If the adjusted odds ratio (AOR) is higher than the crude odds ratio (COR), it implies that there is a confounding effect present in the relationship between the predictor variable and the outcome variable. A confounding variable is a variable that is associated with both the predictor variable and the outcome variable. It can distort or bias the estimated relationship between the predictor and the outcome if it is not properly accounted for in the analysis. When the AOR is higher than the COR, it suggests that the confounding variable has an impact on the relationship being studied. The AOR takes into account the influence of the confounding variable by controlling for it in the analysis, thereby providing a more accurate estimate of the direct relationship between the predictor variable and the outcome variable. In practical terms, a higher AOR than COR could mean that the initially observed association between the predictor and outcome variables was partially or fully explained by the presence of the confounding variable. By adjusting for the confounding variable, the AOR helps to separate the true relationship between the predictor and outcome from the influence of the confounding variable.
For the COR/OR, when your run your regression, you run one to one between your outcome and explanatory variable, but When you do AOR because you are trying to control confounding factors, you run your regression between your outcome variable and all those predictors which found to be significant during your first one to one comparison.
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Please always try to do this in english which is a language for the majority
not visible and clear
what if we get higher AOR than the COR? does it have an implication?
If the adjusted odds ratio (AOR) is higher than the crude odds ratio (COR), it implies that there is a confounding effect present in the relationship between the predictor variable and the outcome variable.
A confounding variable is a variable that is associated with both the predictor variable and the outcome variable. It can distort or bias the estimated relationship between the predictor and the outcome if it is not properly accounted for in the analysis.
When the AOR is higher than the COR, it suggests that the confounding variable has an impact on the relationship being studied. The AOR takes into account the influence of the confounding variable by controlling for it in the analysis, thereby providing a more accurate estimate of the direct relationship between the predictor variable and the outcome variable.
In practical terms, a higher AOR than COR could mean that the initially observed association between the predictor and outcome variables was partially or fully explained by the presence of the confounding variable. By adjusting for the confounding variable, the AOR helps to separate the true relationship between the predictor and outcome from the influence of the confounding variable.
How I can doing Odd ratio and AOR in SPSS
For the COR/OR, when your run your regression, you run one to one between your outcome and explanatory variable, but When you do AOR because you are trying to control confounding factors, you run your regression between your outcome variable and all those predictors which found to be significant during your first one to one comparison.
can you please make this in English language?
Dear/Sir I made the video in English, check out the channel
@@ethioresearch693 Thank you but i already solved my problem... and still i will check it.. Thank you so much