Discussion
Issues with Personality
Tests
In Analysis 1, out of all 20 blood type characteristic items, 13 of them
(after the Bonferroni’s correction) clearly output the differences as
predicted. This study measured self-reported personality, and since many
Japanese believe in the relationship between blood type and personality,
conventional personality psychology theory suggests the differences
among the blood types will certainly appear, as prior mentioned. For the
same reason, personality psychology hardly explains that no difference
in blood type, or inconsistent results, appeared theoretically (Cattell
et al., 1964; Cho et al., 2005; Cramer & Imaike, 2002; Flegr et al.,
2013; Furukawa, 1927 & 1930; Gupta, 1990; Jogawar, 1983; Kim, Lee &
Lee, 2007; Lester & Gatto, 1987; Mao et al., 1991; Nawata, 2014; Rogers
& Glendon, 2003; Sharifi et al., 2015; Sato & Watanabe, 1992; Shimizu
& Ishikawa, 2011; Wu et al., 2005).
Ryu and Sohn (2007) re-analyzed Cho, Suh and Ro’s result of the Big Five
test of 40 items (2005), and found statistically significant differences
which matched blood type characteristics in 10 individual items. This
means that in the case of a “personality factor” composed of multiple
items, the difference by the blood type decreases ‒ few significant
differences appear. Wada (1996) constructed the Big Five Scales (BFS) of
60 personality trait terms, which matched blood type characteristics in
only 3 individual items (Appendix B).
This corresponds to the inconsistent results of many preceding studies
conducted by psychologists. In addition, the reason why psychological
personality tests did not show differences in blood type (Cattell et
al., 1964; Cho et al., 2005; Cramer & Imaike, 2002; Flegr et al., 2013;
Furukawa, 1927 & 1930; Gupta, 1990; Jogawar, 1983; Kim et al., 2007;
Lester & Gatto, 1987; Mao et al., 1991; Nawata, 2014; Rogers &
Glendon, 2003; Sharifi et al., 2015; Sato & Watanabe, 1992; Shimizu &
Ishikawa, 2011; Wu et al., 2005) was probably due to the halo effect.
Kamise and Matsui (1994) said “there exist core characteristics of each
blood type, and the entire contents are formed around them” (Appendix
D). Thus, the differences by the blood type are heavily depended upon
“original” words. In many cases, differences did not appear if
“similar” words were selected.
Consistency with Preceding
Studies
Preceding research by psychologists had concluded that there appeared no
difference in personality among blood types, and that even if there was
a difference predicted by the blood type, it was assumed to be the
result of self-fulfillment prophecy phenomena (Kim et al., 2007;
Sakamoto and Yamazaki, 2004; Yamaoka, 2009; Yamazaki and Sakamoto, 1991
& 1992). However, the same differences in scores were found in the
group who reported no blood type personality knowledge (“no-knowledge”
group), although the values were smaller; all four items showed the same
results as those shown for blood type characteristics in the preceding
psychology papers (Sato et al., 1991; Watanabe 1994; Yamazaki &
Sakamoto, 1991& 1992 – Tables 2-4). Therefore, it is highly likely
that differences by blood type are real, not caused by self-fulfilling
prophecy phenomena; no difference in the “no-knowledge” groups were
caused by Type II errors.
In addition, backed on this study’s AI result, it was suggested that
these phenomena occurred not only because of the wording of question
items, but also because gender and age were not taken into account
(Chart 1). The experimental blood type predictions by AI (Amazon Machine
Learning) in this study found that adding non-blood type variables, such
as gender or age, to the training data considerably increased the
accuracy. Moreover, when performing blood type prediction, AI sometimes
failed to build its machine learning models, if gender or age of the
training and prediction data were different. In this respect, AI
technology may suggest that factors such as gender and age affect the
characteristics of blood type. Therefore, gender, age, and other factors
may offer a better explanation, even if past data were inconsistent.