Re: SEM and Confirmatory factor analysis

[Stanley Mulaik]

--
In article 83ftvs$qjq$[EMAIL PROTECTED], "Haider Al-Katem"
[EMAIL PROTECTED] wrote:


 What is the difference between SEM and Confirmatory factor analysis?
 Can I perform either of those statistical analyses on a sample size of 50?


SEM (Structural Equation Modelling) is more general than confirmatory factor
analysis.  Confirmatory factor analysis only models causal relations from
latents to manifest indicators, leaving the latent variables simply
correlated with one another.  SEM allows causal relations between latents,
where some latents are effects of others, and they in turn of even others.

 

Re: Factor analysis

[Stanley Mulaik]

--
In article 83ftip$qdf$[EMAIL PROTECTED], "Haider Al-Katem"
[EMAIL PROTECTED] wrote:


 Hi,

 I have conducted a factor analysis on some questionnaire items. The
 dependent variables that I am measuring for example ('Intention To Buy',
 'Attitude towards a product'  and 'Trust in buying the product from a
 merchant' ) seem to load significantly high on two factors which leaves me
 with a NOT SIMPLE FACTOR STRUCTURE.

 I am assuming that since 'Intention To Buy', 'Attitude towards a product'
 and 'Trust in buying the product from a merchant'  all seem to be some type
 of an ATTITUDE , the significantly high factor loadings on the two factors
 may be justifiable.

 My questions are:

 1. Are my above interpretations of the result correct?

 2. If not, is there a statistical method that can help me overcome this
 'non-simple factor structure'?



You haven't indicated exactly what the indicators are of these dependent
variables.  If you only have three indicators  then you can only get one
common factor for them.  Two factors are underidentified for three
indicators.

Also beware of a possible simplex for your variables or subset of them.
In that case a common factor model is not sufficient but may be misleading
in fitting fairly well.