Cross-Sectional Studies (or ‘prevalence studies’) examine the data on disease and exposure at one particular time point. Due to the lack temporal evidence, cross-sectional studies cannot assess the cause and effect relationship.
In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. A cross-sectional study involves looking at data from a population at one specific point in time.
They may be conducted either before planning a cohort study or a baseline in a cohort study. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.
Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational. Researchers record the information that is present in a population, but they do not manipulate variables. A cross-sectional study functions as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.
Strengths of a Cross-sectional Study
Limitations of a Cross-sectional Study
"Longitudinal studies employ continuous or repeated measures to follow particular individuals over prolonged periods of time—often years or decades. They are generally observational in nature, with quantitative and/or qualitative data being collected on any combination of exposures and outcomes, without any external influenced being applied. This study type is particularly useful for evaluating the relationship between risk factors and the development of disease, and the outcomes of treatments over different lengths of time" (Caruana et. al., 2015).
Longitudinal Studies v. Cross-Sectional Studies:
"A longitudinal study, like a cross-sectional one, is observational. So, once again, researchers do not interfere with their subjects. However, in a longitudinal study, researchers conduct several observations of the same subjects over a period of time, sometimes lasting many years. The benefit of a longitudinal study is that researchers are able to detect developments or changes in the characteristics of the target population at both the group and the individual level. The key here is that longitudinal studies extend beyond a single moment in time. As a result, they can establish sequences of events" (Institute for Work & Health, 2015).
Further Reading:
Bias:
"A pilot study is a small feasibility study designed to test various aspects of the methods planned for a larger ,more rigorous, or confirmatory investigation. The primary purpose of a pilot study is not to answer specific research questions but to prevent researchers from launching a large-scale study without adequate knowledge of the methods proposed; in essence, a pilot study is conducted to prevent the occurrence of a fatal flaw in a study that is costly in time and money.
A well-planned and executed pilot study also may help researchers identify potential confounding variables that were not previously known and evaluate the strength of relationships among key variables to aid in the calculation of sample size (Polit&Beck,2017).In addition, researchers may use pilot studies to refine training strategies for research personnel and determine whether preliminary findings support a larger, more rigorous investigation" (Lowe, 2019).
"Questionnaire surveys are a technique for gathering statistical information about the attributes, attitudes, or actions of a population by a structured set of questions. Administered by mail, in person, through the Internet, and over the telephone, questionnaire surveys provide broad coverage of populations enabling us to explore spatial and social variations in people's attributes, attitudes, and actions. The aim is to obtain information suitable for statistical analysis, so attention is paid to how respondents are selected, the extent to which questions relate to underlying concepts, and completion rates. The information obtained from questionnaire surveys is constructed through the process of designing and administering the questionnaire and compiling the data that result. The design of the questionnaire affects all subsequent stages of data collection and analysis" (Preston, 2009).
Further Reading:
Bias: "Survey bias is an aspect of a survey that has a negative effect on the outcome of the results. It essentially means that some aspect of your survey could have swayed respondents into answering a certain way or providing certain feedback" (Quantilope, 2023).
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