Table 2.
Explicit Values Clarification Methods
| Method | Description |
|---|---|
| Adaptive conjoint analysis (example 32 ) | The user rates a series of sets of attributes and their levels, where choices presented are tailored to earlier answers. |
| Allocation of points (example 33 ) | The user has a “budget” to “spend” on decision attributes, according to their importance. |
| Analytical hierarchy process (example 34 ) | The user is asked to compare sets of options relative to predefined decision criteria. |
| Best–worst scaling (example 35 ) | The user is asked to indicate the best and the worst object in repeated subsets of a finite number of objects (case 1, also known as object scaling or MaxDiff), the best and worst attributes within each of a number of profiles that systematically vary across a multiple attributes and levels (case 2), or the best and worst profiles from among 3 or more profiles (case 3). |
| Decision analysis or multicriteria decision analysis (umbrella term a ) (resource36,37) | The user is asked to directly indicate the extent to which a decision attribute or outcome matters to them or how good or bad they deem it to be. These values are then used in a model that calculates alignment between what matters to the user and the available decision options. |
| Discrete-choice experiments (example 38 ) | The user is asked to make a series of choices between 2 (or more) alternatives, where each alternative is characterized by attributes and their associated levels. |
| Open discussion (example 39 ) | The user discusses what matters to them in an unstructured or semistructured discussion, possibly aided by a preset or user-created list of topics. |
| Pros and cons (resource 40 ) | The user lists advantages (pros) and disadvantages (cons) of options and/or indicates the relevance (“this matters to me”) or importance (e.g., on a Likert scale) of each advantage or disadvantage. |
| Ranking (example 41 ) | The user is asked to place attributes in order of importance, relative to each other. |
| Rating scales (example 42 ) | The user indicates the importance of an attribute on a visual analog scale (e.g., paper-based visual analog scale, online slider) or Likert scale approximating a visual analog scale. If the rating is then used to calculate and show which option fits best, the method is classified as (multicriteria) decision analysis. |
| Social matching (example 43 ) | The user “observes different characters’ decisions and/or decision-making processes and identifies 1 or more characters” with whom they identify. 16 |
| Standard gamble (example 44 ) | The user indicates their choice between a) living the rest of their life in a particular health state (in the current context, a health state relevant to the health decision they are making) and b) taking a gamble between 2 possible outcomes: the probability p of living the remainder of their life in a state of optimal health and the probability 1 –p of immediate death. |
| Time tradeoff (example 44 ) | The user indicates how many remaining lifetime years in full health they would be willing to give up (i.e., “trade off”) to avoid living for the rest of their life in the health state representing the decision making option of interest. |
a
Multicriteria decision analysis or decision analysis is an umbrella term. It encompasses some of the other, more specific categories (e.g., discrete-choice experiments, best–worst scaling.) When applicable, we use the more specific, narrower categories. Otherwise, we use the umbrella term multicriteria decision analysis or, for brevity in figures, decision analysis. In addition, although within multicriteria decision analysis, the user may be asked to rate attributes on rating scales, what distinguishes multicriteria decision analysis from other methods such as rating scales is that the model calculates how well or poorly the options align with what matters to a user.