自陳法乃是評量 A 型行為傾向最普遍的方式。該評量方式通常以總分為基礎，把人分為 A 型與 B 型兩種類別，但是，A 型行為傾向並不是一個整體性的建構，因此，過度簡化地把人分成 A、B 兩類，將造成 A 型行為傾向在預測心血管疾病上不一致的情況。有人建議必須在連續性量尺上，針對某些特定指標作分類，可以解決此一問題，而模糊集合量尺可以是解決此一議題的新方向。 因為模糊集合基本概念，在於人經常處於多元思維狀態。簡單二分法，實在無法精確描述人類多元行為，因此我們考慮將模糊分類應用於 A 型量表分析，以解決過度簡化分類問題。就複雜多變的人類行為或思維體系，以 A 型行為傾向的分類為例，提供一套更精確合理的分析方法與更多的訊息，做出明確細緻的決策。

The most common way to assess type A behavior pattern (TABP) is self-report measure. People were placed in two categories (A and B) based on total score. However, the type A is not a global construct. Therefore, the oversimplified categorical method (A and B) have emerged inconsistency in type A findings which predict coronary endpoints. Some suggests that accessing specific indicators on continuous scale can solve this problem. But, the current alternatives proposed by other researchers are inconvenient in research and practice. Fuzzy logic based on a continuous scale can be a brand new solution for this issue. In this paper we propose a new analytical method for the type A research. The advantages of eveluation with fuzzy statistical analysis include: (i) Evaluation process becomes robust and consistent by reducing the degree of subjectivity of the evaluator. (ii) Self-potentiality is highlighted by indicating individual distinctions. (iii) Provide the evaluators with an encouraging, stimulating, self-reliant guide that emphasizes on individual characteristics. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional clustering did. To demonstrate the effectiveness and advantages of the new method, the results were presented in comparison with those by the traditional method using dualistic and mutually exclusive categorical analyses.