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Re: st: CR and AVE for factor analysis with 2 factors
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: CR and AVE for factor analysis with 2 factors
Date
Tue, 29 Oct 2013 08:55:42 +0000
What is implied by your log is that
replace sum_factors = sum_factors + sqrt(communalities[3,1])
has the effect of replacing -sum_factors- with missing. So, please
show us that -communalities- matrix,
namely the result of your line
matrix list communalities
(I see on closer examination that the -communalities- matrix is not
the communalaties as first reported, but modified.)
No idea, sorry, about your general factor analysis question. I'm
firmly a PCA person.
Nick
[email protected]
On 29 October 2013 07:14, Florian Christian Esser
<[email protected]> wrote:
> Hi Nick, thanks a lot for your advice.
> Unfortunately, all my communalities are positive.
> Does anyone have any ideas, what else could cause the issue?
>
> Is the general approach correct, to calculate CR and AVE for both factors
> at the same time, or would I have to do it individually. I.e. I do the
> factor analysis, identify, that the indicators load on two factors and
> then calculate CR and AVE for each factor individually?
>
>> Your missing value is presumably the result of taking the square root
>> of a negative number. Is at least one of your communalities reported
>> as negative?
>>
>> That aside you have segments like
>>
>> gen nvar = e(df_m)
>> gen sum_factors=0
>> local i=1
>> while `i' <= nvar {
>> replace sum_factors = sum_factors + sqrt(communalities[`i',1])
>> local i=`i'+1
>> }
>>
>> You can simplify this. First, to hold constants, use locals or
>> scalars, not variables. Second, use -forval- to loop here:
>>
>> local nvar = e(df_m)
>> local sum_factors = 0
>> forval i = 1/`nvar' {
>> local sum_factors = `sum_factors' + sqrt(communalities[`i',1]
>> }
>>
>> Third, use Mata instead. Here is a self-contained example:
>>
>> . matrix foo = (1,2,3,4)
>>
>> . mata : st_numscalar("sum_factors", sum(sqrt(st_matrix("foo"))))
>>
>> . scalar li
>> sum_factors = 6.1462644
>>
>> However, none of these tricks can get round what appears to be your
>> problem.
>>
>>
>> Nick
>> [email protected]
>>
>>
>> On 28 October 2013 14:11, Florian Christian Esser
>> <[email protected]> wrote:
>>> Hi everyone,
>>>
>>> I am trying to do factor analysis in order to measure two strategy
>>> constructs. I have 6 indicators that load on these two constructs. Now I
>>> want to calculate composite reliability (CR) and average variance
>>> extracted(AVE).
>>> I use the following code (here for CR):
>>> ___________________________________________________________________
>>> factor zwmdiff_1 zwpdiff_1 zwpdiff_2 zwcost_1 zwcost_2 zwcost_3, pcf
>>> matrix list e(Psi)
>>> matrix list e(L)
>>> matrix psi = e(Psi)'
>>> matrix communalities = J(rowsof(psi),1,1)
>>> matrix communalities = communalities - psi
>>> matrix colnames communalities = communalities
>>> matrix list communalities
>>> gen nvar = e(df_m)
>>> gen sum_factors=0
>>> local i=1
>>> while `i' <= nvar {
>>> replace sum_factors = sum_factors + sqrt(communalities[`i',1])
>>> local i=`i'+1
>>> }
>>> generate sum_psi=0
>>> local i=1
>>> while `i' <= nvar {
>>> replace sum_psi = sum_psi + psi[`i',1]
>>> local i=`i'+1
>>> }
>>> gen cr=(sum_factors*sum_factors)/((sum_factors*sum_factors)+sum_psi)
>>> drop nvar
>>> drop sum_factors
>>> drop sum_psi
>>> list cr in 1
>>> _________________________________________________________________
>>>
>>> The problem is that once the local macro starts running it tells me:
>>> _____________________________
>>> [...]
>>> (534 real changes made)
>>> (534 real changes made)
>>> (534 real changes made, 534 to missing)
>>> (0 real changes made)
>>> (0 real changes made)
>>> (0 real changes made)
>>> [...]
>>> ___________________________
>>>
>>> and accordingly there is an empty result for CR.
>>> If I add "factors(1)" in the command line above, I get a result, but I
>>> since I have two factors, I think it is not correct to do this.
>>>
>>> Does anyone know what to do here?
>>>
>>> Thanks a lot in advance.
>>>
>>> *
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>> *
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>
>
> *
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