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://jsestatncsuedu/ =
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/ =
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/ =
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
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/ =
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 - 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/ =
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/ =
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/ =
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?
[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?
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?
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?
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?
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/ =
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: Normality in Factor Analysis
Robert Ehrlich [EMAIL PROTECTED] wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... Calculation of eigenvalues and eigenvalues requires no assumption. However evaluation of the results IMHO implicitly assumes at least a unimodal distribution and reasonably homogeneous variance for the same reasons as ANOVA or regression. So think of th consequencesof calculating means and variances of a strongly bimodal distribution where no sample ocurrs near the mean and all samples are tens of standard devatiations from the mean. The largest number of standard deviations all data can be from the mean is 1. To get some data further away than that, some of it has to be less than 1 s.d. from the mean. Glen = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Normality in Factor Analysis
Calculation of eigenvalues and eigenvalues requires no assumption. However evaluation of the results IMHO implicitly assumes at least a unimodal distribution and reasonably homogeneous variance for the same reasons as ANOVA or regression. So think of th consequencesof calculating means and variances of a strongly bimodal distribution where no sample ocurrs near the mean and all samples are tens of standard devatiations from the mean. Hi, I have a question regarding factor analysis: Is normality an important precondition for using factor analysis? If no, are there any books that justify this. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Maximum likelihood Was: Re: Factor Analysis
In article [EMAIL PROTECTED], Ken Reed [EMAIL PROTECTED] wrote: It's not really possible to explain this in lay person's terms. The difference between principal factor analysis and common factor analysis is roughly that PCA uses raw scores, whereas factor analysis uses scores predicted from the other variables and does not include the residuals. That's as close to lay terms as I can get. I have never heard a simple explanation of maximum likelihood estimation, but -- MLE compares the observed covariance matrix with a covariance matrix predicted by probability theory and uses that information to estimate factor loadings etc that would 'fit' a normal (multivariate) distribution. MLE factor analysis is commonly used in structural equation modelling, hence Tracey Continelli's conflation of it with SEM. This is not correct though. I'd love to hear simple explanation of MLE! MLE is triviality itself, if you do not make any attempt to state HOW it is to be carried out. For each possible value X of the observation, and each state of nature \theta, there is a probability (or density with respect to some base measure) P(X | \theta). There is no assumption that X is a single real number; it can be anything; the same holds about \theta. What MLE does is to choose the \theta which makes P(X | \theta) as large as possible. That is all there is to it. -- 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Normality in Factor Analysis
In article 9gg7ht$qa3$[EMAIL PROTECTED], haytham siala [EMAIL PROTECTED] wrote: Hi, I have a question regarding factor analysis: Is normality an important precondition for using factor analysis? If no, are there any books that justify this. Factor analysis is quite robust against non-normality. The essential factor structure is little affected by it at all, although the representation may get somewhat sensitive if data-dependent normalizations are used, such as using correlations rather than covariances, or forcing normalization on the covariance matrix of the factors. Some of this is in my paper with Anderson in the Proceedings of the Third Berkeley Symposium. The result on the asymptotic distribution, not at all difficult to derive, is in one of my abstracts in _Annals of Mathematical Statistics_, 1955. It is basically this: Suppose the factor model is x = \Lambda f + s, f the common factors and s the specific factors. Further suppose that f and s, and also the elements of s, are uncorrelated, and there is adequate normalization and smooth identification of the model by the elements of \Lambda alone. Now estimate \Lambda, M, the covariance matrix of f, and S, the diagonal covariance matrix of s. Assuming the usual assumptions for asymptotic normality of the sample covariances of the elements of f with s, and of the pairs of different elements of s, the asymptotic distribution of the estimates of \Lambda and the SAMPLE values of M and S from their actual values will have the expected asymptotic joint normal distribution. This makes no assumption about the distribution of M and S about their expected values, which is the main place were there is an effect of normality. -- 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Factor Analysis
It's not really possible to explain this in lay person's terms. The difference between principal factor analysis and common factor analysis is roughly that PCA uses raw scores, whereas factor analysis uses scores predicted from the other variables and does not include the residuals. That's as close to lay terms as I can get. I have never heard a simple explanation of maximum likelihood estimation, but -- MLE compares the observed covariance matrix with a covariance matrix predicted by probability theory and uses that information to estimate factor loadings etc that would 'fit' a normal (multivariate) distribution. MLE factor analysis is commonly used in structural equation modelling, hence Tracey Continelli's conflation of it with SEM. This is not correct though. I'd love to hear simple explanation of MLE! From: [EMAIL PROTECTED] (Tracey Continelli) Organization: http://groups.google.com/ Newsgroups: sci.stat.consult,sci.stat.edu,sci.stat.math Date: 15 Jun 2001 20:26:48 -0700 Subject: Re: Factor Analysis Hi there, would someone please explain in lay person's terms the difference betwn. principal components, commom factors, and maximum likelihood estimation procedures for factor analyses? Should I expect my factors obtained through maximum likelihood estimation tobe highly correlated? Why? When should I use a Maximum likelihood estimation procedure, and when should I not use it? Thanks. Rita [EMAIL PROTECTED] Unlike the other methods, maximum likelihood allows you to estimate the entire structural model *simultaneously* [i.e., the effects of every independent variable upon every dependent variable in your model]. Most other methods only permit you to estimate the model in pieces, i.e., as a series of regressions whereby you regress every dependent variable upon every independent variable that has an arrow directly pointing to it. Moreover, maximum likelihood actually provides a statistical test of significance, unlike many other methods which only provide generally accepted cut-off points but not an actual test of statistical significance. There are very few cases in which I would use anything except a maximum likelihood approach, which you can use in either LISREL or if you use SPSS you can add on the module AMOS which will do this as well. Tracey = 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
Dear Haytham, other issue concern with a measure of the latent construct is the unidimensionality. Hair et alli(1998): unidimensionality is an assumption underlying the calculation of reliability and is demonstraded when indicators of a construct have acceptable fit on a single-factor(one-dimensional) model.(...) The use of reliability measures, such Cronbach´s alpha, does not ensure unidimensionality but instead assumes it exists. The researcher is encouraged to perform unidimensionality tests on all multiple-indicator constructs before assessing their reliability. This reference is very important: Gerbing, David W., Anderson, James C. An updated paadigm for scale development incorporating unidimensionality and its assesment. Best regards, Alexandre Moura. P.S. Please accept my apologies for my English mistakes. - Original Message - From: haytham siala [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Friday, June 15, 2001 5:40 PM Subject: Factor Analysis Hi, I will appreciate if someone can help me with this question: if factors extracted from a factor analysis were found to be reliable (using an internal consistency test like a Cronbach alpha), can they be used to represent a measure of the latent construct? If yes, are there any references or books that justify this technique? = 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
The complete reference: Gerbing, David W., Anderson, James C. An updated paradigm for scale development incorporating unidimensionality and its assesment. Journal of Marketing Research. Vol. XXV (May 1988). Alexandre Moura. - Original Message - From: Alexandre Moura [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Saturday, June 16, 2001 9:26 AM Subject: Re: Factor Analysis Dear Haytham, other issue concern with a measure of the latent construct is the unidimensionality. Hair et alli(1998): unidimensionality is an assumption underlying the calculation of reliability and is demonstraded when indicators of a construct have acceptable fit on a single-factor(one-dimensional) model.(...) The use of reliability measures, such Cronbach´s alpha, does not ensure unidimensionality but instead assumes it exists. The researcher is encouraged to perform unidimensionality tests on all multiple-indicator constructs before assessing their reliability. This reference is very important: Gerbing, David W., Anderson, James C. An updated paadigm for scale development incorporating unidimensionality and its assesment. Best regards, Alexandre Moura. P.S. Please accept my apologies for my English mistakes. - Original Message - From: haytham siala [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Friday, June 15, 2001 5:40 PM Subject: Factor Analysis Hi, I will appreciate if someone can help me with this question: if factors extracted from a factor analysis were found to be reliable (using an internal consistency test like a Cronbach alpha), can they be used to represent a measure of the latent construct? If yes, are there any references or books that justify this technique? = 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/ = = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Normality in Factor Analysis
Hi, I have a question regarding factor analysis: Is normality an important precondition for using factor analysis? If no, are there any books that justify this. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Normality in Factor Analysis
In sci.stat.consult haytham siala [EMAIL PROTECTED] wrote: I have a question regarding factor analysis: Is normality an important precondition for using factor analysis? It's necessary for testing hypotheses about factors extracted by Joreskog's maximum-likelihood method. Otherwise, no. If no, are there any books that justify this. Any book on factor analysis or multivariate statistics in general. = 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
Hi there, would someone please explain in lay person's terms the difference betwn. principal components, commom factors, and maximum likelihood estimation procedures for factor analyses? Should I expect my factors obtained through maximum likelihood estimation tobe highly correlated? Why? When should I use a Maximum likelihood estimation procedure, and when should I not use it? Thanks. Rita [EMAIL PROTECTED] Unlike the other methods, maximum likelihood allows you to estimate the entire structural model *simultaneously* [i.e., the effects of every independent variable upon every dependent variable in your model]. Most other methods only permit you to estimate the model in pieces, i.e., as a series of regressions whereby you regress every dependent variable upon every independent variable that has an arrow directly pointing to it. Moreover, maximum likelihood actually provides a statistical test of significance, unlike many other methods which only provide generally accepted cut-off points but not an actual test of statistical significance. There are very few cases in which I would use anything except a maximum likelihood approach, which you can use in either LISREL or if you use SPSS you can add on the module AMOS which will do this as well. Tracey = 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
_Psychometric Theory_, by Jum Nunnally to name one. haytham siala wrote: Hi, I will appreciate if someone can help me with this question: if factors extracted from a factor analysis were found to be reliable (using an internal consistency test like a Cronbach alpha), can they be used to represent a measure of the latent construct? If yes, are there any references or books that justify this technique? -- Timothy Victor [EMAIL PROTECTED] Policy Research, Evaluation, and Measurement Graduate School of Education University of Pennsylvania = 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 of dichotomous variables
A list of such programs and discussion can be found at: http://ourworld.compuserve.com/homepages/jsuebersax/binary.htm The results of Knol Berger (1991) and Parry MacArdle (1991) (see above web page for citations) suggest that there is not much difference in results between the Muthen method and the simpler method of factoring tetrachoric correlations. For additional information (including examples using PRELIS/LISREL and SAS) on factoring tetrachorics, see http://ourworld.compuserve.com/homepages/jsuebersax/irt.htm Hope this helps. John Uebersax = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
PCA and factor analysis: when to use which
What is the basis for deciding when to use principal components analysis and when to use factor analysis. Could anyone describe a problem that illustrates the difference? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
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/ ===
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: 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/ ===
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: 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
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.
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.
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 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
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
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.
