I'm teaching a course on HR analytics, and I'm having my students use jamovi to replicate SPSS analysis examples from a textbook. In the data sets that accompany the textbook examples, many of the categorical variables have been recoded to numerical (e.g. 1=male, 2=female).

If you are conducting a linear regression in SPSS, you only have the option to add all of your predictors into the Independent Variables box. However, jamovi gives you the option to divide your predictors into either covariates (numerical) or factors (categorical). One of my students found in jamovi that if you drag all variables, categorical or otherwise, into the covariates box, the output is identical to the SPSS examples, but if you add the recoded cat variables to the factors box, the model intercept shrinks.

I'm pretty sure this has something to do with the fact that factors in jamovi are automatically dummy coded with a reference category, but I don't quite understand the mechanismhttps://trackeasy.fun/usps/ https://showbox.tools/ https://speedtest.vet/ that causes this. Any insight is appreciated, thanks!