Ah! You're totally right, thank you for catching that in my model. When I re-run the model, I receive the results below, and the AIC is a bit lower (although not by much), which I think is good.

Call:

**polr(formula = Rating ~ Parent_Gender + Child_Gender + **

Parent_Gender * Child_Gender, data = filtered,

Hess = TRUE)
Coefficients:

Parent_Gender Child_Gender

0.02860946 0.20393190

Parent_Gender:Child_Gender

0.11280902

Intercepts:

Low |Medium Medium|High

-4.1682561 0.7750662

Residual Deviance: 22905.84

AIC: 22915.84

(903 observations deleted due to missingness)

>

> summary_table2 <- coef(summary(model_int))

> pval <- pnorm(abs(summary_table2[, "t value"]), lower.tail = FALSE)*2

> summary_table2 <- cbind(summary_table2, "p-value" = round(pval,3))

> summary_table2

Value Std. Error t value p-value

Parent_Gender 0.02860946 0.05200919 0.5500846 0.582

Child_Gender 0.20393190 0.06321883 3.2258097 0.001

Parent_Gender:Child_Gender 0.11280902 0.07490642 1.5059993 0.132

Low|Medium -4.16825611 0.07876843 -52.9178539 0.000

Medium|High 0.77506618 0.04077741 19.0072439 0.000

So, if I'm interpreting these results correctly, this means that the interaction between a parent's gender and their child's gender does not have a significant impact on the child's rating (high, medium, low).

However, a child's gender does have a significant impact on how they are rated? How is that the case?

I apologize for all of my questions, it's my first time running an ordinal logistic regression and I really want to understand what it means. I'm currently using this article

https://stats.idre.ucla.edu/r/faq/ologit-coefficients/ as a reference.

Thank you again!