Posted by
Richard Ristow on
Apr 19, 2013; 4:04pm
URL: http://spssx-discussion.165.s1.nabble.com/Dummy-Coding-Variables-tp5719548p5719565.html
At 07:17 AM 4/19/2013, JJEcon wrote:
>The DV is vehicle registrations for 2011. The IVs include price,
>manufacturer as a proxy for brand loyalty (this is the variable I
>thought needed coding), body type, customer reviews, fuel capacity,
>fuel type etc.
>
>So have I essentially gone at this in entirely the wrong way?
It sounds to me like you have been. You're trying to explain, in your
words, "vehicle registrations (as a proxy for vehicle demand)". In
this case, I don't see how you can have manufacturer as an
independent variable. *Which* car to purchase and register is as much
a part of the outcome as is *whether* to purchase and register a car.
What constitutes a data point in your dataset? A single car purchase?
In that case, the only possible dependent value seems to be 1. Or,
say, a month of registrations?
Regression is not directly about causality. But in setting up a
regression model, it's well to think what causal relationships MAY be
present, and design the model to be sensitive to them.
This isn't a problem about coding variables; it's a problem about how
you think of your study. Tell us, or tell yourself, what outcome
you're trying to explain, and what you think affects it.
To start with: It looks like you're trying to get at the decision to
purchase a car. What do you hypothesize goes into that, and how can
you measure it? And, crucially: since you're only looking at
registrations, what handle do you have on decisions *not* to purchase
a car? A decline in total registrations may reflect such decisions,
but it's not clear that you have such a time component, or anything
comparable.
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