is simple, the “type” field may be sufficient. For example, an RCT may include the entire population in a location – all students in a district’s schools, etc. – in which case the sampling method is “1. Total universe (population)”; no further explanation is required. When sampling was carried out in multiple stages that involve different forms of probability selection (Option 2.5: Probability - Multistage), or using a mixed strategy (Option 4: Mix of probability and non-probability sampling), providing a description of the process is very helpful to data users. Note that _sampling type_ refers to the method for sampling randomization units. If different, information on the sampling of the _observational_ units may be provided in section V. The _Study sampling method: Description_ field also allows contributors to provide more detail on how the sample size was chosen (e.g., a sample determined by ex ante power calculations vs. an RCT at scale), and why targeted and actual numbers of randomization units may not be the same (see above).

_Example:_ The Indonesia study by Alatas et al. (2012) sampled randomization units using a stratified multistage design. The authors randomly selected 640 villages from the included three provinces based on a 30/70 urban/rural split, and then randomly selected one sub-village (neighborhood) from each village. On their own, the two sampling stages could be described as “2.3.1 Probability - Stratified: Disproportional stratified” and “2.1 Probability - Simple random,” respectively. In the CV, the contributor should select “2.5 Probability - Multistage” and describe the two selection stages for the village and sub-village in the free text field. Additional helpful context for the _Study sampling method: Description_ field could include that, even though the targeted and actual sample sizes are equal, five of the originally selected villages were replaced prior to the randomization for various reasons.

The other elements of the Study Design section provide additional information relevant for the variability and external validity of the treatment effect estimates. First, contributors can select the types of _covariates_ available in the data from a CV, with the decision to indicate types of available covariates rather than variable-level information aiming to not overly burden contributors. After testing different CVs, we propose a CV adapted from GESIS (Hoffmeyer-Zlotnik, 2016) to describe individual-level covariates, and a new vocabulary to describe covariates at the cluster level, such as household or other group-level characteristics. Information on covariates is needed for meta-analyses and can also be useful for methodological research. For instance, Tabord-Meehan (2018) uses data from an experiment on increasing charitable donations by Karlan and Wood (2017) to demonstrate a new method of adaptive stratification that uses information from a first experimental wave to select “stratification trees” in the second wave.

The next field of the schema includes a new optional field that describes which forms of treatment effects _the study was designed to analyze_ . This includes the “intent to treat” effect, average treatment effect, and local average treatment effect or average treatment effect on the treated. Note that the latter two imply that compliance with the treatment assignment is known (see below). A study that is designed to 15