Date: Thu, 15 Jun 2006 11:36:45 -0400
Reply-To: Statisticsdoc <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Statisticsdoc <firstname.lastname@example.org>
Subject: Re: Logistic regression help
Content-Type: text/plain; charset=utf-8
Categorizing continuous variables into categorical variables can result is a considerable loss of statistical power because the test for the categorized version of the variable uses more degrees of freedom that the test for the continuous variable. In addition, categorizing a continuous variable can result in a loss of predictive information.
---- Lou <charl_bean@YAHOO.CO.UK> wrote:
> Dear list
> I am trying to carry out a logistic regression analysis and have a quick
> question with regards to the best way to input my independent variables.
> I have three input variables: ethnicity (5 groups), age and deprivation
> score. Although age and deprivation score are continuous variables, I
> have also been asked to split them into groups (4 for age and 5 for
> deprivation) which are pre-determined by previous work on this subject
> matter. The dependent variable is simply whether or not a person took a
> particular test.
> I have tried generating models both with the age and deprivation variables
> as they are and also with the new categorical age and deprivation
> variables. However, when looking at interaction terms, I find that the
> interaction between age and deprivation is significant when they are input
> as the continuous variables but not significant when I used the
> categorical versions. Why would this happen? Furthermore, which is the
> best way to go? I have read information on logistic regression until my
> head hurts, but still dont feel completely satisfied as to how I should
> determine the best model possible.
> Any advice would be appreciated please!
For personalized and experienced consulting in statistics and research design, visit www.statisticsdoc.com