Poisson regression interpreting SPSS results (brief demo)
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- Опубликовано: 16 сен 2024
- This video briefly demonstrates Poisson regression in SPSS and interpretation of results. A copy of the data can be downloaded here:
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For videos and materials on a variety of statistical procedures, please check out:
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What options do we have if the omnibus test is non-significant?
I was told that IRR deals with the log count/expected count, but you state in the video that it’s more or less an odds ratios (increases risk). Is there an easy way to understand interpretation? For example, if I had an IRR of 2.011, with an IV of internet use (yes/no) and a DV of cybercrime victimization (a count measure), how would that be interpreted? Thanks!
Hello. Please see my newest presentation at ruclips.net/video/wBGprqJJlTU/видео.html. This may offer some additional details you may find useful. To your question: If you have an IRR of 2.011 for internet use (0=no, 1=yes) and with cybercrime (count) as your DV, this means that the count for cybercrimes for a person who has internet is 2.011 times that of the count for a person without internet. Or, if you wanted to express this in proportional terms: The number of crimes for a person with internet is 100%(2.011-1) = 100%(1.011) = 101.1% that of the number of crimes for a person without internet. Cheers.
Very nice elaboration
Thanks!
This one made my project happen
Very glad it was helpful! By the way, I also have additional videos on Poisson regression (see links below). I hope you check them out. Best wishes!
ruclips.net/video/Wo_QSeLV0Vk/видео.html
ruclips.net/video/DTPKiLGUuJE/видео.html
@@mikecrowson2462 I have looked at many of them, yes, thank you. All very clear and easy to follow. My interest here in this one was the IRR discussion. I am actually working with NB models due to over-dispersion. I would love to ask you some questions but I know you are a very busy man.
Nice video.
I have one question.
What i have to do to calculate the interaction between two factors and interpret the results?
Hi Mikel, you basically would have to compute an interaction term and add it into the model as a predictor. If the interaction term is significant, then you'd probe the interaction. This is a very long- and involved topic, so that is my short answer. FYI, I have a couple of newer videos on Poisson regression in SPSS you might look at on the general topic - although not directly related to your question per se: ruclips.net/video/LMusRSeYkWE/видео.html and ruclips.net/video/DTPKiLGUuJE/видео.html
A little recommendation: there's no "quick" or "easy" approach to probing significant interactions when running Poisson regression in SPSS. However, there is a very nifty program called Jamovi that will allow you to incorporate interaction terms into your model and test them for significance and to plot and test significance of simple slopes. I don't have a video on this particular topic. But I do provide some demonstration of jamovi in a few videos. I believe if you get the hang of things, you could do what you are asking in pretty short order. Here are some of the links: ruclips.net/video/texa0x3zKaM/видео.html and ruclips.net/video/C0mchKuor2I/видео.html and ruclips.net/video/bWC6LT0J6jA/видео.html . I hope you find this helpful.
I want to ask, how to report in percentage if the output of IRR (Exp B) reach the number 2? (2.188)
When there is no relationship between the predictor and the count outcome, the IRR = 1. So, an IRR = 2 can be re-expressed as a percentage change in the incidence rate per unit increase on the predictor. In general, this percentage can be computed as: 100%(IRR-1). If IRR=2, then you have 100%(2-1) = 100% The IRR grows by 100% per unit increase on the predictor. If your IRR = .8, then, the percentage change is: 100%(.8 - 1) = -20%, where this represents a 20% decrease in the IRR per unit increase on the predictor. Here is a link where they briefly given an example: stats.idre.ucla.edu/stata/dae/poisson-regression/ & Here's another: blogs.ubc.ca/datawithstata/home-page/regression/poisson-regression-2/
Hope this is helpful to you.
Nice explanation but poor utilisation of visual aid
Hi there, Linda. Actually, this is a much older demo. And I agree. Not the best visual (but hey, I was still learning, right? haha). Just an FYI, I have additional demos where you can download accompanying powerpoints and example data from the links underneath the video descriptions: I hope you give the a try. Cheers!
Poisson regression in SPSS (2019): ruclips.net/video/LMusRSeYkWE/видео.html
Poisson and negative binomial regression with offset variable (2019): ruclips.net/video/DTPKiLGUuJE/видео.html
I think you should have put gender in as a factor not a covariate. amirong?
Hi, actually because gender is dichotomous it would work either way. It's essentially a dummy coded variable and you'd get the same results for the regression coefficient if you treated it as a factor (assuming that you've set your reference category for 0). Thanks for asking.
Dear viewers: Please be sure to check out my most recent (June, 2023) video on Poisson and negative binomial regression. You will be able to download the data and a Powerpoint slide as part of the presentation. Go here: ruclips.net/video/wBGprqJJlTU/видео.html