Question

I'm confused on how to approach this question, what statistical tests should I run to receive the required information?

The data related to the health knowledge assessment scores in continuous and the variables it should be tested against are categorical.

Test the association between the student’s health knowledge assessment score and the demographic variables (Gender, Race, and Family Income) and the behavior variables (Body Image, Sexual Activity, and Risk Behavior). Quantify the magnitude and significance of the association between the health knowledge assessment score and each of the independent variables. Make a table of the results and include the magnitude of the association, the significance, and the amount of the variability in the score explained by the variable.

Answer 1

Answer

it is clear from the question that the dependent variable in this case is health knowledge assessment scores and independent variables are gender, race, family income, body image, sexual activity and risk behavior.

We want to find out the magnitude and significance of association between dependent variable and each independent variables, i.e. we have to find the slope coefficients.

we know that ANOVA test is used when dependent variable is continuous and independent variables are categorical.

therefore, in this case, we can use analaysis of variance test or ANOVA

Answer 2

To test the association between the health knowledge assessment score and the categorical independent variables (Gender, Race, Family Income, Body Image, Sexual Activity, and Risk Behavior), you can use a series of statistical tests:

1. Descriptive statistics: Compute the mean and standard deviation of the health knowledge assessment score for each level of the categorical variables. This will give you an idea of the distribution of scores across the different groups.

2. T-tests or ANOVA: If you have two categorical variables, you can use a t-test to compare the mean scores between the groups. If you have three or more categorical variables, you can use an ANOVA to test if there are any significant differences in the means of the groups.

3. Chi-squared test: If you have categorical variables with more than two levels, you can use a chi-squared test to test for an association between the variables and the health knowledge assessment score. This test will tell you if there is a significant difference in the proportion of students with high, medium, and low scores across the different levels of the categorical variables.

4. Logistic regression: If you want to quantify the magnitude and significance of the association between the health knowledge assessment score and each of the independent variables, you can use logistic regression. This will allow you to estimate the odds ratio, which represents the change in the odds of having a high score associated with a one-unit increase in the independent variable, after controlling for other variables in the model.

Once you have run these tests, you can make a table summarizing the results. For each independent variable, include the magnitude of the association (e.g., odds ratio), the significance (e.g., p-value), and the amount of variability in the health knowledge assessment score explained by the variable (e.g., R-squared or pseudo-R-squared). This will allow you to see which variables are most strongly associated with the health knowledge assessment score and how much of the variation in the score can be explained by each variable.