use of information because there are no time variables here. Conducting a successful cross-sectional study requires careful planning, meticulous execution, and an understanding of potential challenges. In this guide, we explore the intriguing realm of cross-sectional studies, unveiling their power to provide a snapshot of the present. Whether you're a curious novice or a seasoned researcher seeking to grasp the intricacies of this methodology, join us in unraveling the secrets of cross-sectional studies. Also, you can find advanced data analysis tools such as trend analysis and dashboards to visualize your information and do your own cross-sectional studies simply and efficiently.
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. Cross-sectional research differs from longitudinal studies in several important ways.
For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.
One of the biggest pros of cross-sectional study is the excellent control it gives to the researchers. Additionally, they don’t have to care about long-term considerations and there’s a specified period for which the data is collected. Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies. In the first three of these, data are collected on individuals, whereas the last one uses aggregated data for groups. They are valuable for understanding the current status of a condition or behavior within a population, making them great for initial assessments. Researchers prefer cross-sectional studies to find common points between variables.
For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further. 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. Selection bias can occur in cross-sectional studies if the sample is not representative of the population from which it was drawn. This can happen due to non-random sampling methods or non-response, leading to skewed results that do not accurately reflect the broader population.
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