How do windows affect users' performance ? A study by Bury et al. (1985) comparing user performance in windowed systems to non-windowed systems revealed that task-completion times in windowed systems can be longer due to window arrangement time. However, in a detailed analysis, the actual times spent on task execution were found to be shorter, and the error rates were significantly lower in windowed systems. Bury's study revealed an important result that when users spend a lot of time doing window arrangement their performance may degrade, thus window arrangement times and the number of disruptions should be minimized.
Given two main stream organization styles (i.e. tiled and overlapped) which one yields more user performance ? Bly and Rosenberg compared user performance of tiled and overlapping window management strategies for regular and irregular tasks, where task regularity is determined by the layout of information in a window. Their results indicated that for regular tasks subjects using tiled windows performed their tasks faster. For irregular tasks, however, expert performance was faster in overlapping windows, whereas novice performance was faster in tiled windows. Lane at al.  also compared tiled and arbitrary overlap strategies for multi-window search tasks. Their results also indicated faster novice user performance for the tiled strategy.
Frequency of use for each window operation gives important information regarding users' interaction with a window manager. Gaylin observed that the number of window operations that are used to switch the active window set constitutes 63% of all the operations in an independent overlapped window manager. This result supports the findings by Bannon et al. that people switch among tasks frequently, forcing them to change the visible set of windows on the screen. According to Gaylin's observations, create and delete window operations accounted for about 15% of total operations, whereas move and resize operations accounted for 6%, with twice as many moves as resizes. Gaylin also measured window operation frequencies during log-on, as users set up their computers in a typical work configuration. Although, the most frequently used commands are still those used to switch the active windows, window creation operations accounted for 17%, move operation for 17%, and resize for 12%.
Card et al.  examined user interaction with a windowing system using the working set theory . According to their results the proportion of the recurrent windows is found to be 71.2%, and the average window working set size as 5. However, their study lacks experimental details such as number of users, tasks, window size, screen size, etc. which I believe effects the quantitative results considerably. However, their study is an important step and a good example for examining the effects of limited screen space and window usage analytically by sound techniques.
In most windowing systems users manipulate windows independently, one window at a time. Coordinated window management strategies can be more effective in situations when the information in multiple windows is related. Shneiderman et al.  examined hierarchical browsing as a window controlling strategy and their studies revealed improved performance with hierarchical browsing for program source code.