Hello Ernest Tewfik

Method selection allows you to specify how independent variables are entered into the analysis. Using different methods, you can construct a variety of regression models from the same set of variables.

• Enter. A procedure for variable selection in which all variables in a block are entered in a single step.
• Forward Selection (Conditional). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of a likelihood-ratio statistic based on conditional parameter estimates.
• Forward Selection (Likelihood Ratio). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of a likelihood-ratio statistic based on the maximum partial likelihood estimates.
• Forward Selection (Wald). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of the Wald statistic.
• Backward Elimination (Conditional). Backward stepwise selection. Removal testing is based on the probability of the likelihood-ratio statistic based on conditional parameter estimates.
• Backward Elimination (Likelihood Ratio). Backward stepwise selection. Removal testing is based on the probability of the likelihood-ratio statistic based on the maximum partial likelihood estimates.
• Backward Elimination (Wald). Backward stepwise selection. Removal testing is based on the probability of the Wald statistic.

The significance values in your output are based on fitting a single model. Therefore, the significance values are generally invalid when a stepwise method is used.

All independent variables selected are added to a single regression model. However, you can specify different entry methods for different subsets of variables. For example, you can enter one block of variables into the regression model using stepwise selection and a second block using forward selection. To add a second block of variables to the regression model

It seems that you are looking for a way of automated model selection and you ask which method to use. The answer in very simple terms:

Don't do it.

The analysis (and the significance in tests of parameters in your model) is contingent on the model. These tests have no meaning that would be helpful to "choose (or select) a model". You have to have a reasonable model as a context or frame in which you are able to value the information from the data w.r.t. the model parameters. Data does not tell you if a model is good. Theory does. So if you want some kind of model selection, then do it based on theoretical consideration, not because some parameter in some model is siginificant or fails to be significant in some test based on some observed data.

Take a look at the LOGISTIC REGRESSION section of the Algorithms manual, which you can find here

ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/26.0/en/client/Manuals/IBM_SPSS_Statistics_Algorithms.pdf

https://www.ibm.com/docs/ko/spss-statistics/23.0.0?topic=regression-logistic-variable-selection-methods

FAQ: How are the likelihood ratio, Wald, and Lagrange multiplier (score) tests different and/or similar? (ucla.edu)

Hi Ernest Tewfik

It seems you want to know the model selection procedure. Forward conditional means you will add one by one variable in the model and then check the sensitivity of the model using GOF test. You should keep an eye on the wald test's p-value as well. The best-fitted model's variables will be accepted as the final model. Then proceed for a colinearity check.

Best wishes