Interpreting the Odds Ratio in Logistic Regression using SPSS
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- เผยแพร่เมื่อ 13 มิ.ย. 2016
- This video demonstrates how to interpret the odds ratio (exponentiated beta) in a binary logistic regression using SPSS with one continuous predictor variable. Converting odds ratio to probability is reviewed. A binary logistic regression returns the probability of group membership when the outcome variable is dichotomous.
Thank you for speaking cogently and not in cryptic, rambling circles as some do. Your passion for teaching shines:-)
You're welcome! Thank you for your kind words.
Dear Dr Todd,
Thank you very much for this informative video.
Useful, thanks! A few questions:
1. How do I interpret odds ratios if I natural-log-transformed the continuous variable first before the logistic regression?
2. I am comparing odds ratios for several different continuous variables that are on different orders of magnitude in scale. Before fitting a logistic regression model to each, I took the log (to fix the skew), subtracted the mean, and divided by the standard deviation of each variable. Is there a term for odds ratio of unit increase in the standardized ("z") variable? Are those odds ratios comparable between different variables now (ie, variable A and B can now be compared against each other using odds ratios to see which is more predictive)?
Thanks!
Hi Todd, your videos are very helpful, do you have any advice on the interpretation of interaction effects of two continuous predictor variables for binary logistic regression?
This is just great and many thanks!
This was very helpful, thank you!
You're welcome. Thanks for watching.
Awesome Dr. Grande, but what if you have more predictor variables do we we compute the opposite value of the dependent variable the same way as you described?
Hi Todd Thank you for sharing this video. I have a question. Can you tell me what is complex multilevel multinational logistics regression? how it is different from otherslogistic regression.. Thank you
is this a good explanation:
We would like to analyze whether understanding your colleagues has a relationship with the burnout risk. To analyze this, we used a Logistic regression analysis with K=2 (1=Yes, 2=No) because the dependent variable is nominal and the independent variable (understanding) is interval. The logistic regression analysis is not significant. As you can see in the table the significance is ,479. P>0.05 so we accept H0 and reject H1. For every 1 point that the ‘understanding’ increases, the probability of a burnout risk will increase with 0.818. So actually it will decrease with 18.2%. To conclude, understanding colleagues has no relationship with burnout-risk.
Thank you!
you saved my day, I burnt out my brain to understand the interpretation :D Thank you!
Can you please explain how to adjust odd's ratio, I mean for beginners for continuous as well as categorical data. Thanks and regards
How to calculate Odds ratio and upper and lower confidence interval in excel? pls help me
Dear Dr. Grande,
I get a 2x2 table for case-control study, but the case number in the cell (group=1、event=0) is 0. I cannot get an Odds Ratio by SPSS\Chi-square test. The results by performing logistic regression is also strange. How could I run SPSS with my dataset to reveal the correlation and the exposure risk between event =0 and =1? Thanks.
Hi Todd..The video was very helpful.
I am glad you found this video useful - thanks for watching.
Thanks I benefited
Thank you very much
does it have to be significant to report odds ratio?
Sir, if I get regression coefficient (b= -9.34), how to calculate the odds ratio?
good video, but for all demonstrative examples in the world you are choosing "2 outcomes fail and pass. with coping skills"...
still very informative!