you can try this if adjustResize still fails to fully show the view you desire.  Whatever view your concerned about make a onTouchListener and then do this:

    setOnTouchListener(new OnTouchListener()  {



        Runnable shifter=new Runnable(){
            public void run(){
                try {
                    int[] loc = new int[2];                 
                    //get the location of someview which gets stored in loc array
                    findViewById(R.id.someview).getLocationInWindow(loc);
                    //shift so user can see someview
                    myscrollView.scrollTo(loc[0], loc[1]);   
                }
                catch (Exception e) {
                    e.printStackTrace();
                }   
            }}
        };

        Rect scrollBounds = new Rect();
        View divider=findViewById(R.id.someview);
        myscollView.getHitRect(scrollBounds);
        if (!divider.getLocalVisibleRect(scrollBounds))  {
            // the divider view is NOT  within the visible scroll window thus we need to scroll a bit.
            myscollView.postDelayed(shifter, 500);
        }



    });


//essentially we make a runnable that scrolls to a new location of some view that you WANT visible on the screen.  you execute that runnable only if its not within the scrollviews bounds (its not on the screen).  This way it shifts the scrollview to the referenced view (in my case 'someview' which was a line divider).  

EditText blocked by keyboard when it is clicked
What are the benefits of adding the variables into a model one by one, as compared to adding them all at the same time?

As I see in most research, the first model that is tested is consisted of all the control variables, then in the second model the first predictor is added, then the second one, and so on, until all predictors are in the model.

Are there any benefits in using this approach, or would it be ok to have just do two models - one with just the controls, and one with controls + all other predictors. 

(p.s.) - am interested in this with regards to logistic regression
