Non-parametric tests measure variables at
(A) Ratio level
(B) Interval level
(C) Nominal level
(D) Non-conditional level
Correct Ans: (C)
Explanation:
Non-parametric tests measure variables at the nominal level, meaning they analyze categorical data rather than numerical values. Since these tests do not assume a specific distribution, researchers use them when data lacks normality or when working with ranked or unordered categories.
For example, when studying media preferences, researchers may categorize responses as TV, radio, online news, or newspapers. Since these categories have no numerical value or order, they require non-parametric tests like the chi-square test to analyze relationships between them. Because these tests handle small sample sizes and ordinal data, they offer flexibility in media and audience research.
Now, let’s examine the incorrect options. Ratio level measurement includes absolute zero points, such as age or income, making it unsuitable for non-parametric tests. Interval level measurement deals with numerical differences but lacks a true zero, like temperature scales. Non-conditional level is not a recognized statistical term, so it does not apply here. Clearly, none of these options fit the nature of non-parametric tests.
In conclusion, non-parametric tests measure variables at the nominal level, making them essential for categorical data analysis in communication research. Since media studies often involve non-numerical classifications, researchers rely on these tests to explore trends, preferences, and audience behavior without requiring strict statistical assumptions.
