Open AccessThe Analysis of Correlation between Anxiety and Students’ Positive Attitudes toward Mathematic Using fuzzy Interval data and fuzzy correlation
Author(s) -
Hari Susanto,
Tatik Sutarti,
Agustina Sri Hafidah
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1320/1/012075
Subject(s) - correlation , fuzzy logic , anxiety , mathematics , correlation coefficient , fuzzy mathematics , pearson product moment correlation coefficient , interval (graph theory) , statistics , data collection , fuzzy set , psychology , fuzzy number , computer science , artificial intelligence , geometry , combinatorics , psychiatry
In the present paper, we suggest the dimension that the statistical concept is based on uncertainty, in this case, the analysis of the correlation between anxiety and positive attitude towards Mathematics should employ a correlation analysis based on the fuzzy concept. However, far too little attention has been paid to this issue. Over past years, the anxiety and positive attitudes toward mathematics are usually explained using a crisp data. Both are usually using a correlation analysis based on statistical concepts. In certain conditions, a crisp data cannot expound a condition of a student; therefore the fuzzy interval data is used to overcome this problem. Fuzzy interval data is obtained from the data collection process using a fuzzy scale of mathematical anxiety and students’ positive attitudes toward mathematics. Moreover, the participants were recruited from 132 Vocational High School students. This research has shown that: (1) fuzzy interval data can expound the flexible and real variables of mathematical anxiety and positive attitude instead of the crisp data; (2) mathematical anxiety has a negative correlation at a moderate level with students’ positive attitudes toward mathematics; (3) the fuzzy correlation coefficients in the form of intervals provides benefits for researchers to determine the subjectivity of data analysis results, when compared to the correlation coefficient of Pearson correlation.