Date:         Mon, 22 Jul 1996 07:36:47 +0100
Reply-To:     Jose Luis Alabart <joseluis@MIZAR.CSIC.ES>
Sender:       "SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>
From:         Jose Luis Alabart <joseluis@MIZAR.CSIC.ES>
Subject:      Re: Factor analysis

>Paul Carreno <PCARRENO@ACSERV.AD.COWAN.EDU.AU> wrote:
>>
>> Hi SASlers,
>>
>> I was hoping that someone can point me in the right direction...
>> I have been asked to do a Factor Analysis on some data....
>> It seems that the Proc Factor procedure maybe the appropriate one to
>> use...
>> My problem is that I am not a statistician but a computer person so
>> I dont know how to prepare the data to put it  thru the test....
>>
>> I have 3 very similar medical tests for each of 120 patients:
>>
>> TEST  1....20 questions with answers from 0 to 5
>>                   0 indicates "No Problem"
>>                  1 to 5 indicate "Various degree of the  problem"
>>
>> TEST  2....22 questions with answers from 0 to 4
>>                   0 indicates "No Problem"
>>                  1 to 4 indicate "Various degree of the  problem"
>>
>> TEST 3....25 questions with answers from 0 to 5
>>                   0 indicates "No Problem"
>>                  1 to 5 indicate "Various degree of the  problem"
>>
>> Each test has a total score by adding all the scores for each
>> question.
>>
>> ***The client needs to know what problems go together so to speak...***
>>
>> 1: DO i use each test  total scores for the factor analysis?
>>
>> 2: Do I just have 2 variables per question, ie. 0= No Problem; 1=Problem?
>>
>> 3: Do I just run the factor analysis with variables as they are?
>>
>> Hpw do I prepare  the data to run the proc factor procedure?
>>
>> Thanks for your assistance...
>> PC
>> *******************************************************************
>> Paul Carreno
>> Research Analyst-Measurement, Assessment and Evaluation Laboratory
>> Edith Cowan University, Perth - Western Australia
>> E_mail:  p.carreno@cowan.edu.au         Ph:  61-09-273 8384
>> *******************************************************************
>> * I used to be uncertain of myself but now... I'm not so sure.    *
>> *******************************************************************
>
 
 
        I think that in your case, as you have categorical variables, it
would be more appropriate to use Multiple Correspondence Analysis (MCA
option of PROC CORRESP (SAS/STAT module). Is similar to factor analysis for
categorical data.
 
 
        J.L. Alabart
        Servicio de Investigacion Agraria
        Apdo. 727
        50080 Zaragoza (Spain)
        FAX:   (Spain) 76 57 55 01
        Voice:  (Spain) 76 57 63 36
                    (Spain) 76 57 63 11
        Tx.    58194 IDAE E
        E-mails: JOSELUIS@mizar.csic.es (or)
                    ALABART@cc.unizar.es