Hello,
I am an economics student doing an undergraduate empirical dissertation using data from the European Working Conditions Survey, where my DV is job satisfaction (ordinal, 4-point scale) and my two main predictor variables are recognition from employer (ordinal, 5-point likert scale: strongly disagree... strongly agree) and working from home (WFH).
The main issue I have is with my WFH variable. From the survey it is measured as follows: “during the last 12 months in your main paid job, how often you have worked in your own home?” 1=Daily; 2=Several times a week; 3=Several times a month; 4=Less often; 5=Never.
Due to the ordinal nature of my DV, I am conducting ordinal logistic regression, and of the two options for handling my ordinal predictors (treat as continuous or categorical), my supervisor advised that I treat them both as continuous. I am aware of the drawbacks of doing so (eg underlying assumption of equally spaced intervals), but have been told for purposes of undergrad dissertation, this is ok and in much existing literature, ordinal variables are treated as continuous in the same way.
My first question is when treating WFH as continuous, would I keep the original coding so it is just a continuous scale from 1 to 5, or is there a way to re-code it so it better approximates a continuous scale?
Secondly, I want to discuss marginal effects but this does not seem intuitive for my predictor variables that don't have an easily quantifiable "one-unit change".
For example if I had age as my continuous predictor variable, my interpretation would be "an increase in age by one year causes a beta change in the log-odds of reporting very satisfied with job."
But I am not sure of the equivalent of a one unit change in my WFH variable the way it is measured.
So how would I frame / quantify this marginal effect for my WFH variable? (and for that matter my recognition variable too?)
I would really appreciate any help/advice, thanks!
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