The general approach to describe discrete / categorical variables is using counting techniques that are summarized into frequencies and proportions / percentages. When dealing with a conceptual component made of complementary indicators, Multiple-Responses Sets (MRS) as composite variable can be generated using Multiple-Responses Analysis (MRA) technique; but whether individual indicator’s score or composite-aggregated scores within conceptual component, the statistical outputs are generated in term of frequency and proportion. But the statistical outputs for discrete / categorical variables are presented differently by different scholars, statisticians or researchers as if there was no set standard. Following convincing scientific and statistical analysis and demonstrations, it is proven that the acceptable standard is proportion coming before frequency [e.i. 50% (30) instead of 30 (50%)] given the mathematic conservation law that scientifically gives priority to proportion, which is termed proportionate-conservation law in this article. I therefore recommend that this standard be followed for the uniformity of the presentation of statistical outputs for discrete / categorical variables.
Discrete / categorical variables; Analysis; Counting techniques; Outputs; Frequency; Proportion / Percentage; Standard
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