00:01
今日はエニアグラムのお話をさせていただこうと思います。 Today, I am going to talk about Enneagram.
エニアグラムとは人の性格を9つのタイプに分類するような性格の類型論のことだそうです。 Enneagram is a category of personality that categorizes people into 9 types.
9つなのでちょっと多いんですけども、紹介したほうがいいかなと思いますので、全部ざっと紹介していきます。 There are 9 types, so it's a little too many, but I think it's better to introduce them all.
本当は質問書とかインタビューを用いたりして、このタイプ分けを行っていくそうなんですけども、今日はそこまではやらないので、 Today, I'm not going to do that, so if you ask me something like this,
馴染みがちょっと出るんじゃないかなと思いますが、本当はそういうこれっぽいなみたいな分け方でやるのは良くないと思うんですけど、 I think you'll get used to it, but I don't think it's a good idea to do it like that.
最初のザッとした理解をしてもらえればと思っています。 I hope you can understand the first roughly.
一つ目、改革する人。完璧主義で倫理的、秩序を重んじる。正義感が強く、ミスや不正を嫌うような人だそうです。 The first one, people who reform. They have a sense of perfectionism, ethics, and order. They have a strong sense of justice, and they hate mistakes and mistakes.
二つ目、助ける人。いいですね。The Helperっていうらしいです。英語だと。 The second one, people who help. It's nice. It's like The Helper in English.
人に尽くすことを求め、愛されることを重視する。世話好きで、気配り上手。 They seek to serve others, and they value being loved. They are good at taking care of others.
三つ目、達成する人。成功を追求し、結果を、成果を重んじる。効率的で、目標志向である。 The third one, people who achieve. They pursue success, and they value the results. They are efficient, and they have a goal.
四つ目、個性的な人。独創的で、自主性豊か。自己の特別さを求める。独自の世界観を持つ。 The fourth one, people who are unique. They are unique, and they have a sense of self-sacrifice. They seek to be special, and they have their own worldview.
五つ目、研究する人。知識を重視し、独立心が強い。考えることが好きで、人と距離を取ることもある。 The fifth one, people who research. They value knowledge, and they have a strong sense of independence. They like to think, and they sometimes distance themselves from others.
六つ目、忠実な人。安定や安全を求め、不安に敏感。信頼できる人やルールを大切にする。 The sixth one, people who are loyal. They seek stability and security. They are anxious and sensitive. They value people who can trust and the rules.
七つ目、熱中する人。楽観的で、好奇心旺盛。自由を求める。退屈を嫌う。 The seventh one, people who are passionate. They are optimistic, curious, and they seek freedom. They hate boredom.
八つ目、挑戦する人。自己主張が強く、支配的でパワフル。良さを見せたがらない。 The eighth one, people who challenge. They are strong, dominant, and powerful. They don't want to show weakness.
最後、九つ目、平和を求める人。昭和を重視し、対立を避ける。落ち着いた雰囲気。 The last one, people who seek peace. They value harmony and avoid confrontation. They are calm.
平和を求める人、昭和を重視する人、楽観的で、好奇心旺盛。 The ninth one, people who are passionate. They seek freedom and peace. They hate boredom. They hate the violence.
十一つ、人を放棄する人。この英語がとても素敵だと思いますが、こういう九つだそうです。 These nine words are great.
I think there are a lot of things that can be applied, but there is a way to divide it like this.
In psychology, there is a way to divide the personality that is often used, so there is something like the Big Five that I often introduce.
As I said, there are five, but this Big Five and Enneagram and MBTI are the same, but the big difference in the type theory is that
03:02
this person doesn't decide on this type. For example, the benefits of extroversion are 4.6 out of 5,
the benefits of openness are 3 out of 5, and the tendency of neurasthenia is 1 out of 5.
So, of course, there are people who have a high degree of extroversion and a high degree of openness,
and there are people who have a higher tendency for neurasthenia.
I think there is a big difference in thinking here.
I don't think there is any good or bad at this stage.
This is a paper that I researched how much it can be used in research and whether there is a scientific basis.
I hope you can get a rough overview of the research of Enneagram.
In this research, it is said that 104 studies are being reviewed.
First of all, there is a limit that there are not so many problems that are related to reading.
So I have a feeling that this research is on the rise.
The PIC-5 I just introduced is a relatively well-researched and I often use it, but I think there is not much research compared to it.
The paper I am introducing now is from 2021, and I think it is a reliable journal called Journal of Clinical Psychology.
However, I thought that if this paper was reviewed so firmly, it would be better to quote it more,
but on Google Scholar, there are only about 50 quotes, and even though it is a paper four years ago,
I thought that it was less than I was doing my best to research.
I thought that this Enneagram research theme itself showed that it was not studied much.
In this paper, we are investigating whether there is a scientific basis from various perspectives.
06:03
I'm a little hesitant about how much I'm going to introduce,
but I'd rather start with a rough conclusion.
Unfortunately, I think that there are many research results that show that the degree of reliability is low.
I'm not saying that there is absolutely no degree of reliability, but there are some studies that say there is,
but there are some studies that say there is not, and I think there are more of them.
However, I think that there is still a limit to the degree of reliability.
Why do I say that?
For example, I introduced nine types earlier.
If you use a statistical book called quantum analysis,
you can calculate that there are 9, 10, and 8 sets.
When I calculated it, it seemed that there were a lot of things that were not 9.
I said that I would divide it into 9,
but I wonder if it is a bit of a problem that it does not become 9 on the statistical numerical table.
From the field of psychology that I am doing,
there seems to be a problem that the degree of consistency between tools is low.
When you measure the enneagram with this question paper and when you measure the enneagram with another question paper or method,
you are measuring the same enneagram, so you want it to be the same result.
In this case, what about reliability?
To give you a little more detail, there seems to be a concept called wing.
I think it's the order of the nine I introduced,
but there seems to be a hypothesis that the types adjacent to each other are similar.
There seems to be a result that it was not supported statistically.
09:06
As long as I read this paper, I feel that there is such a problem.
I think that it is such a view.
I'm a beginner, and now I'm in a very good age,
and Kagushun-san also introduced it,
but the deep research function of ChatGPT's pro plan has been implemented recently,
so I asked what the latest research on enneagram was like and what the degree of consistency was like.
I'm going to put a link to the results I heard,
so please take a look if you like.
I was surprised.
Kagushun-san also said he was surprised, but it's really amazing.
This is a really surprising function.
If this is just a researcher review, I don't think it's really necessary.
I've accessed a lot of papers, and the summary is very easy to understand.
Please read it.
In fact, the paper I introduced today is the first to be read.
So I think my choice of paper was not wrong,
but there is still a basis for it.
On the other hand, there is also a claim that there is also a strength.
I think that's what research is all about.
If there is criticism, there are people who support it.
Today, I started with a little criticism and laughed at the scientific basis,
but there is also research that is not.
I'm thinking of introducing that next time,
so please forgive me for one paper.
I hope you will take a look at the output of GPT.
Next time, I would like to introduce the strength of Enneagram,
which cannot be handled in Big 5.
I think it's a fair attitude for researchers who are not interested in this research theme.
12:06
Please listen again at that time.
If you are interested in Enneagram, I think you can look it up more.
So, what is Enneagram?
Today, I introduced the scientific basis of Enneagram.
If you read a paper like this, you may think that it is not necessary,
but I don't think so.
I don't think there's much to say about the blood type,
but I think it's interesting to think about the meaning of what's left.
I would like to do that next time.
I think it's good to read a paper like this and introduce it,
and then I think it's good for everyone to think about it.
If you can have a strong partner like AI and deep research here,
I think it will be more fun to think like this,
and I think it will be more fun to discuss and research.
Looking at the deep research,
I felt that it would be more important to decide where to bring your research question
and where to get new data by leaving it to machines and AI.
I'm sorry, I've been talking about that today.
I think it would be nice if you could feel that part.
Thank you for listening to the end.
Let's have a good day today.
It was Junpei.
With all my heart.
