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First, let’s consider what CASPer Duet actually is. It’s pitched as allowing ‘value-alignment assessment’, which means that it assesses how well a candidate might fit the place to which they are applying. It claims to offer a standardised way to assess the values of applicants. This requires a two-step process, in which ‘key stakeholders’ from the program will complete Duet themselves, and through their completing it a ‘program profile’ is generated. This program profile should, at least in theory, provide a map of the program’s attributes, and what it deems to be important. The characteristics which are ranked include ‘values, priorities and differentiators.’ Then, applicants will go on to complete the Duet assessment, and themselves select the characteristics that they prefer from a program. After this, their responses are compared to the ‘fit profile’ – in other words, the mapping – that has been generated from the course leaders who sat the Duet previously. Applicants will then be given a ‘fit score’ which determines how close their perception of characteristics and values is to that of the program leaders. This is performed through matching characteristics in pairs – previously, there have been three sets of ten characteristics which must be paired.
Acuity Insights, the company behind CASPer and Duet, advises those that sit Duet to answer as themselves, and that you must sit the test within two weeks of having sat the CASPer test itself.
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In the past, Duet allowed applicants to answer questions in binary (yes/no, either/or) format. However, from the 2023-24 admissions cycle onwards, Acuity Insights are implementing a Likert scale response format ‘to allow more nuance in applicants’ responses.’ They state further that the ‘Likert scale gives more distributed data for a better analysis to support finding a good match between programs and applicants.’
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A Likert scale is a type of rating scale commonly used in surveys and questionnaires to measure people’s attitudes, opinions, beliefs, and perceptions. The scale consists of a set of statements or items, and respondents are asked to indicate their level of agreement or disagreement with each statement on a five or seven-point scale.
The Likert scale is named after its inventor, Rensis Likert, who developed it in the 1930s as a tool for measuring people’s attitudes towards various social and economic issues. The scale has since become a widely used tool in psychology, education, marketing, and other fields.
The typical Likert scale consists of a series of statements or items, often in the form of declarative statements or questions, to which respondents are asked to indicate their level of agreement or disagreement. The scale typically ranges from five to seven points, with 1 representing “strongly disagree” or “not at all,” and the highest number representing “strongly agree” or “completely.” Some scales also include a “neutral” option, usually represented by the number 3.
A typical scale might be:
Strongly satisfied
Somewhat satisfied
Neither satisfied nor dissatisfied
Somewhat dissatisfied
Strongly dissatisfied
After completing the Likert scale, the researcher (in this case, Acuity Insights) can compute a score for each respondent by summing up the values of their responses to each item. The resulting score can then be used to compare respondents’ attitudes or to identify trends or patterns in the data – in this case, the program’s own desired attributes. As stated, one advantage of the Likert scale is that it allows for a more nuanced and detailed measurement of attitudes and opinions than other types of scales, such as binary or dichotomous scales, which only allow for two possible responses (yes/no, true/false, etc.). The Likert scale also allows respondents to express their opinions on a continuum, rather than forcing them into discrete categories. However, it still has limitations – for one, the scale assumes that attitudes and opinions are unidimensional, or that they can be measured along a single dimension (such as agreement or satisfaction). This may not always be the case, and more complex measures may be necessary for more complex attitudes and opinions. Additionally, the Likert scale can be vulnerable to response bias, where respondents may be reluctant to express negative opinions or may simply choose the same response for all items without actually reading them.
Overall, you should therefore be ready to rank responses in a more complex manner than the previous binary options – and be ready to consider how important attributes of a program are on a deeper level.
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