Paired Comparisons of Statements (PCS) is a scaling technique for dealing with binary attributes (yes/no or present/absent attributes) and to estimate the preference associated with each of them (e.g., importance or desiderability). A PCS exercise consists in exposing respondents to a pairs of attributes / messages / statements.
Respondents are asked to choose the most preferred attribute from the pair; in addition, they can be asked to indicate the strength of the preference.
- definition of the items to be included in the PCS exercise; items should be phrased as dichotomous (i.e., item present or absent);
- identification of the most appropriate PCS framework for the study (sample size; short vs graded model; inclusion of neutral point; number of scenarios; number of versions/blocks);
- preparation of the PCS design; the combinations of items to be presented to respondents are determined by an experimental design based on design of experiment rules;
- analysis of the PCS data by individual level estimation;
- interpretation of findings and delivery of results.
R-sw Tradeoff is the only commercial software that allows running both the short and the graded Paired of Comparisons Models.
The PCS approach presents a number of important benefits over other approaches commonly used to assess preference or importance for a list of items:
- the PCS setting allow exploring the preference for a potentially rather long list of items, usually between 20 and 40 items, with a limit only imposed by the sample size;
- results are highly differentiated, much more than with a rating scale approach;
- results are highly comparable across markets or segments as items are measured on a common scale, no matter the respondents background (no cultural or experience bias);
- results are on a ratio scale that is highly welcome by researchers;
- very simple exercise for respondents (reduced fatigue, high quality data);
- simple to execute (design easy to prepare, even with restrictions, and analysis easy to run).