Final answer:
In categorical data analysis, a participant must fall in only one category to prevent overlap and ensure clear data analysis. The minimum expected frequency per cell is context-dependent but should generally not be less than 2, and responses should not be dependent on each other for certain statistical tests.
Step-by-step explanation:
Regarding categorical data analysis, the statements that hold true include that a participant must fall in only one category. This ensures clarity and prevents overlap in data analysis. It's common to require that each cell in a contingency table has an expected frequency of at least 5 for the chi-square test to be valid; however, some statisticians accept a minimum of 1, though generally not less than 2. Hence, the statement that none of the expected frequencies should be less than 2 is somewhat context-dependent on the statistical test being used. Regarding independence, it's true that no response should be related to or dependent on any other response, as dependence can skew results and violate assumptions of some tests, such as the chi-square test for independence. Therefore, the statement that would hold true across different categorical data analysis contexts is c) A participant must fall in only one category.