There are tons of assessment solutions out there.
They tend to look very similar, and to make similar claims.
But they aren’t all equally useful. How can you tell the difference?
There are a number of important considerations, but probably the most important is the distinction between theory-driven assessment and data-driven assessment.
Some of the most popular assessments openly acknowledge that they are theory-driven.
For example, the MBTI assessment (Myers-Briggs Type Indicator) is based on a theory of personality proposed by Carl Jung, while the DISC assessment (dominance, influence, steadiness, compliance) is based on a behavioral theory proposed by William Marston.
This appeals to people because the assessment seems to be backed by an acknowledged authority and expert. And the underlying theory can be described in a way that is simple and easy to understand.
Other theory-driven assessments don’t specifically mention theory, but are nevertheless based on the assumption that their results are accurate, relevant, and important.
The value of theory-driven assessment is based on relatively simple claims such as:
- There are 4 basic types of people.
- Optimists make better salespeople.
- The results of this assessment provide accurate, relevant, and useful information.
The hallmark of theory-driven assessments is that their value can be determined by answering a simple yes/no question such as:
- Are there 4 basic types of people?
- Do optimists make better salespeople?
- Do the results of this assessment provide accurate, relevant, and useful information?
Answering these questions can help decide if the assessment seems worthwhile or not, but that’s pretty much it.
The assessment and its underlying theory don’t usually change based on new evidence (particularly if the theorist authority/expert has been dead for a long time).
Assessments don’t need to come from a theory.
In fact, it’s usually not a great idea.
Data-driven assessment turns this process on its head.
Instead of proposing a theory, and then needing to test whether its assumptions are actually correct, we can begin by asking relevant questions and then answer those questions with data.
- Are there a number of basic “types” of people? (spoiler alert: probably not)
- What personality traits make better salespeople?
- What assessment results provide the most accurate, relevant, and useful information?
Unlike with theory-driven assessment, these questions can’t really be answered with a simple yes/no.
After all, they are based on evidence from the very beginning.
The answers to these questions can be continuously updated, refined, and improved as new data becomes available over time.
Because specific data can be used to drive the assessment results, they can do much more than a general theory ever could.
For example, with the data from a specific company, we can actually ask:
- Are there basic types of people in this company?
- What personality traits make better salespeople for this company?
- What assessment results provide the most accurate, relevant, and useful information for this company?
Would a company really want to know the answers to those questions, in general, rather than specifically about their own people? Probably not.
How can you know a data-driven assessment when you see it?
It won’t be based on theoretical assumptions (assumptions which might not be correct).
It will begin by looking at evidence, and then working toward a solution.
The solutions it generates are flexible, and subject to change as new data come in.
The solutions it generates can be customized to more accurately predict, describe, or explain specific data.