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From |
Patricia Sourdin <patricia.sourdin@adelaide.edu.au> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: -factor- with binary variables |

Date |
Sun, 28 Nov 2004 21:04:02 +1030 |

Thanks Clive, I'll check out the references. Quoting Clive Nicholas <Clive.Nicholas@newcastle.ac.uk>: > Patricia Sourdin wrote: > > > just a query on -factor-. > > I am trying to construct an index where I have five variables which are > > binary > > indicators. > > I have read somewhere that it is not appropriate to use factor analysis if > > the > > variables are binary. Can anyone confirm, please? > > Well, if you ever read what Chatfield and Collins (1980) had to say (or, > should I say, spit?) on CFA, as they prefer to call it, it's such a > useless and unreliable method of data analysis (largely, they say, because > it's difficult to replicate), that there's little point in wasting your > time doing it! I don't entirely share this view, however. :) > > The whole point of factor analysis, as I understand it, is to explore (in > a preliminary fashion) correlations between variables that appear to 'hang > together', which in turn _could_ be combined into new variables in further > analysis if it were both valid and desirable to do so. > > It's no accident that FA was part-invented by Karl Pearson back in the > early 1930s. Strictly speaking, you're not meant to run Pearson > correlations between binary/discrete variables because they are designed > for continuous variables only. You use chi-square tests for two binary > variables and eta-coefficient tests if one variable is continuous and the > other is discrete. But as Eric Morecambe would have said, "Come on now, be > honest!" How many of us have run Pearson correlations inappropriately? I > know I have: and I'm not proud of myself, either. > > Having flicked through perhaps one of the most accessible books around on > factor analysis (Kline, 1994), although he does not say that the use of > binary variables is disallowed, _all_ of the examples in the book > exclusively use either scale measures or naturally continuous scores, such > as years of education or age. Therefore, I would advise against using > binary variables in factor analysis. > > > Also, would -pca- be an alternative in this case? > > Principal components analysis is similar to FA in that it's a data > reduction technique. However, the 'factors' extracted in FA are > hypothetical: it's left to you to describe why the variables that form the > factor(s) just extracted have something in common. PCA is rather more > autistic in its approach: in practice, it's all about estimating how much > variance the first couple of PCs account for, regardless of whether they > _really_ have something in common or not. Thus, dropping binary variables > into a PCA would make it no more valid, in my view. > > CLIVE NICHOLAS |t: 0(044)7903 397793 > Politics |e: clive.nicholas@ncl.ac.uk > Newcastle University |http://www.ncl.ac.uk/geps > > References: > > Chatfield C and Collins AJ (1980) INTRODUCTION TO MUTLIVARIATE ANALYSIS, > London: Chapman and Hall. > > Kline P (1994) AN EASY GUIDE TO FACTOR ANALYSIS, London: Routledge. > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: -factor- with binary variables***From:*Patricia Sourdin <patricia.sourdin@adelaide.edu.au>

**Re: st: -factor- with binary variables***From:*"Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>

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