Double Introverts, Dual Extraverts
I describe here how I discovered a new way to find the function-attitudes—the ‘building blocks’ of personality type—associated with any set of MBTI® results. I discovered this method almost by accident. My goal was to form teams of graduate design students working together to conceive, build, demonstrate, and report on a real physical project sponsored by a medical or industrial institution. I speculated that type-diverse teams would be more successful. The focus of this article, however, is not on how the function-attitudes were used, but rather on how they were computed from the MBTI data and on the revelations uncovered by this new approach.
At the time, the only method for finding an individual’s preferred function-attitudes (the Jungian functions of Sensing, Intuiting, Thinking, or Feeling, in their attitudes of extraversion or introversion) was the set of five steps given in the MBTI Manual (1998, p. 30) for identifying the hierarchy of functions of each type. These MBTI scoring rules, referred to here as the type dynamics steps, involve examining the four-letter type code and finding from it the extraverted or introverted dominant and auxiliary functions (Steps 1-4), from which are inferred the tertiary and inferior functions (Step 5). These rules involve only the MBTI categories, not their associated scores. I found these steps too cumbersome to apply one by one to fifty students, so I sought another approach involving the scores that I could incorporate into a simple computerized numerical spreadsheet such as Excel. It did not occur to me when I first started using the scores that my approach might produce a different result from the standard method.
MBTI certification programs tell prospective practitioners never to use these scores because it is possible to misuse them as suggesting ‘strength’ of preference. The formulas developed here do not do this, however. They simply constitute a more reliable way of determining the correct function-attitude pattern. The procedure given here is the first to exploit the MBTI instrument’s numerical information, heretofore overlooked, to advance the use of function-attitudes in personality theory. For this task it is not enough to know just the MBTI categories E/I, S/N, T/F, and P/J. You must also use the associated computer-generated scores, known as the ‘preference clarity indices’ (pci’s). The pci’s are the difference between the responses for the two poles of each dichotomy. For example, a score of 19 for N and 11 for S gives a pci of 8 (19 minus 11) for Intuiting, and we can infer a pci of -8 for Sensing. The pci’s may then be discarded, and need never be regarded as indications of preference ‘strength,’ any more than they have been in the past. The calculations involve only plugging the scores into simple formulas that can be calculated either automatically, using an easily constructed spreadsheet, or manually.
The pci scores being the only numbers available, I decided to use them to construct a numerical approximation to Jung’s already well known function-attitude model. The table below shows my numeric calculations for one actual respondent (‘Isaac’) whose Form M pci’s were I 30, N 8, T 26, and J 6. The formulas show how to calculate composite values (CVs) that reflect both the function and its attitude. The type dynamics rules would assign an INTJ type code to this individual, implying that Ni is dominant with Te auxiliary. That scoring method, however, does not give enough weight to the high score for Thinking, and particularly for introverted thinking (Ti). My calculations produce the highest scores for two introverted functions—Ni and Ti. Whether we call this outcome ‘double introvert’ or ‘same-attitude auxiliary,’ we cannot ignore the high scores for both introverted intuition and introverted thinking.
The eight rectangles of the table each represent one function-attitude—for example, extraverted sensing (Se) in the upper left. Half of the rectangles, those involving Sensing and Intuition, are grouped into the perception (P-) domain on the left, the other four involving Thinking and Feeling are collected into the judgment (J-) domain on the right.
Each rectangle contains a spreadsheet formula automatically calculating a Composite Value for that function-attitude. Each formula achieves its maximum Composite Value when the relevant pci scores are at their maximum values. For the MBTI Form M, the maximum computer-generated score for each function or attitude is 30. You can verify that Se’s composite value, for instance, is indeed 30 when its pci scores for E, S, and P are all 30. We can show this with a hypothetical individualwhose responses to the instrument are extreme, with the highest possible score for extraverted sensation—call her ‘Sophia Sensation.’ For Sophia, the Composite Values for Ne and Si go to zero and for Ni (Se’s function-attitude opposite), the CV is -30. Note also that the T/F dichotomy is a non-factor here, since this hypothetical individual’s responses indicate zero inclination for judging of any kind. The T and F responses are therefore ‘neutral,’ i.e., both T and F =0. For Se therefore, this construction generates the scores for Jung’s function-attitudes accurately from the relevant MBTI scores. And it works equally well for the other seven function-attitudes.
This system of scoring and the standard system produce identical results when all pci scores are zero. A function or attitude score is zero for a dichotomy whenever the respondent’s answers tally equally for the two poles (for E these are extraversion and introversion). And if E, I, S, N, P, and J all = 0, then all the Composite Values for the P-domain function-attitudes are also zero (i. e., Se, Ne, Si, and Ni = 0). Similarly in the J-domain if E, I, T, F, P, and J = 0, then Te, Fe, Ti, and Fi = 0. The function-attitude formulas, therefore, fit perfectly, both at the extreme values of the two models where responses indicate maximum clarity of preference, and also at their centers where the responses indicate no preference. In testing the extremes of possible scores using these ten combinations, we see that the Composite Values for the function-attitudes of Jung’s theory exactly match the pci scores of the functions and attitudes generated by the MBTI assessment. Thus, this method of calculating scores reconciles the MBTI four-letter type system with the eight-function-attitude system, and is the only approach, to my knowledge, that can claim to do so. Each rectangle contains a spreadsheet formula automatically calculating a Composite Value for that function-attitude. Each formula achieves its maximum Composite Value when the relevant pci scores are at their maximum values. For the MBTI Form M, the maximum computer-generated score for each function or attitude is 30. You can verify that Se’s composite value, for instance, is indeed 30 when its pci scores for E, S, and P are all 30. We can show this with a hypothetical individual whose responses to the instrument are extreme, with the highest possible score for extraverted sensation—call her ‘Sophia Sensation.’ For Sophia, the Composite Values for Ne and Si go to zero and for Ni (Se’s function-attitude opposite), the CV is -30. Note also that the T/F dichotomy is a non-factor here, since this hypothetical individual’s responses indicate zero inclination for judging of any kind. The T and F responses are therefore ‘neutral,’ i.e., both T and F =0. For Se therefore, this construction generates the scores for Jung’s function-attitudes accurately from the relevant MBTI scores. And it works equally well for the other seven function-attitudes.
Non-maximum response counts are weighted proportionately by CPP’s pci formulas, constructed to interpolate between the centers and the extremes. Thus in the example of Isaac in the table, whose scores are I 30, N 8, T 26, and J 6, the implied corresponding negative scores would be E=-30, S=-8, F=-26, P=-6. This results in a Composite Value for extraverted sensing of [(-30 – 6)/2 – 8]/2 = -13 as shown at the bottom of the Se square of the table. This negative score indicates that Ni, the function-attitude diagonally opposite from Se, will be positive with the same absolute value (Ni =13), so no further calculation is needed to find the Composite Value for Ni. Likewise, once any function-attitude has been calculated, we know that its opposite function-attitude has the same numerical value, but with the opposite (+/-) sign. So only four, not eight, computations are required, and they are easily carried out manually for an individual or using a spreadsheet with embedded formulas for a team. Results for the other six function-attitudes are shown in the remaining squares, the positive ones being shown in boldface.
Unless they zero-out, as illustrated above, the formulas thus yield positive Composite Values for all four preferred function-attitudes of Jung’s personality theory. In the example these are: Ni 13 and (Si 5) in the P-domain, and Ti 19 and Te 7 in the J-domain. In each domain (perception and judgment) the function-attitude with the higher score is called the ‘principal,’ the other being known as the ‘subsidiary.’ The parentheses around the P-subsidiary (Si 5) indicate that its composite value is less than 6, or 20% of the maximum of 30, which makes its preference clarity ‘slight,’ or in quantitative terms, not statistically significant. The fact that the J-principal function-attitude Ti 19 is 6 more than P-principal Ni 13 makes the introverted thinking score significantly higher (statistically), and therefore indicates the dominant in Jung’s terminology, with introverted intuiting, then, being auxiliary.
It is important to note that the J-subsidiary Te 7 is considered to be statistically significant. This means that this individual has a noteworthy preference for extraverted thinking, even though his or her dominant is introverted thinking, and type dynamics theory would have us believe that such ‘double-thinking’ preferences cannot happen. The discovery and exploitation of previously hidden talents associated with subsidiary function-attitudes thus revealed is one of the most important contributions of this new quantitative method. Any client would certainly be interested in knowing about such personal potentials, currently hidden in the unexamined MBTI data. Guidance counselors in particular can enhance their services by bringing subsidiary function-attitudes to light, provided they are statistically significant, and of course by exploring newly revealed dominant and auxiliary preferences as well.
These results squarely contradict, and in my opinion disprove, conventional type dynamics assumptions about ‘balance’ that lead in part to the previously assumed arrangement of the function-attitude sequence of innate preferences. Our example, Isaac, would be reported through conventional MBTI interpretation as INTJ, and therefore expected by type dynamics theory to have a fairly high (tertiary) preference for introverted feeling (Fi). Whereas, my approach leads me to conclude that Fi is not developed in this individual, and it is Fi’s ‘opposite,’ Te, which plays the subsidiary J-domain role. And remarkably, the type dynamic steps wouldn’t even mention introverted thinking (Ti), which our quantitative analysis shows to be dominant. This oversight would distort the personality description, leading to inadequate career counseling and mediocre assignment to a team. Moreover, according to conventional type thinking, the MBTI type with Ti dominant and N auxiliary is INTP rather than INTJ, making it another perplexing case for Personality Type in Depth’s recent “Question of the Day: INTJ or INTP?”
When I first noticed such discrepancies, I paid special attention to the students involved to see which prediction fit better. These observations involved several hundred students over the last fifteen years. Since it was soon apparent to me that the Composite Value predictions provided more reliable information than conventional MBTI reports, I stopped using the standard scoring method entirely. The fraction of Stanford teams winning national awards tripled (Wilde, 2009, 2010) when CV analysis of the MBTI results was used to create function-attitude-diverse teams, as compared to previously not taking personality type into consideration at all. Reynierse and Harker then published their statistical study of type dynamics behavior predictions, summarized in “The Case against Type Dynamics” (2009), concluding that the correlation was unacceptably poor. I took their study as further reason to believe that a better way of calculating MBTI scores was needed.
That’s how the study of teams led to the discovery of quantitative typology. Here’s hoping this story inspires further examination and quantification of the function-attitudes. I would enjoy hearing from anyone who cares to comment, ask questions, or share their experiences with this approach. For a more in-depth analysis of my method and its consequences for practitioners, career counselors, and management consultants, see Jung’s Personality Theory Quantified (Wilde 2011).
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Reynierse, J. H. (2009). The case against type dynamics. Journal of Psychological Type, 69:1.
Shumate, C. and Hunziker, M. (Oct 2011). “Is it INTJ or INTP?” Personality type in depth.
Wilde, D. J. (2009). Teamology: the construction and organization of effective teams. London, England: Springer.
Wilde, D. J. (2010, February). Personalities into teams. Mechanical Engineering Magazine, 39:1, 20-24.
Wilde, D. J. (2011). Jung’s personality theory quantified. London, England: Springer.