Sampling design

This is the landing page for everything related to how you select a subset of occasions and a subset of cases (e.g., persons) from all possible occasions and the population that you want to generalize your results to.

For instance, sampling faster and more frequently allows you to see more detail in the fluctuations of a process over time; sampling for a longer period, allows you to see whether the process is characterized by slow changes over time; and sampling multiple cases allows you to see whether the process is characterized by individual differences.

Below we have specified multiple articles where you can read more about important aspects to consider when designing your sampling scheme.

Think more about your sampling design

Selecting a subset of occasions

The timing of your measurement occasions is essential to what patterns you may capture from your process of interest.

Selecting a subset of cases

The characteristics of the participants determine to what extent your results are generalizable to the population of interest.

Missing data

Missing data may be introduced due to the nature of the sampling procedure or participant noncompliance.

  • [Missing observations]
  • [Night gaps in sampling designs]
How your construct is distributed in reality

Ideally, your sample distribution closely resembles the real-world distribution of your construct.


Noémi K. Schuurman

Last modified: 2025-05-09