I would add another criterion, which is qualitative, and therefore not reducible to a quantitative rule: 3. Use your professional judgement. Does the pattern of factor loadings make sense? For example, if the variables are item scores on a multi-dimensional instrument, can you see a meaningful connection among the items which load highly on a particular factor? The "eigen-value greater than 1" criterion is very arbitrary, and in interpreting a factor analysis matrix of item scores, I often discard numerous factors which meet the eigen-value criterion but fail to make any sense when I apply my judgement to the pattern of loadings. I can reduce all this to a single maxim: Factor analysis is an art as well as a science. Paul Gardner Alex Yu wrote: > > There are several rules. The most popular two are: > > 1. Kasier criterion: retain the factor when eigenvalue is larger than 1 > 2. Scree plot: Basically, it is eyeballing. Plot the number of factors > and the eigenvalue and see where the sharp turn is. > > Hope it helps. > Chong-ho (Alex) Yu, Ph.D., CNE, MCSE > > On Tue, 2 May 2000 [EMAIL PROTECTED] wrote: > > > Would any of you know a rule of thumb for selecting the proper (of > > optimal) number of factors to be extracted from a factor analysis. > > Also, how many variables can there be in such factor (is two variable > > in one factor not enough?).
begin:vcard n:Gardner;Dr Paul tel;cell:0412 275 623 tel;fax:Int + 61 3 9905 2779 (Faculty office) tel;home:Int + 61 3 9578 4724 tel;work:Int + 61 3 9905 2854 x-mozilla-html:FALSE adr:;;;;;; version:2.1 email;internet:[EMAIL PROTECTED] x-mozilla-cpt:;-29488 fn:Dr Paul Gardner, Reader in Education and Director, Research Degrees, Faculty of Education, Monash University, Vic. Australia 3800 end:vcard
