Cross sequential study4/19/2023 ![]() ![]() Multiple variables and outcomes can be researched and compared at once Minimal room for errorīecause all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained. These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys. They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population. ![]() The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population. In analytical cross-sectional studies, researchers investigate an association between two parameters.While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating. 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.Ĭross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.įor example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time. In this type of study, researchers are simply examining a group of participants and depicting what already exists in the population without manipulating any variables or interfering with the environment.Ĭross-sectional studies aim to describe a variable, not measure it. Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population. ![]()
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