Interpreting Observational Study

Interpreting observational studies from a student’s perspective
Observational study is when the researcher has no control over the variables. It is most commonly used fields such as epidemiology, social sciences, psychology, and statistics (McFalls, J., 2003). Observational study is often viewed as a research without manipulation because it is based solely on observation and/or how outcomes are measured. Based on the video, it presents two different types of observational studies; retrospective (the past). Which is any research conducted using data that already exist. And, prospective (here & now). Which is research conducted using data information that is present-day (MaestasMath, 2016). Although observational studies cannot be used to imply

This is an observational study, because the researcher is simply observing the answers of the survey without influencing the outcome in any way (Pennington, 2017). Another example of an observational study is determining the effects eating strictly organic foods has on overall health. The researcher finds 200 individuals where 100 of them have eaten organically for the past three years, and the other 100 haven't eaten organically in the past three years. They then give each subject an overall health assessment. Then based on information gathered, analyze the data, and use it to draw conclusions on how eating organically can affect one's overall health. This is an observational study, because the researcher hasn't done anything other than observe the individuals in the study (Pennington,

difficulty establishing clear chronology of exposure and outcome (McFalls, J., 2003). Next, Cohort which is specifically prospective and it consists of examining multiple health effects of an exposure; subjects are defined according to their exposure levels and followed over time for outcome occurrence. The advantages are you can evaluate multiple effects of a single exposure, it provides more efficient for rare exposures and outcomes with long induction and latency periods, you can directly measure incidence and it gives a clear chronological relationship between exposure and outcome (McFalls, J., 2003). The disadvantages are expensive, time-consuming and it is inefficient for rare outcomes with long induction or latency periods (McFalls, J., 2003). Lastly, ecological- examines relationship between exposure and outcome with population-level rather than individual-level data (usually defines groups by place, time, or both). The advantages are it is also inexpensive, less time-consuming, it is simple and easy to understand and it examines community-, group-, or national-level data and trends. The disadvantages are subject to the ecological fallacy, which infers association at the population level whereas one may not exist at the individual level, and it