| A typical survey can be conducted at relatively low costs | ✓ | ✓ | | |
| Data can be collected during quick sessions with participants | | ✓ | ✓ | |
| Low frequency of sessions required by patients | ✓ | ✓ | ✓ | |
| Relatively quick delivery of preparation, data collection, and analysis | ✓ | ✓ | ✓ | ✓ |
| A large number of attributes can be explored | | | | ✓ |
| Suitable to study preferences in a small sample size | ✓ | ✓ | | ✓ |
| A low cognitive load on patients | ✓ | ✓ | ✓ | ✓ |
| Does not need an education tool or preparatory instructions in order to enhance participant comprehension | | ✓ | ✓ | |
| Publically acknowledged by your organisation’s guidelines as an acceptable method to study preferences | | | ✓ | ✓ |
| New attributes can be added without making prior results invalid | ✓ | ✓ | ✓ | |
| Can be used to collect data from more than one participant in a single session | | | ✓ | |
| The analysis can calculate risk attitudes, like risk tolerance, and calculate how value functions bend due to the presence of uncertainty in the participant | ✓ | ✓ | ✓ | ✓ |
| Explores the reasons behind a preference in detail | ✓ | ✓ | ✓ | ✓ |
| Can estimate weights for attributes | ✓ | ✓ | ✓ | ✓ |
| Estimates trade-offs that patients are willing to make among attributes | ✓ | ✓ | ✓ | ✓ |
| Can quantify heterogeneity in preferences | ✓ | ✓ | ✓ | ✓ |
| Internal validity can be established | ✓ | ✓ | ✓ | ✓ |
| External validity can be established | ✓ | ✓ | ✓ | ✓ |
| Outcomes can refer to a course of health over time (as opposed to a constant health state) | | | ✘ | ✘ |
| Sensitivity analysis is possible | ✘ | ✘ | ✘ | ✘ |
| Can combine quantitative and qualitative methods | | ✘ | ✘ | ✘ |
| Applies validation tests | ✘ | | ✘ | ✘ |
| Results can be reproduced by an (independent) researcher for reproducibility | ✘ | ✘ | ✘ | ✘ |
| Applies tests for consistency | | | ✘ | ✘ |
| Can be conducted without the need for specialized software (beyond Excel) | | | | |
| Can be conducted without programming skills | | | | |
| Researcher does not need to supervise the data collection | | | | |
| Does not require hypothetical scenarios | | | | |
| Attributes and attribute levels can be determined as part of the method itself (internal identification) | | | | |
| Data saturation can be achieved relatively quickly | | | | |
| Does not require model estimations | | | | |
| Outcomes can be expressed in a particular format (e.g. probability scores, marginal rates of substitution, monetary values) | | | | |
| Outcomes can refer to a constant health state (as opposed to a course of health over time) | | | | |
| Uses respondent validation by asking participants to check their data or responses | | | | |
| Validates through triangulation | | | | |
