After you receive your certificate, you will have access to the Human Insight Platform. To access the Human Insight platform navigate to the bottom of our website and click Human Insight Platform.

Please find the size of our reference populations below.

For the AEM-Cube:
Self-images: 28.145
Feedback-images: 51.369

For the Qi:
Current: 1292
Desired: 941

The normal distribution curve, also known as the bell curve because of its shape, serves as a visual representation of a prevalent phenomenon
observed in various aspects of the world. It’s called “normal” because lots of things in nature follow this distinct pattern. The curve illustrates how
data tends to cluster around a central value, or mean.

Take the heights of a large group of people, for instance. If you plot how many people have a certain height, you would notice that the data clusters
around a central value. This central value is called the mean. Your plot line will end up looking like a bell shape, with the mean being the highest point.
The number of people with a certain height goes down the farther you are removed from the mean. In other words, there are a lot of average
sized people and only a small number of really tall or really short people.

It is natural for individuals to present themselves in a favorable light. For example, wanting to appear more explorative because it seems like an
attractive trait. While social desirability can introduce bias into any self-report assessment, we mitigate this through the use of standardized
scores (norming). Because social desirability affects the general population, the average scores of the reference group will also reflect
this upward trend. By comparing an individual’s raw results against this normed population, the relative impact of the bias is neutralized.
Furthermore, rigorous statistical testing has confirmed that our assessments remain highly reliable and consistently measure the specific
psychological constructs they are intended to capture.

The use of standardised scores has the effect that even individuals with relatively neutral responses might still end up with seemingly extreme
standardised scores. For instance, suppose the reference population has a mean exploration score of 65 and a standard deviation of 10 on a scale
from 1 to 100. In this case, a person with a score of 60, while closer to the explorative end in absolute terms, is actually less explorative than the
average person. Standardised scores reflect this nuance.

Just like the AEM-cube, psychometric tests typically use standardised scores. This is because standardised scores provide a common metric for
interpreting individual outcomes, enabling practitioners to assess an individual’s contribution and perspective relative to a baseline. This facilitates more
accurate and reliable evaluations.

Imagine, for instance, that a group of students completes a French test and they all score around 95%. Does this mean that everybody in that group
speaks nearly perfect French or is it more likely that the test was too easy? The answer lies in comparing the raw scores of this group to that of a
reference population and which will allow us to gain a much better understanding of the implications behind these outcomes.

The calculation of standardised scores involves several steps. For each of our assessments we have created a reference group comprising thousands
of participants. For each of these groups, we calculate the mean and standard deviation. Next, we use these values to construct a normal distribution
curve. By comparing an individual’s raw score to this distribution, we determine how far their score deviates from the mean. This comparison yields
the individual’s position within the normal distribution. This is their standardised score.

For example, let’s consider a reference group with a mean of 5 and a standard deviation of 2. If an individual obtains a raw score of 7, we calculate their
deviation from the mean using the formula (raw score – mean) / sd. Substituting the values, we get (7 – 5) / 2 = 1. This indicates that the person is 1
standard deviation removed from the mean. In a normal distribution, this deviation corresponds to a standardised score of 84.

The score provided in an individual’s report is their standardised score. It is determined by comparing their raw score to that of a reference population.
This reference population is comprised of a large group of individuals whose scores serve as a basis for comparison, acting as a benchmark that
reflects broader society. Therefore, the standardised score reflects the individual’s position within the general population.