Sampling design

This is the landing page for everything related to how you select a subset of occasions and/or individuals from the total population of occasions and individuals you want to generalize your results to.

The way your sampling procedure is designed will directly impact what you will be able to capture about your construct. This depends for example on the distribution of your construct in reality, the presence of missing data, and how you select a subset of occasions and units from your population.

Below we have specified multiple articles where you can read more about 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: 2025-03-31