Factor analysis

[Haider Al-Katem]

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'?

Thanks.

FACTOR ANALYSIS

[haytham siala]

Hi,

I have a question related to factor analysis.

If a questionnaire item was found to load significantly on more than one
factor and let us assume that each factor represents a potential measurement
scale for a particular construct, should I retain the same item for both
factors (scales) i.e should that same item be included in the two
measurement scales? Or should I take the highest loading of the item as the
decisive solution to which factor it should belong?

Cheers.

FACTOR ANALYSIS

[haytham siala]

When I perform a factor analysis on the items of a questionnaire should I
include items that make up the Dependent Variables (DVs) as well as the
Independent Variables (IVs) in the analysis or should I perform two separate
factor analysis, one on the items making up the Dependent Variables and
another on the items making up the Independent Variables.

FACTOR ANALYSIS

[haytham siala]

When I perform a factor analysis on the items of a questionnaire should I
include items that make up the Dependent Variables (DVs) as well as the
Independent Variables (IVs) in the analysis or should I perform two separate
factor analysis, one on the items making up the Dependent Variables and
another on the items making up the Independent Variables.

factor Analysis

[Huxley]

Hi,
I've got a question. Does anyone know how to set object in 2-factor
dimensional space i.e I have 2 factor score. Therefore I can put variables
in this space. But variables describe objects (i.e. these are 12 consumer
products) and I don't care variables in space but only these products.
I heard that factor score for a product is equal to product of the suitable
factor loadings and variables mean. i.e.
f(m,p)=a(1,m)u(1,p) +a(2,m)u(2,p)+ ...+a(j,m)u(j,p)
where: f(m,d) - factor score for m-factor,  p-th - consumer product , u(*) -
mean for variable j and product p.
Could you tell me is this true? How to proof this formally


Huxley




=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis

[Rich Ulrich]

On Sat, 18 Dec 1999 12:00:52 -, "Haider Al-Katem"
<[EMAIL PROTECTED]> wrote:

> 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.
 
 - Hey, two factors is pretty simple, if you start with a few dozen
items ...

> 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?

Well, if "not simple" is an interpretation, it seems premature or
impossible for us readers to comment, because there is no content
worth commenting on.  If "may be justifiable" is an interpretation, it
is wimpy enough that I wouldn't claim it is incorrect.

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

 And what is goal is "overcome" supposed to indicate?  If there are
two factors, you can provide the outcome of your survey as two
composite scores instead of just one.
-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html

Re: Factor analysis

[Chuck Cleland]

Haider Al-Katem wrote:
> 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.

Simple structure is present when each item loads high on one factor and
low on all of the others.  You have not said whether the two factors you
extracted can be named (if the first factor is ATTITUDE TOWARD PRODUCT
X, then what is the second factor?).  Confirmatory factor analysis (CFA)
is a special case of SEM (specifically the measurement model part of
SEM).  I would say that 50 cases is probably too low to warrant much
confidence in the results of an exploratory factor analysis or CFA.

Chuck 

--
Chuck Cleland
Institute for the Study of Child Development
UMDNJ-Robert Wood Johnson Medical School
97 Paterson Street
New Brunswick, NJ 08903
phone: (732) 235-7699
  fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
--

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.

Re: Factor analysis

[Joe Ward]

Haider --
 
You may want to consider another 
approach:
 
1.  Use "Policy Capturing", "Judgment 
Analysis (JAN)", "Policy Specifying" or any of your
favorite Multi-Attribute Decision Model approaches 
to obtain ONE function of
your THREE DEPENDENT VARIABLES.
 
IMHO,  Only human(s) should make 
judgments
about how to combine multiple dependent 
variables.
 
After that, you now have Y = function of (your 
THREE DEPENDENT VARIABLES)
 
2.  Then you use your favorite 
regression 
program to predict Y = function of (your 
PREDICTOR VARIABLES)
 
This approach is not involved with factor analysis 
interpretation.
 
However, if you want to do a factor analysis on 
the PREDICTORS, then you can  
USE THE FACTOR SCORES AS PREDICTORS. The 
disadvantage of using factor scores
is that you still have to use ALL OF THE PREDICTOR 
VARIABLES.  So if you 
would like to reduce the number of predictors, 
then you should NOT use
factor scores but use regression 
models.
 
-- Joe
 
* Joe 
Ward  
Health Careers High School ** 167 East Arrowhead 
Dr 
4646 Hamilton Wolfe    ** San 
Antonio, TX 
78228-2402    
San Antonio, TX 78229  ** Phone: 
210-433-6575   
Phone: 210-617-5400    ** Fax: 
210-433-2828 
Fax: 210-617-5423  ** 
[EMAIL PROTECTED]    
** http://www.ijoa.org/joeward/wardindex.html   
*
 
 
 
 
- Original Message - 
From: Haider Al-Katem <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Saturday, December 18, 1999 4:00 
AM
Subject: Factor analysis 
| 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'?| | Thanks.| | 
| 

Re: FACTOR ANALYSIS

[lthayer]

If these factors were length measured in feet and in yards, would it
make sense to have both in the same model. No

If these factors were measure of ability like IQ, IQ test 1 and IQ test
2, then the question depends on how the two test are related.  If they
are highly correlated, drop one. If they measure different things then
they should be included, if significant.  If they overlap, look at your
hypothesis and make a judgment based on the results.


In article <864hr0$805$[EMAIL PROTECTED]>,
  "haytham siala" <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I have a question related to factor analysis.
>
> If a questionnaire item was found to load significantly on more than
one
> factor and let us assume that each factor represents a potential
measurement
> scale for a particular construct, should I retain the same item for
both
> factors (scales) i.e should that same item be included in the two
> measurement scales? Or should I take the highest loading of the item
as the
> decisive solution to which factor it should belong?
>
> Cheers.
>
>


Sent via Deja.com http://www.deja.com/
Before you buy.

FACTOR ANALYSIS 2

[haytham siala]

When I perform a factor analysis on the items of a questionnaire should I
include items that make up the Dependent Variables (DVs) as well as the
Independent Variables (IVs) in the analysis or should I perform two separate
factor analysis, one on the items making up the Dependent Variables and
another on the items making up the Independent Variables.

Factor Analysis 3

[haytham siala]

Hi,

I am sorry that I am sending a lot of questions related to this subject and
here is another question:

If some dissimilar iets load on a common factor (the factor does not seem to
make sense since it consists of some related and some completely unrrelated
items), should I ignore that factor or should I delete the unrrelated items
from the factor analysis?

Thanks in advance.

Texts: Factor Analysis

[srmillis]

What are your favorite book(s) on factor analysis?

What do you think of R. Gorsuch's book?


Thanks,
Scott Millis


===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

Re: factor Analysis

[John Uebersax]

A program like SAS or SPSS will calculate factor scores for you.  A
factor score is an estimated location of an object (not a variable)
relative to a factor.  If your factors are orthogonal, then you can
plot each case using that case's score on Factor 1 and the score on
Factor 2 as the X- and Y- coordinates of in a 2-dimensional space.

I believe the formula for estimating factor scores of a common-factor
model is not trvial (unless all communalities are 1).  Therefore one
might as well let the software calculate factor scores.  The topic is
well explained in the SAS manual (PROC FACTOR)--perhaps also in the
SPSS manual.


John Uebersax, PhD (805) 384-7688 
Thousand Oaks, California  (805) 383-1726 (fax)
email: [EMAIL PROTECTED]

Agreement Stats:   http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
Existential Psych: http://members.aol.com/spiritualpsych
Diet & Fitness:http://members.aol.com/WeightControl101


"Huxley" <[EMAIL PROTECTED]> wrote in message news:...
> Hi,
> I've got a question. Does anyone know how to set object in 2-factor
> dimensional space ...
> I heard that factor score for a product is equal to product of the suitable
> factor loadings and variables mean. i.e.
> f(m,p)=a(1,m)u(1,p) +a(2,m)u(2,p)+ ...+a(j,m)u(j,p)
> where: f(m,d) - factor score for m-factor,  p-th - consumer product , u(*) -
> mean for variable j and product p.
> Could you tell me is this true? How to proof this formally


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Huxley]

Thank you for explanation. Bu my question was unclear therefore let me ask
again. I invented an exapmle.

I have 10 questions in a questionnaire. These questions are my 10 variables.
A consumers fill this questionnaire for each 15 products e.g cars. Because
10 variables (X1, X2, ...,X10) are correlated with each other I use factor
analysis and (for convinence I ordered it) I get
Factor1: X1,X2,X3,X4,X5,X6,X7
Factor2: X8,X9,X10

I can  e.g put X1 into 2-D space, because I know that
X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
It's simple. But I'm not interested in positioning X1. For me it's important
where there are products (cars) in 2-D space. Therefore my question is how
to do it. I heard (but I do not know) that using e.g variable X1,...X10
mean and factor loadings I can do it i.e. for car1: I multiple  factor
loadings and variables mean (suitable) and I get this position
Could you help me verify this?
I would be very appreciate

Regards
Huxley

Uzytkownik "John Uebersax" <[EMAIL PROTECTED]> napisal w wiadomosci
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> A program like SAS or SPSS will calculate factor scores for you.  A
> factor score is an estimated location of an object (not a variable)
> relative to a factor.  If your factors are orthogonal, then you can
> plot each case using that case's score on Factor 1 and the score on
> Factor 2 as the X- and Y- coordinates of in a 2-dimensional space.
>
> I believe the formula for estimating factor scores of a common-factor
> model is not trvial (unless all communalities are 1).  Therefore one
> might as well let the software calculate factor scores.  The topic is
> well explained in the SAS manual (PROC FACTOR)--perhaps also in the
> SPSS manual.
>
> --
--
> John Uebersax, PhD (805) 384-7688
> Thousand Oaks, California  (805) 383-1726 (fax)
> email: [EMAIL PROTECTED]
>
> Agreement Stats:
http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
> Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
> Existential Psych: http://members.aol.com/spiritualpsych
> Diet & Fitness:http://members.aol.com/WeightControl101
> --
--
>
> "Huxley" <[EMAIL PROTECTED]> wrote in message
news:<a2u3sa$q3e$[EMAIL PROTECTED]>...
> > Hi,
> > I've got a question. Does anyone know how to set object in 2-factor
> > dimensional space ...
> > I heard that factor score for a product is equal to product of the
suitable
> > factor loadings and variables mean. i.e.
> > f(m,p)=a(1,m)u(1,p) +a(2,m)u(2,p)+ ...+a(j,m)u(j,p)
> > where: f(m,d) - factor score for m-factor,  p-th - consumer product ,
u(*) -
> > mean for variable j and product p.
> > Could you tell me is this true? How to proof this formally




=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Gottfried Helms]

It's not so simple. You have to do matrix-inversion for
that. 

If your statistical program is able to spit out "factor scores",
you just take these as your coordinates. For each of your objects
you get values in each factor, which you can use as coordinates 
in the factorspace. 

Regards -

Gottfried.


Huxley schrieb:
> 
> Thank you for explanation. Bu my question was unclear therefore let me ask
> again. I invented an exapmle.
> 
> I have 10 questions in a questionnaire. These questions are my 10 variables.
> A consumers fill this questionnaire for each 15 products e.g cars. Because
> 10 variables (X1, X2, ...,X10) are correlated with each other I use factor
> analysis and (for convinence I ordered it) I get
> Factor1: X1,X2,X3,X4,X5,X6,X7
> Factor2: X8,X9,X10
> 
> I can  e.g put X1 into 2-D space, because I know that
> X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
> It's simple. But I'm not interested in positioning X1. For me it's important
> where there are products (cars) in 2-D space. Therefore my question is how
> to do it. I heard (but I do not know) that using e.g variable X1,...X10
> mean and factor loadings I can do it i.e. for car1: I multiple  factor
> loadings and variables mean (suitable) and I get this position
> Could you help me verify this?
> I would be very appreciate
> 
> Regards
> Huxley
>


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Huxley]

Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> It's not so simple. You have to do matrix-inversion for
> that.
>
Not simple? I heard that taking suitable factor loadings and every variable
mean I can obtain this space. e.g. (I do not know is it true)
Let mean for car1 and questions 10 (variables):
mean X1=1
mean X2=2
..
mean X10=10
I have 2 factor score.
factor loadins (aij) I have, therefore for first factor score, co-odrinate
for car1 is
F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
is it true?

Huxley




=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Pedro . Valero-Mora]

What you need is a program that makes biplots for principal 
components. ViSta, a freeware program, will do it for you. In facti, 
it includes examples of data about cars and the goal of the analysis 
is to visualize them in the space of the variables.

Pedro 

> It's not so simple. You have to do matrix-inversion for
> that. 
> 
> If your statistical program is able to spit out "factor scores",
> you just take these as your coordinates. For each of your objects
> you get values in each factor, which you can use as coordinates 
> in the factorspace. 
> 
> Regards -
> 
> Gottfried.
> 
> 
> Huxley schrieb:
> > 
> > Thank you for explanation. Bu my question was unclear therefore 
let me ask
> > again. I invented an exapmle.
> > 
> > I have 10 questions in a questionnaire. These questions are my 10 
variables.
> > A consumers fill this questionnaire for each 15 products e.g 
cars. Because
> > 10 variables (X1, X2, ...,X10) are correlated with each other I 
use factor
> > analysis and (for convinence I ordered it) I get
> > Factor1: X1,X2,X3,X4,X5,X6,X7
> > Factor2: X8,X9,X10
> > 
> > I can  e.g put X1 into 2-D space, because I know that
> > X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
> > It's simple. But I'm not interested in positioning X1. For me 
it's important
> > where there are products (cars) in 2-D space. Therefore my 
question is how
> > to do it. I heard (but I do not know) that using e.g variable 
X1,...X10
> > mean and factor loadings I can do it i.e. for car1: I multiple  
factor
> > loadings and variables mean (suitable) and I get this position
> > Could you help me verify this?
> > I would be very appreciate
> > 
> > Regards
> > Huxley
> >
> 
> 
> =
> Instructions for joining and leaving this list, remarks about the
> problem of INAPPROPRIATE MESSAGES, and archives are available at
>   http://jse.stat.ncsu.edu/
> =
> 





=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Gottfried Helms]

Huxley schrieb:
> 
> Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > It's not so simple. You have to do matrix-inversion for
> > that.
> >
> Not simple? I heard that taking suitable factor loadings and every variable
> mean I can obtain this space. e.g. (I do not know is it true)
> Let mean for car1 and questions 10 (variables):
> mean X1=1
> mean X2=2
> ..
> mean X10=10
> I have 2 factor score.
> factor loadins (aij) I have, therefore for first factor score, co-odrinate
> for car1 is
> F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
> is it true?
> 
> Huxley

Loadings of factor f1,f2 for items x1,x2,x3,x4... 
 f1f2
 x1  0.4   0.6
 x2  0.3   0.9
 x3  0.2  -0.1
 x4 -0.8  -0.4
 ...
Call this loadingsmatrix A, your correlation-matrix R 
That means, that A*A' = R
Call your empical datamatrix   (x1,x2,x3,...) X 
Call the unknow factorscores  SC
Then it is assumed that

A*SC = X 

Then you must find inv(A) to be able to find SC:

inv(A)*A*SC = inv(A) *X
SC = inv(A)*X

If the shape of A is not square and/or the rank is lower
then its dimension, then you have to find a workaround to
compute the general_inverse of A. 

I don't find it so simple ;-) 

Gottfried.


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor Analysis

[Rich Ulrich]

On Tue, 29 Jan 2002 10:52:30 +0100, "Huxley" <[EMAIL PROTECTED]> wrote:

> 
> Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > It's not so simple. You have to do matrix-inversion for
> > that.
> >
> Not simple? I heard that taking suitable factor loadings and every variable
> mean I can obtain this space. e.g. (I do not know is it true)
> Let mean for car1 and questions 10 (variables):
> mean X1=1
> mean X2=2
> ..
> mean X10=10
> I have 2 factor score.
> factor loadins (aij) I have, therefore for first factor score, co-odrinate
> for car1 is
> F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
> is it true?

No, that is not true.  
Please believe them.

Factor loadings are *correlations*  and serve as descriptors.  
They were neither scaled nor computed as regression coefficients -
which is what you are trying to use them as.


Now, in clinical research, we don't usually bother to create the
actual, real, true factor, for our practical purposes.   For practical
purposes, it is important to have some face-validity for what 
the factor means.  And it is handy for replication, as well as 
for understanding, if we construct a factor as the summed score
(or average score) of a set of the items.

So I look at the high loadings.  For a good set of items, it
can be realistic and appropriate to 'assign'  each item to the
factor where its loading is greatest, thus using each item just
once in the overall set of several derived factors.  (For a set 
of items where many items were new and untested, it can 
be appropriate to discard some of items -- where the loadings
were split, or were always small.)  Each factor is scored as 
the average score for of a subset of items.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: factor analysis

[Herman Rubin]

In article <[EMAIL PROTECTED]>,
Roland Pesch  <[EMAIL PROTECTED]> wrote:
>HI,

>I'm trying to perform factor analysis on mosses from 1028 moss
>monitoring sites, each of which was chemically anaylsed on 20 heavy
>metal elements. All of these samples do not follow a normal distribution
>pattern, they are all skewed positively.

>It is my understanding that, to calculate the correlation coefficient
>matrix, one should be very careful when the data samples are other than
>normally distributed. So I transformed each sample lognormally but most
>of the results still do not follow a normal distribution pattern (I
>checked this with a Kolmogorow-Smirnow goodness-of-fit-test).

Linear methods, such as correlations or factor analysis, 
depend very heavily on LINEARITY assumptions.  The have
reasonable problems under non-normality, assuming the
moments exist, although the usual tests will not have the
same sampling distribution.

Attempting to make things normal is likely to destroy the
linearity of the relations.  Nothing in nature is normal,
although some MAY be close.  The tests, etc., for regression
analysis are not precise without normality of the "errors",
but the reasonableness of the procedures holds if the "true"
relation is linear.
-- 
This address is for information only.  I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054   FAX: (765)494-0558


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

factor analysis instructional materials

[Marley Watkins]

Hi,

I'm going to be doing a mini-course on exploratory factor analysis and
don't wnat to reinvent the wheel. Does anyone have PowerPoint or other
graphics that I could 'borrow' to show geometric nature of efa, rotations,
etc.?

thanks,
Marley

Re: Texts: Factor Analysis

[James E. Strouse]

Check out 'Multivariate Data Analysis' (4th Ed.)
Hair, Anderson, Tatham & Black
Great book.

[EMAIL PROTECTED]


[EMAIL PROTECTED] wrote:

> What are your favorite book(s) on factor analysis?
>
> What do you think of R. Gorsuch's book?
>
> Thanks,
> Scott Millis
>
> ===
> This list is open to everyone.  Occasionally, less thoughtful
> people send inappropriate messages.  Please DO NOT COMPLAIN TO
> THE POSTMASTER about these messages because the postmaster has no
> way of controlling them, and excessive complaints will result in
> termination of the list.
>
> For information about this list, including information about the
> problem of inappropriate messages and information about how to
> unsubscribe, please see the web page at
> http://jse.stat.ncsu.edu/
> ===



===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

Re: Texts: Factor Analysis

[Gottfried Helms]

> [EMAIL PROTECTED] wrote:
> 
> > What are your favorite book(s) on factor analysis?
> >
> > What do you think of R. Gorsuch's book?
> >

My favorite is Stan Mulaik "The foundations of factor analysis".
It is comprehensive and still straightforward from the introduction
to all covered themes. I have tried different others, but none
was like that. Not being educated mathematician I felt I got
most that I needed with a good insight of the principles.

One similar is from Dirk Revenstorf, but I doubt it is available
in english.

Gottfried Helms.


-- 
   -
Gottfried Helms Soz.Päd./Soz.Arb. 
FB04 // FG Prevention & Rehabilitation at University
D-34109 Kassel  Moenchebergstr. 19 B

email: mailto:[EMAIL PROTECTED]
www:   http://www.uni-kassel.de/~helms



===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

Re: Texts: Factor Analysis

[dennis roberts]

go to http://www.sagepub.com/

search on ... factor analysis ... some nice short books here

At 03:06 PM 4/5/00 +0200, Gottfried Helms wrote:
> > [EMAIL PROTECTED] wrote:
> >
> > > What are your favorite book(s) on factor analysis?
> > >
> > > What do you think of R. Gorsuch's book?
> > >
>
>My favorite is Stan Mulaik "The foundations of factor analysis".
>It is comprehensive and still straightforward from the introduction
>to all covered themes. I have tried different others, but none
>was like that. Not being educated mathematician I felt I got
>most that I needed with a good insight of the principles.
>
>One similar is from Dirk Revenstorf, but I doubt it is available
>in english.
>
>Gottfried Helms.
>
>
>--
>-
>Gottfried Helms Soz.Päd./Soz.Arb.
>FB04 // FG Prevention & Rehabilitation at University
>D-34109 Kassel  Moenchebergstr. 19 B
>
>email: mailto:[EMAIL PROTECTED]
>www:   http://www.uni-kassel.de/~helms
>
>
>
>===
>This list is open to everyone.  Occasionally, less thoughtful
>people send inappropriate messages.  Please DO NOT COMPLAIN TO
>THE POSTMASTER about these messages because the postmaster has no
>way of controlling them, and excessive complaints will result in
>termination of the list.
>
>For information about this list, including information about the
>problem of inappropriate messages and information about how to
>unsubscribe, please see the web page at
>http://jse.stat.ncsu.edu/
>===




===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

Re: Texts: Factor Analysis

[Gene Gallagher]

For the natural sciences, try Reyment & Joreskog Applied Factor analysis
for the natural sciences, Cambridge Univ Press.

In article <[EMAIL PROTECTED]>,
  [EMAIL PROTECTED] wrote:
> > [EMAIL PROTECTED] wrote:
> >
> > > What are your favorite book(s) on factor analysis?
> > >
> > > What do you think of R. Gorsuch's book?
> > >
>
> My favorite is Stan Mulaik "The foundations of factor analysis".
> It is comprehensive and still straightforward from the introduction
> to all covered themes. I have tried different others, but none
> was like that. Not being educated mathematician I felt I got
> most that I needed with a good insight of the principles.
>
> One similar is from Dirk Revenstorf, but I doubt it is available
> in english.
>
> Gottfried Helms.
>
> --
>-
> Gottfried Helms Soz.Päd./Soz.Arb.
> FB04 // FG Prevention & Rehabilitation at University
> D-34109 Kassel  Moenchebergstr. 19 B
> 
> email: mailto:[EMAIL PROTECTED]
> www:   http://www.uni-kassel.de/~helms
> 
>

--
Eugene D. Gallagher
ECOS, UMASS/Boston


Sent via Deja.com http://www.deja.com/
Before you buy.


===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

maximum likelihood factor analysis

[kjessup]

A self-report scale was constructed to measure work ethic and included three
conceptually derived components of work ethic.  Maximum likelihood factor
analysis was then applied with the request of 3 factors to determine if the
conceptually derived components actually represent empirical factors.  Is
this an appropriate/acceptable manner of evaluating the factor structure of
the scale?  Also, my version of SPSS (6.0) reports percent of variance
accounted for by each factor, but doesn't indicate if this is common variance
or total variance.  Does someone know which variance is reported.  Is maximum
likelihood factor analysis used with either principle components or principal
factors analysis?  I would appreciate any explanation someone might offer.  I
have had difficulty finding any explanation on the web concerning these
issues. Kary


Sent via Deja.com http://www.deja.com/
Before you buy.


===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

Re: factor analysis instructional materials

[Alex Yu]

Hi, I have several things. The following is a website explaining concepts 
of factor, vector, eigenavector, eigenvalue, variable space, subject 
space...etc:

http://seamonkey.ed.asu.edu/~alex/computer/sas/biplot.html

I also have a multimedia program:

http://seamonkey.ed.asu.edu/~alex/multimedia/factor.zip

The program is a self-contained movie made by Macromedia Director. The 
file size is 8 meg. You need Winzip to decompress it. 

Hope it helps.


Chong-ho (Alex) Yu, Ph.D., CNE, MCSE
Instruction and Research Support
Information Technology
Arizona State University
Tempe AZ 85287-0101
Voice: (602)965-7402
Fax: (602)965-6317
Email: [EMAIL PROTECTED]
URL:http://seamonkey.ed.asu.edu/~alex/
   
  

Correction: Factor analysis instructional materials

[Alex Yu]

Two days ago I posted a URL for downloading a tutorial of factor 
analysis. However, when I uploaded the program, I forgot to include the 
associated QuickTime movies. The corrected zip file has been re-uploaded to:

http://seamonkey.ed.asu.edu/~alex/alex/multimedia/factor.zip

I made the same correction to another tutorial of collinearity:

http://seamonkey.ed.asu.edu/~alex/alex/multimedia/collinear.zip

Sorry for wasting your bandwidth and downloading time.


Chong-ho (Alex) Yu, Ph.D., CNE, MCSE
Instruction and Research Support
Information Technology
Arizona State University
Tempe AZ 85287-0101
Voice: (602)965-7402
Fax: (602)965-6317
Email: [EMAIL PROTECTED]
URL:http://seamonkey.ed.asu.edu/~alex/
   
  

SEM and Confirmatory factor analysis

[Haider Al-Katem]

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

Re: maximum likelihood factor analysis

[Chuck Cleland]

[EMAIL PROTECTED] wrote:
> A self-report scale was constructed to measure work ethic and included three
> conceptually derived components of work ethic.  Maximum likelihood factor
> analysis was then applied with the request of 3 factors to determine if the
> conceptually derived components actually represent empirical factors.  Is
> this an appropriate/acceptable manner of evaluating the factor structure of
> the scale?  Also, my version of SPSS (6.0) reports percent of variance
> accounted for by each factor, but doesn't indicate if this is common variance
> or total variance.  Does someone know which variance is reported.  Is maximum
> likelihood factor analysis used with either principle components or principal
> factors analysis?  I would appreciate any explanation someone might offer.  I
> have had difficulty finding any explanation on the web concerning these
> issues. Kary

Kary:
  You might want to check out chapter 13 in Tabachnick, B.G., &
Fidell, L.S.(1996). Using multivariate statistics (3rd Edition). New
York: Harper Collins.  This chapter has a nice discussion of the
differences and similarities between principal components and common
factor analysis and it has some stuff on different estimation
procedures.  It sounds like you really want to do a confirmatory
factor analysis in which you could specify which items load on which
of the three factors.  I don't think SPSS 6.0 will do CFA, but you may
want to look into it anyway.

HTH,

Chuck
 
--
Chuck Cleland
Institute for the Study of Child Development
UMDNJ-Robert Wood Johnson Medical School
97 Paterson Street
New Brunswick, NJ 08903
phone: (732) 235-7699
  fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
--


===
This list is open to everyone.  Occasionally, less thoughtful
people send inappropriate messages.  Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.

For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===

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.

 

help on factor analysis/non-normality

[Mobile Survey]

What do i do if I need to run a factor analysis and have non-normal
distribution for some of the items (indicators)? Does Principal
component analysis require the normality assumption. Can I use GLS to
extract the factors and get over the problem of non-normality. Please
do give references if you are replying.
Thanks.


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: help on factor analysis/non-normality

[Rich Ulrich]

On 1 Mar 2002 04:51:42 -0800, [EMAIL PROTECTED] (Mobile Survey)
wrote:

> What do i do if I need to run a factor analysis and have non-normal
> distribution for some of the items (indicators)? Does Principal
> component analysis require the normality assumption. 

There is no problem of non-normality, except that it *implies*
that decomposition  *might*  not give simple structures.
Complications are more likely when covariances are high.

What did you read, that you are trying to respond to?

>  Can I use GLS to
> extract the factors and get over the problem of non-normality. Please
> do give references if you are replying.
> Thanks.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html


=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Re: help on factor analysis/non-normality

[Robert Ehrlich]

to amplifiy a bit, the interpretability of regression tends to go down as
the assumptions of normality and homogeneous variance are markedly
different from reality.  You can still go through the calcualtions but the
interpretation of results gets tricky.  Factor analysis is a sort of
regression analysis and so suffers in the same way from break downs of
assumptions.

Rich Ulrich wrote:

> On 1 Mar 2002 04:51:42 -0800, [EMAIL PROTECTED] (Mobile Survey)
> wrote:
>
> > What do i do if I need to run a factor analysis and have non-normal
> > distribution for some of the items (indicators)? Does Principal
> > component analysis require the normality assumption.
>
> There is no problem of non-normality, except that it *implies*
> that decomposition  *might*  not give simple structures.
> Complications are more likely when covariances are high.
>
> What did you read, that you are trying to respond to?
>
> >  Can I use GLS to
> > extract the factors and get over the problem of non-normality. Please
> > do give references if you are replying.
> > Thanks.
>
> --
> Rich Ulrich, [EMAIL PROTECTED]
> http://www.pitt.edu/~wpilib/index.html



=
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
  http://jse.stat.ncsu.edu/
=

Factor analysis - which package is best for Windows?

[Aron Landy]

Any ideas, anyone? I am thinking of using IMSL (which comes free with Compaq
Visual Fortran). Can I do better?

Aron Landy





=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Magenta]

"Aron Landy" <[EMAIL PROTECTED]> wrote in message
3b8b6418$0$8507$[EMAIL PROTECTED]">news:3b8b6418$0$8507$[EMAIL PROTECTED]...
> Any ideas, anyone? I am thinking of using IMSL (which comes free with
Compaq
> Visual Fortran). Can I do better?

Any of the "standard" statistical packages should be fine (e.g. SPSS, SAS,
S-Plus, Statistica, Minitab).  All have Windows versions, and all have
different types of site licenses.  If you are a student, you may be able to
get a student discount on the statistical software through your educational
institute.  You may also be able to locate a demonstration version, although
you would then have problems once the evaluation period ended (e.g.
inability to open the package-specific files).  I just recommend going with
a package that statisticians use, then you know that the results produced
are accurate.  Your choice of package will possibly be constrained by the
ease of use of the package (and even when you can programme, a menu system
can still be much more rapid).

I've not heard of ISML, but then I'm not a Fortran programmer (SPSS syntax,
SAS, and VBA are my limits - but about to learn Sax!!!).

Hope this helps, and good luck with your analysis!  :-)

cheers
Michelle




=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[John Uebersax]

It depends on what you want to do.  Sure, for learning about factor
analysis, it's fun to write your own fortran programs.  It's also a
good way to learn to use IMSL routines.  If you're heading towards
work in methodology and software development, it might be instructive
to write such a program.

But for applied factor analysis--why reinvent the wheel?  Any program
you write using fortran and IMSL routines probably won't be as good as
what one finds with SAS or SPSS.  I have written my own factor
analysis routines in fortran.  But in most research situations, I'm
far more likely to use SAS PROC FACTOR than my own program.


John Uebersax, PhD (805) 384-7688 
Thousand Oaks, California  (805) 383-1726 (fax)
email: [EMAIL PROTECTED]

Agreement Stats:   http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
Existential Psych: http://members.aol.com/spiritualpsych



"Aron Landy" <[EMAIL PROTECTED]> wrote in message 
news:<3b8b6418$0$8507$[EMAIL PROTECTED]>...
> Any ideas, anyone? I am thinking of using IMSL (which comes free with Compaq
> Visual Fortran). Can I do better?
> 
> Aron Landy


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Aron Landy]

Problem is, SAS costs about $20,000 whereas CVF & IMSL come bundled for
$800

Aron

"John Uebersax" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> It depends on what you want to do.  Sure, for learning about factor
> analysis, it's fun to write your own fortran programs.  It's also a
> good way to learn to use IMSL routines.  If you're heading towards
> work in methodology and software development, it might be instructive
> to write such a program.
>
> But for applied factor analysis--why reinvent the wheel?  Any program
> you write using fortran and IMSL routines probably won't be as good as
> what one finds with SAS or SPSS.  I have written my own factor
> analysis routines in fortran.  But in most research situations, I'm
> far more likely to use SAS PROC FACTOR than my own program.
>
> --
--
> John Uebersax, PhD (805) 384-7688
> Thousand Oaks, California  (805) 383-1726 (fax)
> email: [EMAIL PROTECTED]
>
> Agreement Stats:
http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
> Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
> Existential Psych: http://members.aol.com/spiritualpsych
> --
--
>
>
> "Aron Landy" <[EMAIL PROTECTED]> wrote in message
news:<3b8b6418$0$8507$[EMAIL PROTECTED]>...
> > Any ideas, anyone? I am thinking of using IMSL (which comes free with
Compaq
> > Visual Fortran). Can I do better?
> >
> > Aron Landy




=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Richard Wright]

KyPlot runs under Windows, is freeware and gives you several factor
analysis algorithms to choose from.

http://www.rocketdownload.com/Details/Math/kyplot.htm


On Wed, 29 Aug 2001 23:59:44 +0100, "Aron Landy" <[EMAIL PROTECTED]>
wrote:

>Problem is, SAS costs about $20,000 whereas CVF & IMSL come bundled for
>$800
>
>Aron



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Aron Landy]

I have tried it and it is amazing. A bargain ;)


"Richard Wright" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> KyPlot runs under Windows, is freeware and gives you several factor
> analysis algorithms to choose from.
>
> http://www.rocketdownload.com/Details/Math/kyplot.htm
>
>





=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

RE: Factor analysis - which package is best for Windows?

[Magill, Brett]

Also check out R, a GNU implementation of the S language, most prominently
known through its use in S-Plus.  R is a fully featured statisitical
programming environment.  In its MVA (Multivariate) package, it includes
routines for factor analysis using maximum liklihood estimation with varimax
and promax rotations.

R is open-source, which means that it is frequently updated and, most
importantly, it can be downloaded free of charge.  The only downside (to
some) is that at this stage of its development R is completely
command-prompt driven.  However, I find the R language intuitive and easy to
learn.

http://www.r-project.org


-Original Message-
From: Aron Landy [mailto:[EMAIL PROTECTED]]
Sent: Thursday, August 30, 2001 6:33 AM
To: [EMAIL PROTECTED]
Subject: Re: Factor analysis - which package is best for Windows?


I have tried it and it is amazing. A bargain ;)


"Richard Wright" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> KyPlot runs under Windows, is freeware and gives you several factor
> analysis algorithms to choose from.
>
> http://www.rocketdownload.com/Details/Math/kyplot.htm
>
>





=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[John Uebersax]

Thanks for the tip on KyPlot.  It does seem very nice.  

Two questions:

1.  As best I can tell, the Factor Analysis routines work off
a correlation or covariance matrix.  At least from a perusal
of the Help index, I can't see how to run Factor Analysis from
raw data, or to calculate a correlation/covariance matrix from 
raw data (short of applying matrix manipulations).  Is there
a way to produce a corr/cov matrix within KyPlot?

2.  Does anyone know the current homepage for KyPlot?

Thanks

John Uebersax, PhD (805) 384-7688 
Thousand Oaks, California  (805) 383-1726 (fax)
email: [EMAIL PROTECTED]

Agreement Stats:   http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
Existential Psych: http://members.aol.com/spiritualpsych


 [EMAIL PROTECTED] (Richard Wright) wrote in message 
news:<[EMAIL PROTECTED]>...
> KyPlot runs under Windows, is freeware and gives you several factor
> analysis algorithms to choose from.
> 
> http://www.rocketdownload.com/Details/Math/kyplot.htm


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Jerry Harder]

"Aron Landy" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> Problem is, SAS costs about $20,000 whereas CVF & IMSL come bundled for
> $800
>
> Aron
>
> "John Uebersax" <[EMAIL PROTECTED]> wrote in message
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
[snipped]
> --
> >
> >
> > "Aron Landy" <[EMAIL PROTECTED]> wrote in message
> news:<3b8b6418$0$8507$[EMAIL PROTECTED]>...
> > > Any ideas, anyone? I am thinking of using IMSL (which comes free with
> Compaq
> > > Visual Fortran). Can I do better?
> > >
> > > Aron Landy
>
>
See R which is free and includes all the matrix manipulation functions that
you will probably require. http://lib.stat.cmu.edu/R/CRAN/

--
Good luck,

Jerry Harder
remove spamnein from address to reply



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[PeterOut]

[EMAIL PROTECTED] (Magill, Brett) wrote in message 
news:<[EMAIL PROTECTED]>...
> Also check out R, a GNU implementation of the S language, most prominently
> known through its use in S-Plus.  R is a fully featured statisitical
> programming environment.  In its MVA (Multivariate) package, it includes
> routines for factor analysis using maximum liklihood estimation with varimax
> and promax rotations.
> 

I have installed R1.3.0 on  my Windows system and have noted that MVA
is an add-on.  The FAQ tells how to obtain these add-ons but only for
UNIX.  Is this add-on actually available for Windows?  If so, how do I
obtain it?

Thanks,
Peter


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

RE: Factor analysis - which package is best for Windows?

[Magill, Brett]

MVA comes with R base.  However, it is a seperate library.  Libraries that
are not sent with base are available in Windows binaries on CRAN, but you do
not have to worry about that for MVA.

Type:

library()

and you will get a list of the available packages.  To make MVA available
(i.e. load it), type:

library(mva)

then you can ask for, for example:

help (factanal)



-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]
Sent: Wednesday, September 05, 2001 5:42 PM
To: [EMAIL PROTECTED]
Subject: Re: Factor analysis - which package is best for Windows?


[EMAIL PROTECTED] (Magill, Brett) wrote in message
news:<[EMAIL PROTECTED]>...
> Also check out R, a GNU implementation of the S language, most prominently
> known through its use in S-Plus.  R is a fully featured statisitical
> programming environment.  In its MVA (Multivariate) package, it includes
> routines for factor analysis using maximum liklihood estimation with
varimax
> and promax rotations.
> 

I have installed R1.3.0 on  my Windows system and have noted that MVA
is an add-on.  The FAQ tells how to obtain these add-ons but only for
UNIX.  Is this add-on actually available for Windows?  If so, how do I
obtain it?

Thanks,
Peter


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Richard Wright]

I can't say whether it any good, let alone the best. But I have just
seen the following on an archaeological post.

"UNESCO has released WinIDAMS 1.0 for 32-bit Windows operating system.
WinIDAMS is a freeware software package for numerical information
processing and statistical analysis. It provides a complete set of
data manipulation and validation facilities and a wide range of
classical and advanced statistical techniques, including interactive
construction of multidimensional tables, graphical exploration of data
and time series analysis.

You can find more information at the following url:

http://www.unesco.org/idams "

I have checked the URL. It does offer factor analysis.

Richard Wright


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[jcd]

UNESCO IDAMS team would be very pleased to collect your comments about WinIDAMS
Factor Analysis procedure and any matters regarding the software.

[EMAIL PROTECTED] (Richard Wright) wrote in message 
news:<[EMAIL PROTECTED]>...
> I can't say whether it any good, let alone the best. But I have just
> seen the following on an archaeological post.
> 
> "UNESCO has released WinIDAMS 1.0 for 32-bit Windows operating system.
> WinIDAMS is a freeware software package for numerical information
> processing and statistical analysis. It provides a complete set of
> data manipulation and validation facilities and a wide range of
> classical and advanced statistical techniques, including interactive
> construction of multidimensional tables, graphical exploration of data
> and time series analysis.
> 
> You can find more information at the following url:
> 
> http://www.unesco.org/idams "
> 
> I have checked the URL. It does offer factor analysis.
> 
> Richard Wright


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: Factor analysis - which package is best for Windows?

[Robert Ehrlich]

you may wish to consider NCSS (they have a web site)  provides essentially the same 
output as SAS but is run from  templates not SAS
language.  Less expensive, good documentation, excellant support. However does not 
provide an audit trail--a necessary feature for
some governmental / legal groups.

PeterOut wrote:

> [EMAIL PROTECTED] (Magill, Brett) wrote in message 
>news:<[EMAIL PROTECTED]>...
> > Also check out R, a GNU implementation of the S language, most prominently
> > known through its use in S-Plus.  R is a fully featured statisitical
> > programming environment.  In its MVA (Multivariate) package, it includes
> > routines for factor analysis using maximum liklihood estimation with varimax
> > and promax rotations.
> >
>
> I have installed R1.3.0 on  my Windows system and have noted that MVA
> is an add-on.  The FAQ tells how to obtain these add-ons but only for
> UNIX.  Is this add-on actually available for Windows?  If so, how do I
> obtain it?
>
> Thanks,
> Peter



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

SAS is the best package for Factor analysis in Windows

[Andreas Karlsson]

In my opinion SAS is the best computer package for Factor analysis in
Windows. And for most other analyses too...


=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Re: SAS is the best package for Factor analysis in Windows

[Andreas Karlsson]

On Thu, 06 Sep 2001 13:41:32 GMT, [EMAIL PROTECTED] (Andreas
Karlsson) wrote:

>In my opinion SAS is the best computer package for Factor analysis in
>Windows. And for most other analyses too...


testing...



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=