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Descriptive vs. Analytical Study Designs

Descriptive vs. Analytical Study Designs

Discuss why cross-sectional studies would be considered “descriptive” study designs rather
than “analytic” study designs. Discuss an example of a disease where survival could
influence the association between a possible exposure and the disease when measured with
a cross-sectional study. Do not discuss examples used in the textbook.

Descriptive vs. Analytical Study Designs

In epidemiological research, cross-sectional studies are considered “descriptive” rather
than analytical study designs because they focus on providing data obtained from the general
population under study while the latter includes individuals with specific characteristics.
Moreover, cross-sectional studies are descriptive because they facilitate the determination of the
odds ratio, absolute risks (AR), and relative risks (RR) from the prevalence rates. Similarly,
descriptive study designs focus on generating hypotheses and answering questions such as what,
when, where, and who. On the other hand, analytical studies seek to test the hypotheses and
proving answers on why and how phenomena occur. According to Omair (2015), descriptive
study designs include qualitative studies and surveys (cross-sectional), case-series, and reports
whose objective entails measuring the frequency of several factors and the size/magnitude of the
problem. Besides, descriptive studies aim at providing a clear picture of what is happening in a
population through determining the incidence, prevalence, and experiences of a group as
opposed quantifying the relationship between two factors (the effect of an intervention or
exposure on a particular outcome), as is the case in analytical studies.

Nevertheless, cross-sectional studies are associated with some element of criticism based
on the provision of limited causal inference that occurs as a result of the concurrent assessment
of exposure and health outcomes. According to Omair (2015), such studies are prone to a reverse
causation bias that involves the exposure status becoming an effect of the disease as opposed to
the cause. For instance, the transition from one department to another or leaving employment due
to the development of a respiratory illness associated with dust may be considered as a case in
which survival influences the association between a possible exposure and the disease when
measured with cross-sectional study design. The bias, in this case, could lead to an
underestimation or missed association if the most heavily exposed and the severely affected
workers have left employment and thus unavailable for the study (Omair, 2015). Moreover, the
attempt to identify and include such workers could lead to bias that makes the study prone to
worker survivor effect.



Omair, A. (2015). Selecting the appropriate study design for your research: Descriptive study
designs. Journal of Health Specialties, 3(3), 153.

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