How should I check the assumption of linearity to the logit for the continuous independent variables in logistic regression analysis? I am confused with the assumption of linearity to the logit for continuous predictor variables in logistic regression analysis. Do we need to check for the linear relationship while screening for potential predictors using univariable logistic regression analysis? 
In my case, I am using the multiple logistic regression analysis to identify factors associated with nutritional status (dichotomous outcome) among the participants. The continuous variables including age, Charlson comorbidity score, Barthel Index score, hand grip strength, GDS score, BMI etc. My first step is to screen for significant variables using simple logistic regression. Do I need to check for the linearity assumption during simple logistic regression analyses for each continuous variables? Or should I just check for it in the final multiple logistic regression model?
Besides, for my understanding, we need to transform the non-linear continuous variable before enter it into the model. Can I categorize the non-linear continuous variable instead of transformation? 
 A: I think that we should plot continuous variables and check for linearity before using them in a regression model. If linearity seems like a reasonable assumption, I think this will probably still hold in the final multivariable regression model in most cases, and if not, I think this might primarily be caused by interaction effects that you can correct for.
Yes, categorizing non-linear continuous variables is one option. The problems with this are that categories may in most cases seem arbitrary, and small differences in cut-off scores between categories may lead to different results (especially regarding statistical significance), and, depending on the number of categories and the size of your data, you may lose much valuable information in the data.
An alternative approach is to use a generalized additive model which is a regression model that can be specified as a logistic regression, but in which you can include non-linear independent variables as "smoother functions". Technically, this is not very complicated in R, but I don't know about other software packages. These models will identify non-linear relationships to the dependent variables, but a drawback might be that you won't get neat and tidy numbers in your output to present, but rather a visual curve that is tested for statistical significance. So it depends how interested you are in quantifying the effect of the non-linear variable on the outcome variable.
Finally, you can use generalized additive models as described above to test the assumptions of linearity in your logistic regression model, at least if you use R.
Take a look at this book (a very different field from yours, and mine, but that doesn't matter at all): http://www.amazon.com/Effects-Extensions-Ecology-Statistics-Biology/dp/0387874577/ref=sr_1_1?ie=UTF8&qid=1440928328&sr=8-1&keywords=zuur+ecology
A: Logistic regression does NOT assume a linear relationship between the dependent and independent variables.  It does assume a linear relationship between the log odds of the dependent variable and the independent variables (This is mainly an issue with continuous independent variables.)  There is a test called the Box-Tidwell that you can use for this. The stata command is boxtid.  I don't know the SPSS command, sorry. 
This may be of help --
http://www.ats.ucla.edu/stat/stata/webbooks/logistic/chapter3/statalog3.htm
A: As I describe in detail in my book Regression Modeling Strategies (2nd edition available 2015-09-04, e-book available now), the process of attempting to transform variables before modeling is frought with problems, one of the most important being the distortion of type I error and confidence intervals.  Categorization causes even more severe problems, especially lack of fit and arbitrariness.
Instead of thinking about this as a "check for lack of fit" problem, it is better to think of it as specifying a model that is very likely to fit.  One way to do this is to allocate parameters to the parts of the model that are likely to be strong and for which linearity is not already known to be a reasonable assumption.  In this process one examines the effective sample size (in your case the minimum of the number of events and number of non-events) and allows complexity to the extent that the data's information content allows (using e.g. the 15:1 events:parameter rule of thumb).  By pre-specifying a flexible additive parametric model one will only be wrong where it matters by omitting important interactions.  Interactions should be pre-specified, generally speaking.
You can check whether nonlinearity was needed in the model with a formal test (made easy with the R rms package) but removing such terms when insignificant creates the inferential distortions I outlined above.
More details may be found at course notes linked to from https://hbiostat.org/rms
A: Since I don't know your data I don't know if combining those three variables -- the basic variable, its natural log, and an interactive term --  will be a problem.   However, I know that in the past when I have considered combining three terms I often lose conceptual track of what I am measuring.  You need to have a good handle on what you are measuring or you'll have trouble explaining your findings.   Hope that helps!
