Tetrachoric Association Under The Assumption Of Normality | ||
| Baghdad College of Economic sciences University | ||
| Article 1, Volume 0, Issue 47, October 2018, Pages 439-450 | ||
| Author | ||
| Dr. Ahmed S. El-Aloosy | ||
| Abstract | ||
| Numbers can’t “talk” but they can tell as much as your human sources can. But just like with human sources, YOU HAVE TO ASK? By the expression categorical data, we mean data which are presented in the form of frequencies falling into certain categories or classes. A categorized "variable" may simply be a convenient classification of a measurable variable into groups, in the manner already familiar to us. On the other hand it may not be expressible in terms of an underlying measurable variable at all. For example, we may classify by : (a) their height, (b) their color,(c) their favorite games. Here (a) is a categorization of measurable variable, but (b) and (c) are not. There is a further distinction between (b) and (c) , for hair color itself may be expressed on an order scale ,according to pigmentation from light to dark. This is not so for (c) .We refer to (b) as an ordered classification or categorization, and (c) as an unordered one. There is a further point to be born in mind: on occasion, the two variables being investigated may simply be the same variable observed on two different occasions, e.g.,( before and after some events ) or on the related samples e.g., father and son, husband and wife, etc..).We shall refer to such situation as one with identical categorization. Identical categorization may, of course, be of any of the types (a) ,(b)or (c). Our interest in categorical data associated with two or more variables expressed in categorical form, there expression called a contingency table Types of variables (i) Qualitative, ex. Eye color, hair color etc. (ii) Quantitative: ex. Weight, height etc. | ||
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