If I have the following Hypothese:H1: Women tend

Answered question

2022-05-15

If I have the following Hypothese:

H1: Women tend to watch romantic films more than men 

Apparently it's a chi-square because I'm measuring relationships between expected frequency and Observed frequency and also because Gender is on the nominal level. But what if this is designed for a survey in which questions are on the nominal, Ordinal and interval level (Likert scale - differential sementic) will the outcome still be a Chi-square?

Answer & Explanation

Mr Solver

Mr Solver

Skilled2023-05-14Added 147 answers

If you have the hypothesis:
H1: Women tend to watch romantic films more than men.
Based on your description, it seems that you are interested in examining the relationship between gender (a nominal variable) and movie genre preference. In this case, a chi-square test would indeed be appropriate to analyze the data.
The chi-square test is commonly used to assess the association between two categorical variables. It compares the observed frequencies in each category to the expected frequencies under the null hypothesis of independence. The test determines whether there is a significant association between the variables.
In the scenario you described, where the survey includes questions on the nominal, ordinal, and interval levels, the outcome will still involve a chi-square test. This is because the chi-square test is specifically designed for analyzing categorical data and assessing the independence or association between categorical variables. It does not take into account the specific measurement levels or the nature of the survey questions.
Therefore, regardless of whether the survey questions are on the nominal, ordinal, or interval level, if you are examining the relationship between gender and movie genre preference, you can use a chi-square test to analyze the data.
Remember that conducting a chi-square test involves formulating the null and alternative hypotheses, calculating the chi-square test statistic, and determining the p-value associated with the test statistic to assess the statistical significance of the relationship.

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