# Defining categorical variables

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## Defining categorical variables

 Hi guys, 1 - When we use SPSS to define categorical variables (change contrast to indicator), when working with logistic regression, if we choose the reference category to be last for example, does last mean last in numbe or last alphabeticallyr? For example, I have 1- white, 2- black, 3- asian. If I choose to define the last category as the reference, will SPSS choose 3- asian, because it is the last in the numerical order, or will it choose 1- white because it is the last category alphabetically? I read books giving different instructions so I am confused about this. I have another simple question about categorizing. If I have a variable with four categories, and the one i want as a reference is the one in the middle, do I have to move this category to last or first so I can set the reference category? Also, how do i know it changed to last or first? does last show a < sign after ther variable and does first show a (first)
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## Re: Defining categorical variables

 I am also interested in this topic as I am currently working on a meta-analysis. I plan to use regression analysis to explore the effects of various study characteristics on the effects sizes. Many of the independent variables are categorical (e.g. theoretical orientation of the interventions). I have assigned numbers to all the categories. I read: “The predictor variables can be dichotomous…Categorical variables can, of course, be “dummy” coded as a set of dichotomous variables, one less than the number of categories (Cohen & Cohen, 1975)” (Lipsey & Wilson, 2001 p. 138) If anyone can tell me how to go about this, using SPSS, I would be very grateful. Thanks! Laurie References --------------- Cohen, J. & Cohen, P. (1975). Applied multiple regression/ correlation analysis for the behavioural sciences. Hillsdale, NJ: Lawrence Erlbaum. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks: Sage.
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## Re: Defining categorical variables

 In reply to this post by Love-2 If you have SPSS CATEGORIES you can use CATREG ((nonlinear) regression for categorical variables). If you choose nominal scaling level for the independent variables, the result is identical to result of linear regression with dummy variables. If you have ordered categorical variables you can choose ordinal scaling level. Anita van der Kooij Data Theory Group Leiden University -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Laurie Petch (sent by Nabble.com) Sent: 04 August 2006 17:25 To: [hidden email] Subject: Re: Defining categorical variables I am also interested in this topic as I am currently working on a meta-analysis. I plan to use regression analysis to explore the effects of various study characteristics on the effects sizes. Many of the independent variables are categorical (e.g. theoretical orientation of the interventions). I have assigned numbers to all the categories. I read: "The predictor variables can be dichotomous...Categorical variables can, of course, be "dummy" coded as a set of dichotomous variables, one less than the number of categories (Cohen & Cohen, 1975)" (Lipsey & Wilson, 2001 p. 138) If anyone can tell me how to go about this, using SPSS, I would be very grateful. Thanks! Laurie References --------------- Cohen, J. & Cohen, P. (1975). Applied multiple regression/ correlation analysis for the behavioural sciences. Hillsdale, NJ: Lawrence Erlbaum. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks: Sage. -- View this message in context: http://www.nabble.com/Defining-categorical-variables-tf2046140.html#a5652367 Sent from the SPSSX Discussion forum at Nabble.com. ********************************************************************** This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error please notify the system manager. **********************************************************************