An alternative approach is to generate the data by you, for example, with a survey. In survey design, we have to pay attention to data sampling; that is, who the respondents are that are answering the survey. We only get data from the respondents who are accessible and willing to respond. Also, respondents can provide answers that are in line with their self-image and researcher's expectations.

Alternatively, the data can be collected with simulations, where a domain expert specifies the behavior model of users at a micro level. For instance, crowd simulation requires specifying how different types of users will behave in a crowd. Some of the examples could be following the crowd, looking for an escape, and so on. The simulation can then be run under different conditions to see what happens (Tsai et al., 2011). Simulations are appropriate for studying macro phenomena and emergent behavior; however, they are typically hard to validate empirically.

Furthermore, you can design experiments to thoroughly cover all of the possible outcomes, where you keep all of the variables constant and only manipulate one variable at a time. This is the most costly approach, but usually provides the best quality.