If you code the yes/no question, as binary (0 for no, 1 for yes) you can calculate pearsons r coefficient, which will tell what is the association between continous and binary variables. Check this link for how to do it in SPSS: https://www.statisticssolutions.com/point-biserial-correlation/

Because one variable is ordinal the test criterion should be nonparametric in which case the correlation coefficient should be Spearman's rank or Kendall's tau.

Thankyou! if I did the non-parametric correlation coefficient spearman’s would that work With the scalar data?

If you regard your dependent variable as ordinal (1=No, 2=maybe, 3=Yes) (with numbers indicating order but not scale), then you should consider ordered logit or ordered probit (not to be confused with binary logistic regression or similar). These two techniques have different theoretical backgrounds but in practice give similar outcomes. One or both of ordered logit or ordered probit should be available in your stats package.

Ordinal logistic regression might work, provided you code the dependent variables as 1=No, 2=Maybe, 3=Yes. ANOVA with the dependent variables as factors will give you an R-square as a measure of the strength of the relationship. If you want to include all the IVs simultaneously in one model, you could use MANOVA. In the cases of ANOVA or MANOVA, you will not rely on the ordinality of the DV. A completely different approach would be to apply Discriminant Analysis or even CHAID.

Francois E Steffens Thanks for your answer! Say I use ordinal logistic regression with the scale variable as IV and ordinal as DV, does it matter that the scale variable isn't normally distributed? Can I do anything to make it so it is before doing the test?

Pippa Mullins, I agree with Francois E Steffens in applying ordinal logistic regression or CHAID. Discriminant analysis is a bit more complicated due to assumptions.

ordinal logistic regression

Logistic regression, including ordinal logistic regression, does not require the independent variables to be normally distributed.

I suggest to go for log linearity test and apply logit in SPSS.