We will be conducting a project to asses the risk factors of developing type 2 diabetes in a young population through stats analysis of the data that we generate from volunteers in a cohort study of around 95 volunteers, it will involve data such as blood analysis, BMI, diet, age etc….
What statistical test using SPSS would be useful to include and why?
Primary statistical analysis would usually be logistic regression or a log binomial generalized linear model. The latter provides a measure of risk ratio for each predictor variable, the former, a measure of odds ratio. Both assume a binary (diabetes or not) dependent variable.
Primary statistical analysis would usually be logistic regression or a log binomial generalized linear model. The latter provides a measure of risk ratio for each predictor variable, the former, a measure of odds ratio. Both assume a binary (diabetes or not) dependent variable.
I agree with Adrian. I used to do the SPSS analysis for my projects.
I also agree with Adrian's suggestions. But given that your sample size is only 95, I fear that you are going to have too few 'events' to support the number of explanatory variables you are considering--e.g., if 10% of your subjects develop diabetes, you'll only have 9-10 events. To avoid over-fitting, you should have 15-20 events per variable. See Mike Babyak's nice article (link below) for more details. HTH.
https://people.duke.edu/~mababyak/papers/babyakregression.pdf
I found Mike Babyak's article illuminating. It highlights some common mistakes in data analysis.
I would suggest looking at the attached paper from the 2016 Proceedings of the Joint Statistical Meetings, Amer. Stat. Assoc. The protocol should be helpful. Wish you the best, David
Muhammad: Consider also the correlation between the response variable and the independent variable. If two independent variables are highly correlated with each other, remove the one with the lower correlation with the response variable. This is a basic approach, but it should work well.
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