Odds of an event occurring is the probability that the event will happen and this can be expressed as a proportion of the likelihood that the event will not happen. Odds ratio, in this case, is a ratio associated with exposure and an outcome. The odds ratio will identify that an outcome will happen given a particular exposure. Odds are easy to understand if they are expressed as a number that begins from zero to infinity, an event will never happen to an event is certain to happen respectively. Odds ratio are usually a measure of the size of an impact and may be reported in case-control studies, clinical trials, and cohort studies. Odds ratio are interpreted as being equivalent to relative risk. Relative risks are the likelihood of an event in relation to possible events to occur. This risk ratio is one of the probabilities (Davies, Crombie, & Tavakoli, 1998). One group compared with another is the ratio of the risks in the two groups, therefore the risk ratio identifies the extent of risk increased or decreased from an initial level. If the relative risk is below zero, it shows that the risk has been decreased and a relative risk of more than one show that the risk has been increased. The odds ratio is calculated in the same way with odds less than zero being decreased and if the odds ratio is greater than one that shows that, they have been increased.
The analysis of variance abbreviated as ANOVA is the statistical technique for testing if surveys are significant; they also compare the means of three or more populations. They help a researcher find out if he needs to reject the null hypothesis or accept the alternative hypothesis. The analysis test for significant differences between means (Davies, Crombie, & Tavakoli, 1998).Correlation is a measure that shows the extent to which more variables fluctuate together. A correlation that can be termed as a positive one show the extent to which variables increase or decrease in parallel. A negative one shows the extent a variable increases as the other variable decreases.
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T-tests and ANOVA are the same model and can be used for any problem that ANOVA and t-test can answer. The two methods use a single dependent variable and any number of independent variables, and the variable is continuous. Both a Chi-square and correlation do not separate variables into independent and dependent (Glass, 2006). Mostly a Chi-square is for categorical variables and used to test associations between two or more variables.
They type of study uses each of the above-explained analysis is the observational studies. Observational studies sometimes called epidemiological studies are those that identify which risk factors or exposures that can be associated with an increase or decrease of a risk that an individual will develop a disease like cervical cancer. In this kind of study, the researcher observes the participant and does not intervene. Observational studies can include a case-control study. This kind of study identifies a group of people who have a certain disease like cervical cancer they are the cases. A comparison group is identified and the group is without the disease and are referred to as controls. The second type of an observational study is the retrospective cohort study (Elliott, Fischer, & Rennie, 1999). The study first identifies two groups of individuals who are alike but do differ by a certain characteristic. For example, doctors who smoke and those that do not smoke. The researchers then go back in time and compare their medical records for a certain disease. If they find a common disease in those that smoke, they conclude that the disease is brought about smoking. The third type of study is a prospective cohort study. This study identifies a large number of people. They collect their information including medical records, observe them, and see what happens to their health. This type of study can use relative risks or ANOVA to conduct their study since the study involves observation and does not use any data from the target population. Results from epidemiological studies can conclude that association does not mean causation. Both the case-control study and the retrospective cohort study have the same limitation. Researchers may have to deal with family members of the people in the study if they are no longer alive.
Odds ratio in the medical literature is the measure of association between exposure and outcome. Relative risk, on the other hand, is a measure of association and be directly be determined in a cohort study. The odds ratio can be used in case-control studies and in a cohort study because the outcomes happen in less than 10 percent of the population that is unexposed. The odds ratio in that aspect provides an approximation that is reasonable of the relative ratio (Lindsay, 2011). The main difference between relative risk and the odds ratio is dependent on the risks or odds in both groups. Odds ratio will overstate the case when they are interpreted as relative risks and that will make the degree of overstatement increase. A serious divergence between the odds ratio and the relative risks occurs only with groups at high initial risk with large effects.
References
Davies, H. T., Crombie, I. K., & Tavakoli, M. (1998). When can odds ratios mislead? BMJ , 316 (7136), 989-991. doi:10.1136/bmj.316.7136.989
Elliott, R., Fischer, C. T., & Rennie, D. L. (1999). Evolving guidelines for publication of qualitative research studies in psychology and related fields. British Journal of Clinical Psychology , 38 (3), 215-229. doi: 10.1348/014466599162782
Glass, G. V. (2006). Primary, Secondary, and Meta-Analysis of Research. Educational Researcher , 5 (10), 3. doi: 10.2307/1174772
Lindsay, T. (2011). Statistical Correlation. Retrieved from https://explorable.com/statistical-correlation
