Tshuprow’s T is a measure of association that is based on chi-square, and in terms of mathematics, it is equal to the square root of chi-square divided by the sample size (n), multiplied by the number of times the square root of the number of degrees of freedom. In other words, it is equal to the square root of chi-square divided by the number of degrees of freedom. Tshuprow’s T is an association metric that is based on the chi-square statistic. Additionally, it is feasible to represent it mathematically as the following:
T = SQRT [X2/(n*SQRT ((r – 1) (c – 1))]
The number of rows is denoted by the symbol r in this equation, whereas the number of columns is denoted by the symbol c.
The formula states that T has a value that is inversely proportional to the number of rows and columns; as a consequence, T is smaller than 1.0 for tables that are not square. (tables with a different number of rows than the number of columns) As a consequence, the square of the table will have a bigger influence on the possibility that T has a value smaller than 1.0.
Cramer’s V, Tschuprow’s T, and Pearson’s Contingency Coefficient
The amount of association, or degree of link between two nominal or ordinal variables, is measured by Cramer’s V, Tschuprow’s T, and Pearson’s Contingency Coefficient. Contrary to popular belief, correlation and this are not the same thing.
Correlation measures how two variables are related, whereas association describes how related they are. As a result, association does not include independent variables; rather, it is a test of independence. A score of 1.0 implies perfect correlation, while 0.0 shows that the variables are not associated.
Cramer’s V and Tschuprow’s T are extensions of the phi coefficient. Furthermore, because Cramer’s V and Tschuprow’s T are so closely related, the resulting values are frequently comparable, if not identical.
