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.
FACTOR ANALYSIS
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
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
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
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
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
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
-- 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
> [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
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
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
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
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
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
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
[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
-- 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
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
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
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?
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" <[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?
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?
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?
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?
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?
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?
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?
"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?
[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?
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?
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?
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?
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
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
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/ =