Retorio works with a video-based Artificial Intelligence system that supports human intuition. Our technology is based on one of the most well-researched personality taxonomies, the so-called Big5 (or OCEAN) model, which has been validated as a reliable predictor of personality and job performance.
Our AI quantifies the behaviour that participants of Retorio's assessment show in short video recordings; their facial expressions, gesture, language, and voice in order to derive a perceived personality prediction, meaning that our AI predicts how your candidates and their behavior would be perceived by a representative set of people.Â
How exactly can we help you with your hiring process
In job interviews, candidate's assessment of their own personality and soft skills tend to be altered by their desire to appear more favorable and get the job. Retorio has learned to judge interviews objectively and free from bias.
Our AI simulates how your candidates will be perceived by the future team members, clients, subordinates or superiors. This way, you can assess reliably, how closely a candidate matches the requirements of the task that he or she is expected to perform, and how well a candidate fits into your company culture.
What parameters of a person does Retorio's AI analyze
The combined rating of the AI makes use of visual input from facial expression and gesture, as well as auditive information like speaking speed, language sentiment and engaging language.
Note: No. of questions and duration has been provided as a guide. We recommend using between 3 to 5 questions that can be set up in few minutes with an option to set response time.Â
Measures put in place to remove or reduce biases
We make it our objective to assess candidates' personality as objectively as possible. We guarantee high objectivity and reliability by removing single observer-based bias from the data and using multiple observer ratings. We also employ statistical methods to remove any biases resulting from gender, age, ethnicity or skin color.
Key Information
Online - desktop / tablet, Online - mobile
15 mins5 questions
English (UK)
Free trial : Yes No training required to use
Personalised to your role (based on Big 5 personality factors)
Our system has a prediction accuracy of roughly 92%. In other words, if you asked a representative group of hundreds of people to judge a person whom they don't know based on a short video, Retorio's personality prediction would show a 92% overlap with the average judgment of this group of people.
Comparison groups available for your candidate scores
<p>&bull; 6500+ Job Specific Formulas &bull; 175 Job Performance Impact Traits &bull; 25+ Years Research &amp; Validation &bull; Advanced Assessment Technology &bull; High ROI &bull; Easily Customized &bull; Reports Specific to Person and Job &bull; 29+ Languages &bull; No Adverse Impact</p>
<p>The Harrison Assessments System provides a comprehensive assessment of the behavioural competencies required for a position and accurately predicts success and potential obstacles. Integrated selection tools include performance based interviewing questions, how to attract the candidate, and the ability to calculate eligibility, suitability, and interview ratings for a composite ranking of candidates.</p>
<p>The assessment is web-based and scored online with comprehensive results available within 15 seconds.</p>
<p><strong>Enjoyment Performance Theory:&nbsp;</strong> Enjoyment Performance Theory states that an individual will perform more effectively in a job if they enjoy the tasks required by that job, have interests that relate to the position, and have work environment preferences that correspond with the environment of the workplace. Harrison Assessment&rsquo;s global research indicates that the enjoyment of these various aspects of a job is highly correlated with good performance. &quot;If you enjoy an activity, you tend to do it more. By doing it more, you tend to learn and improve the related skills. As a result, you tend to gain recognition (including self recognition) which helps you enjoy the activity more.&quot;</p>
<p>Candidates rank answers in order of what you they&nbsp; know enjoy. Enjoyment performance theory states that if someone enjoys 75% of the tasks required in a job role they are 3x more likely to be successful.</p>
<p><strong>Paradox Theory:&nbsp;</strong>Harrison Assessment&rsquo;s Paradox Theory provides a greater depth of psychological understanding because it reveals an entire system of behaviour rather than merely offering insights about specific traits. It also predicts stress behaviour and provides a framework that facilitates objective understanding of self and a clear direction for self-development.</p>
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<p><strong>Eligibility/Suitability</strong></p>
<p>The ability to predict job performance is dependent upon identifying all of the critical factors. If one assesses eligibility or technical competencies, it only represents a portion of the critical factors to predict performance. When behavioural competencies are also measured, such as emotional intelligence, personality, and work preferences, a high degree of accuracy is attained to predict performance.</p>
<p>Note:&nbsp;No training required use the assessment as we will support you in administering and delivering the reports to you. should you wish to manage this process yourself, we would be able to offer the training.</p>
Bias results when test performance is affected by unintended factors and those factors are not evenly distributed between groups. This results in group differences in test performance that are not related to the constructs the test is intended to measure. For example, a test of numerical reasoning that uses a lot of text may be biased against people who have English as an additional language. Group differences do not result from different levels of numerical reasoning ability, but from questions being more difficult for some due to their use of language.
Test developers may address bias through some or all of the following:
. Providing a clear rationale for what the test is, and is not, intended to measure
· Reviewing content to ensure it is accessible and free from complex language
· Ensuring scoring is automated and objective (i.e. free from user bias)
· Providing evidence of any group difference in test scores
· Examining the effect of group membership on individual questions – sometimes referred to as ‘differential item functioning’ or ‘dif’
· Ensuring norm groups used for comparisons are representative of the populations they reflect
· Providing guidance on using the reports and interpreting constructs measured
Reliability is an indicator of the consistency of a psychometric measure (Field, 2013). It is usually indicated by a reliability coefficient(r) as a number ranging between 0 and 1, with r = 0 indicating no reliability, and r = 1 indicating perfect reliability. A quick heads up, don’t expect to see a test with perfect reliability.
Reliability may refer to a test’s internal consistency, the equivalence of different versions of the test (parallel form reliability) or stability over time (test-retest reliability). Each measures a different aspect of consistency, so figures can be expected to vary across the different types of reliability.
The EFPA Test Review Criteria states that reliability estimates should be based on a minimum sample size of 100 and ideally 200 or more. Internal consistency and parallel form values should be 0.7 or greater to indicate adequate reliability, and test-retest values should be 0.6 or greater.
Most test scores are interpreted by comparing them to a relevant reference or norm group. This puts the score into context, showing how the test taker performed or reported relative to others. Norm groups should be sufficiently large (the EFPA Test Review Criteria states a minimum of 200) and collected within the last 20 years. Norm groups may be quite general (e.g. ‘UK graduates’) or more occupationally specific (e.g. ‘applicants to ABC law firm’).
A key consideration is the representativeness of the norm group and how it matches a user’s target group of test takers. It is therefore important to consider the distribution of factors such as age, gender and race in norm groups to ensure they are representative of the populations they claim to reflect. This is particularly important with norms claiming to represent the ‘general population’ or other wide-ranging groups. Occupationally specific norms are unlikely to be fully representative of the wider population, but evidence of their composition should still be available.
Validity shows the extent to which a test measures what it claims to, and so the meaning that users can attach to test scores. There are many different types of validity, though in organisational settings the main ones are content, construct and criterion validity. Reference may also be made to other types of validity such as face validity, which concerns the extent to which a test looks job-relevant to respondents.
Content validity relates to the actual questions in the test or the task that test takers need to perform. The more closely the content matches the type of information or problems that a test taker will face in the workplace, the higher its content validity. For tests such as personality or motivation, content validity relates more to the relevance of the behaviours assessed by the test rather than the actual questions asked.
Construct validity shows how the constructs measured by the test relate to other measures. This is often done by comparing one test against another. Where tests measure multiple scales, as is the case with assessments of personality and motivation, it is also common to look at how the measure's scales relate to each other.
Criterion validity looks at the extent to which scores on the test are statistically related to external criteria, such as job performance. Criterion validity may be described as 'concurrent' when test scores and criterion measures are taken at the same time, or 'predictive' when test scores are taken at one point in time and criterion measures are taken some time later.
Construct and criterion validity are often indicated by correlation coefficients which range from 0, indicating no association between the test and criterion measures, and 1, indicating a perfect association between the test and criterion measures. It is difficult to specify precisely what an acceptable level of validity is, as this will depend on many factors including what other measures the test is compared against or what criteria are used to evaluate its effectiveness. However, for criterion validity, tests showing associations with outcome measures of less than 0.2 are unlikely to provide useful information and ideally criterion validity coefficients should be 0.35 or higher. The samples used for criterion validity studies should also be at least 100.
Overall, whilst a publisher should provide validity evidence for their test, validity comes form using the right test for the right purpose. Therefore, users need to use available validity evidence to evaluate the relevance of the test for their specific purpose.
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