In communication research, the chi-square statistic is also referred to as

(A) Correlation

(B) Dispersion

(C) Crosstabs

(D) Convenient statistic

Correct Ans: (C)

Explanation:

Researchers refer to the chi-square statistic as crosstabs (cross-tabulation) in communication research. Since they often analyze relationships between categorical variables, they use chi-square tests to determine whether distributions differ significantly.

For example, when researchers study news consumption habits, they examine whether gender influences the preference for television or online news. They organize data in a contingency table and apply the chi-square test to check if the relationship occurs by chance or represents a meaningful pattern. Because this method works with nominal (categorical) data, researchers frequently use it in surveys, media research, and audience analysis.

Now, let’s examine the incorrect options. Correlation measures the strength and direction of relationships between numerical variables, so it differs from chi-square’s categorical analysis. Dispersion describes how data spreads in statistical distributions, making it unrelated to chi-square tests. Convenient statistic is not a standard term in research methodology, so it does not apply here.

In conclusion, researchers rely on the chi-square test (crosstabs) to analyze categorical data in communication studies. Since media and audience research often involve non-numerical classifications, this method helps uncover significant patterns and relationships. By understanding chi-square analysis, researchers improve data interpretation and strengthen findings in mass communication.