Date: Fri, 29 Apr 2005 15:38:27 -0400
Reply-To: Darryl Wilson <dwill_22@HOTMAIL.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Darryl Wilson <dwill_22@HOTMAIL.COM>
Subject: Repost: Random effects & Repeated measures
Can anyone explain which occasions it would be appropriate to use Random
effects as opposed to fixed effects (in a regression). In an experimental
design sense a fixed effect is when we are only concerned about making
inferences about the particular treatments such as 10mg, 20 mg, 30mg. A
random effect seeks to make inferences about all treatments (population)
based on a random sample of 10mg, 20mg 30mg. This concept is
understandable, but a random effect's use in a regression model isn't.
Problem. If we are looking to model the amount of revenue expected to be
collected given a bill amount (y being the amount paid on that bill) or
the probability of paying a bill, or to build an attrition model using
many other variables as predictors (some predictors are repeated measures
such as monthly usage while other aren't such as rate or salary. The bill
amounts are taken for a 3 year period per account.
Note that all accounts may not have 3 years worth of data. This appears
to be a repeated measure problem. What models or procs would be needed to
model these two models or models like it? Would you treat this as a
random or a fixed effect? If one chooses random wouldn't it only be
because the bill amounts were chosen from a population of bill amounts per
account? If one uses all possible amounts per customer would it then be a
fixed effect? What models are procs should be used for eithr case?
Is a repeated measure model the only way to model this?