Sometimes the best ideas are the obvious ones. For example, our Lumina Spark questionnaire measures introversion and extraversion separately. You might be surprised to learn that most other tools don't do that.

At Lumina Learning we see the value in measuring both ends of a scale equally positively, avoiding the risk of evaluative bias prevalent in many tools.
History, idea, method of Lumina Learning

The Evolution of Lumina Learning

Greek origins of psychometrics
The Greeks produced the theory of humours, dividing people into four categories - phlegmatics, melancholics, sanguines, and cholerics. This could be seen as a state of the art personality theory for its time, based on what the Greeks knew of the body rather than of the mind.

With hindsight, Eysenck showed that the Greeks had successfully identified two factors that have since been empirically verified in the Big5. Lumina Spark refers to these factors as introversion/extraversion, and risk reactors/reward reactors.

Carl Gustav Jung
Jung's (1921) theory of personality was a significant improvement on the humours and has served organisations well as a practical model for raising self awareness for many decades. Like the Greeks, Jung identified the introversion and extraversion factor and two additional factors that he termed feeling/thinking and intuition/sensing.

However, his work was based on case studies and anecdotal observations rather than statistical analysis. It is a massive credit to Jung's insights that, back in 1921, his theory correctly identified what would later be validated as three of the Big5's factors. Lumina Spark has named these Introversion/Extraversion, People Focused/Outcome Focused, and Big Picture Thinking/Down to Earth.
Origins of the Big 5
In 1936 Allport and Odbert created a source of over 4,500 words in an attempt to find the core ingredients of personality. In the 1940s Raymond Cattell continued in this vein and concluded that 16 factors defined one's personality. Fiske (1949) later refuted and found errors in Cattell's analysis. Fiske concluded that five factors could account for the variances in human personality, although Eysenck advocated three factors, and Ashton advocates six factors.

Nevertheless, it was not until the 1950s, when Tupes and Christal took this work further, that the first version of the Big5 model was officially born. Their work was replicated by Norman in 1963 and the Big5 model began its slow march towards becoming the accepted taxonomy for academics to research personality.
The Psychometrics Barren Years
The 1960s and 1970s were not great times for personality research as the behaviourists and other academics dismissed personality theory (see Mischel's 1968 attack on trait theory). However, back in the world of business, Mischel's reservations had little impact and practitioners forged ahead, often using the popular Jungian approach. Business has always been more concerned with 'what works' rather than pursuing the best academic approach!

Myers and Briggs took Jung's three factor model and added an additional factor to cover four of the Big5. Separately both Digman and DeYoung found two higher order factors that simplified the Big5 into two domains. More controversially Musek collapsed all five factors into one general factor of personality. The academically interesting "Big One" has found little practical application. All this research suggests the academic tide has turned back in favour of personality research overturning Mischel's concerns from the barren 1960s.
The development of Lumina Learning
In the 1980s and 1990s the Big5 emerged as the academics' theory of choice, with Costa and McCrae's model leading the field (1992). Costa and McCrae have set the gold standard for academic research using the Big5. Lumina Spark has been designed to integrate best practice identified in a range of Big5 and Jungian models for application in selection and development in organisations.

In particular, Lumina Spark has set out to retain the benefits established by the Jungian approach in equally valuing both ends of each polarity, without resorting to typing ('don't throw the baby out with the bath water'). Lumina Spark has been developed based on Big5 empirical research, yet provides a helpful Jungian lens with which to make sense of your personality. In 2009 the Lumina Spark model was embedded in the revolutionary Lumina Learning cloud-based platform, making innovative digital solutions accessible to clients throughout the world.
Don't force the choice

Don't Force the Choice

One unique feature of the Lumina Spark questionnaire is that it measures both ends of each polarity separately. In contrast, most Jungian instruments ask questions such as "Do you prefer to go to parties or stay in and read a book?" This 'forced choice' means the learner must declare themselves as either extraverted (a party goer) or introverted (a book reader). This forced choice is in fact a false choice.

With Lumina Spark, learners are not expected to make a false choice. Each concept from both ends of the polarity is assessed by a separate question. Thus learners may choose to declare they like to go to parties and they like to stay in and read books (or neither). This is achieved using a five point Likert scale to provide the learner with more responses so that they can accurately express their views.
All of your personality

All of your personality, all of the time

When working in organisations to help people develop, there is much research to show that being able to develop and integrate both ends of a polarity is of great benefit. For example, Lumina Learning helps individuals to see how they can blend their people skills with their drive for outcomes.

By measuring 3 personas (Underlying, Everyday and Overextended), Lumina Learning creates one portrait of your personality which reveals all of your personality, removing the need for additional tests and models.

Instead of measuring one end of the polarity in everyday mode and the opposite end in overextended mode (part of your personality, part of the time), Lumina Learning measures all three personas at both ends of the polarity - All of your personality, all of the time.

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Lumina Learning is a global network of skilled experts and practitioners operating in more than 40 countries all over the world.

Comparing Jungian, Big5 & Lumina Factors

Jungian / 4 measures Big 5 / 5 measures Lumina Spark / 10 measures
Introvert OR Extravert / I or E Extraversion / E+ to E- Extraversion E+ / Introversion E-
Feeling OR Thinking / F or T Agreeableness / A+ to A- People Focused A+ / Outcome Focused A-
Intuition OR Sensing / N or S Open to Experience / O+ to O- Big Picture Thinking O+ / Down to Earth O-
Judging OR Perceiving / J or P Conscientious / C+ to C- Discipline Driven C+ / Inspiration Driven C-
Typically not Measured Neuroticism / N+ to N- Risk Reactor N+ / Reward Reactor N-

Jungian vs. Lumina Spark Assumptions

The left hand column shows the Jungian assumptions that have been used for several decades. The contrasting Big5 and Lumina Spark assertions are outlined in the right hand column.
Jungian Assumption Lumina Spark / Big5 Latest empirical research
A total of four factors can be used to define personality. These four factors are 'bimodal', with an individual having to be at one end of the polarity i.e. an individual must be either an introvert or an extravert (and cannot be both). A total of five factors are needed to define personality (not four). These five factors are not ‘bimodal’ but instead form a ‘normal’ distribution. Furthermore more modern research such as Tett's 'trait activation theory' suggests an individual could possess contrasting traits in different contexts and refute the over simplicity of typing. Lumina Spark assumes an individual can be both introverted and extraverted in different situations.
Each of the four factors has two polarities.

An individual is assigned to one of the polarities across each of the four factors. For some individuals this assignment is clear cut and they strongly associate with one polarity. However, for some individuals their score can be closer to the mid-point and the reading of their type is less clear.

Nevertheless, after this assignment, each individual can be located in a cell within a 4 x 4 matrix of 16 personality types.
Within each of the five factors are sub-factors that Lumina Learning terms 'qualities'. Individuals can score anywhere on a continuum and the concept of 'type' as described by Jung is simply not empirically justifiable.

Forcing an arbitrary typing split in the middle of a normally distributed factor has the unintended consequences of damaging the test/re-test reliability of the sixteen types. Put simply, it is psychometrically unappealing for an individual to answer just one question differently in a questionnaire and then flip to become a different type.
Of the four functions - sensing, intuition, thinking and feeling - the one that is 'dominant' is not determined by the highest score of the four, but instead by a calculation based on the additional judging and perceiving factor.

Furthermore, the judging and perceiving factor determines the order of an individual's eight Jungian attitudinal functions (yet this ordering has little empirical support).
To determine the relative intensity of the five factors does not require a calculation based on the judging and perceiving scores. Instead, a simpler approach suggests the degree of preference an individual has for a factor is determined by the strength of their score in it.

Furthermore, rather than making unsupported assumptions about the order of use of different qualities, the intensity of each Lumina Spark quality in each of the three personas is measured directly and normed without the need for any complex assumptions.
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In other personality assessment tools there’s generally a binary choice between the different aspects. What I love about Lumina Learning is that it tells about the duality of man – or woman. It’s very easy to use, it’s very easy to understand, and it gives everyone in the organisation a common language. It’s such a powerful tool because you can connect and communicate more effectively as a result of the tool itself.

Ted Huang

Product Strategist, Moody's Analytics