Hello,
I have conducted a logistic regression (method: forced entry) in R to analyze a group of independent variables (n=4). My criterion is a disease (yes/no). My sample size is n=105. I selected these variables based on theoretical consideration (some of them are known risk factors). My aim was to examine how well these variables predict the outcome and which of them are significant. My results indicate that only one of the 4 variables is significant (one trending).
Now I am wondering what might be a reasonable next step:
1) Is it appropriate to report the model "as it is" with R^2 (Nagelkerke etc.), a goodness of fit statistic (chi-square test), even if only one variable is significant? My idea behind this is to demonstrate the status quo and show that some variables indeed have no predictive power (at least in our data set)
2) In the next step (after looking at the first model), should I run a stepwise backward regression to find a model that best fits the data and contains only significant predictors? If I understand correctly, stepwise methods are more useful for exploratory analysis with a large sample size.
3) Can I just exclude all non-significant predictors in model 1, rerun my logistic regression analysis and report model 2 with the one significant predictor and perhaps the one trending (I'm not sure if this is actually a stepwise backward regression).
I'm looking forward to hearing from you and thank ypu all for your help!
Best reagards,
Nicole
1) Why not?
2) The choice of model should be based on logic first of all, not on "best fit" which might be accidental.
3) Are your independent variables correlated? Does your model have parameters to detect that?
I am always willing to take a look at your data.
Koen Van de Moortel Thank you for your fast reply!
1) I guess it is a general uncertainty to report statistics with non-significant results. But, on the other hand, that is the status quo.
2) That's reasonable! But I wonder if it is more useful to report a model that contains significant predictors only.
3) I checked the variance inflation factor and the tolerance of the predictors as recommended in "Discovering Statistics using R" (Field et al., 2012)
Thank you for your kind offer to review my data, but the data is not mine and I need to consult my supervisor first.
to more information following this link.
https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/binary-logistic-regression/interpret-the-results/key-results/
Nicole Walentek , The weird thing about significance is increasing your sample size will make everything in your model significant. Its better to pay attention to your effect sizes instead. Feel free to report everything but significance is overrated.
Rebaz Yahya Thank you very much for the link! It will help me understanding logistic regression better.
Anuraj Nayarisseri, indeed, paying attention at effect sizes is reasonable. In case of logistic regression, it is the odds ratio, right? Thank you, for you reply!
Nicole Walentek, Yes, You may check univariate logistics regression first. Also, you can try to remove the constant from the model :)
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