Note. Top three responses were indicated.
Utilizing those items
(Tables
2-4), several large-scale surveys in Japan were designed or analyzed by
psychologists or other academic researchers after 1990, which clearly
confirmed self-reported personality traits using ANOVA (Matsui, 1991;
Muto et al., 2012; Ryu & Sohn, 2007; Sakamoto and Yamazaki, 2004;
Yamaoka, 2009; Yamazaki & Sakamoto, 1991). Archetypical sample sizes of
these were 6,660 (Yamaoka, 2009), 11,766 (Matsui, 1991), 32,347
(Sakamoto and Yamazaki, 2004; Yamazaki & Sakamoto, 1991 & 1992 –
these three used the same sample) and over 100,000 (Muto et al., 2012;
our estimation; the exact number was not specified in this report,
although it alluded the size was much larger than preceding ones). These
results cannot be explained by the Burnham effect (vague and general
descriptions), because the differences in self-reports were not either
virtual, vague, nor ambiguous, but supported by definite and real
values.
Regardless, statistical differences had not been confirmed in
respondents without blood type personality knowledge. Therefore, the
current scientific consensus is that these differences are
self-fulfilling phenomena induced by the “contamination by knowledge”
(Cramer & Imaike, 2002; Eysenck & Nias, 1982; Kim, Lee & Lee, 2007;
Matsui, 1991; Ryu & Sohn, 2007; Sakamoto & Yamazaki, 2004; Sato &
Watanabe, 1992; Yamaoka, 2009; Yamazaki & Sakamoto, 1991 & 1992).
Reliability and Validity of Single-
or Two-item
Scale
In recent years, scales that attempt to measure psychological constructs
with a very small number of items had been developed in various fields,
such as scales to measure subjective well-being (Diener, 1984) and
self-esteem (Robins, Hendin, & Trzesniewski, 2001) with a single item.
Both scales are used in many studies. Similarly, in the Big Five
personality test, a very brief measure, 5 and 10-item inventories were
developed. For example, Gosling, Rentfrow and Swann (2003) created the
Ten Item Personality Inventory (TIPI), which measured the five factors
of the Big Five in 10 items, 7-point scale. Later, Oshio, Abe and
Cutrone (2012) developed a Japanese version of Ten Item Personality
Inventory (TIPI-J). The results of multiple validation tests generally
supported the reliability and validity of the two tests: the Cronbach’s
alphas were 0.40-0.73 (Gosling, Rentfrow & Swann, 2003) and 0.72-0.91
(Oshio, Abe & Cutrone, 2012); test-retest reliabilities were 0.62-0.77
and 0.64-0.84, respectively. Furthermore, according to several studies
of psychologists (Cho et al., 2005; Ryu & Sohn, 2007; Wada, 1996), more
than ten items of the Big Five scales of personality trait correspond to
blood type personality (Appendix B).
Results of Personality
Tests
There are many types of personality tests used in psychology, depending
on the purpose. The “Big Five” test is generally used for blood type
and personality studies. The Big Five personality test, as the name
implies, comprehensively describes personality by five factors called
the Big Five (Goldberg 1990 & 1992). These five factors are usually
called Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness. The model collects vocabularies from dictionaries and
traditional personality tests, as well as re-analyses of personality
scales, and five factors were extracted through factor analysis. Thus,
the Big Five does not assume any background theory. In other words, it
can be said that the Big Five model was constructed as the result of an
attempt to broadly describe personalities with as few factors as
possible, without assuming any background theory. Thus, the Big Five
models are characterized by the bottom-up process and personality is
comprehensively captured by five factors. The NEO-PI-R, commonly used as
a Big Five personality test, consists of 60-240 question items, each of
which is rated using a five-point scale (Costa & McCrae, 1992;
Kunisato, Yamaguchi & Suzuki, 2008; Shiinonaka, Nakazato, Gondo,
Takayama, 1998; Wada, 1996).
As mentioned above, the Big Five is a questionnaire-based personality
assessment, which consists of answering to multiple questions regarding
multiple self-reported personality traits. These traits integrate into
five personality factors by statistical processing. In theory, this
means that the self-reported answer will either directly or indirectly
appear in the result. Although there are many academic studies using
personality tests (including the Big Five) on the relationship between
blood type and personality, the inconsistency among results (Cattell,
Boutourline & Hundleby, 1964; Cho, Suh & Ro, 2005; Cramer & Imaike,
2002; Flegr, Preiss & Klose, 2013; Furukawa, 1927 & 1930; Gupta, 1990;
Jogawar, 1983; Kim et al., 2007; Lester & Gatto, 1987; Mao, Xu, Mu, Ma
& He, 1991; Nawata, 2014; Rogers & Glendon, 2003; Sharifi, Amadian &
Jalali, 2015; Sato & Watanabe, 1992; Shimizu & Ishikawa, 2011; Wu,
Lindsted & Lee, 2005) has led to the endless academic controversy about
whether the relationship is scientifically confirmed. As another
example, a 2014 10,000-particpant study in Japan and the US re-analyzed
data of large-scale social surveys on money and consumer life revealed
no meaningful difference (Nawata, 2014).
After 2000, a growing number of studies proved the previously questioned
link between blood type and physical constitution, with the exception
being in the weak gastrointestinal tract: this demonstration proposed a
new approach to medicine (Ewald & Sumner, 2016; Risch, 2000). For
example, more than 10 studies had reported a relationship between
susceptibility to COVID-19 and blood type (Barnkob et al., 2020;
Ellinghaus et al., 2020; Hoiland et al., 2020). According to these
studies, type O was the least susceptible, and type A the most.
There had also been several studies on biological factors, which
investigated whether physical constitution affected personality
(Hobgood, 2011a & 2011b). In 2015, a genotype of blood type and the
personality using the Temperament and Character Inventory (TCI) had been
determined to be related, as predicted by blood type personality theory
(Tsuchimine, Saruwatari, Kaneda & Yasui-Furukori, 2015). In this study,
type A was found to be the most “persistent.” The TCI, a top-down
personality model, is often used to examine genetic dispositions
(Cloninger 1987; Kijima et al., 1996). This personality test built a
model for temperament with a physiological basis in the background. The
test consists of 240 items using a yes-no scale rating.
Cloninger hypothesized that personality consists of traits that are
hereditary and stable throughout life, and traits mature throughout life
under the influence of socio-cultural environment. The TCI consists of
seven dimensions, including four temperament dimensions (Novelty
Seeking, Harm Avoidance, Reward Dependence, and Persistence) and three
character dimensions (Self-directedness, Cooperativeness and
Self-transcendence). Three of the temperament dimensions have been
hypothesized to be associated with monoamine neurotransmitters. Novelty
seeking has been hypothesized to be associated with dopaminergic, harm
avoidance with serotonergic, and reward dependence with noradrenergic.
Overview of AI
Technology
Current AI technology often uses a technique called machine learning.
The technology, such as deep learning, is based on a neural network that
consists of perceptrons, which simulate the mechanisms of human neurons
in a multilayered network. This makes it feasible to learn various
characteristics contained in the data with dramatically high accuracy,
in comparison with the conventional techniques (LeCun, Bengio & Hilton,
2015). By inputting a large amount of image, voice, and text data into
the neural network, the computer system automatically learns the
characteristics contained in these data. There are various alternatives
other than deep learning, though AI nowadays often refers to the system
that uses it.
Objectives of This
Study
In this study, therefore, we examined validity and reliability of blood
type personality theory using the previously mentioned academic findings
and commonly used methodologies in psychology (e.g. ANOVA). In addition,
we used the latest artificial intelligence, AI (machine learning), which
can theoretically handle nonlinear multi-factor models. Details are
outlined in the following methods section.