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: EDA
Data mining , by and large, seems to use fairly conventional multivatiate stats tools along with a bunch of clustering procedures. In addtion there is a lot of use of neural nets (mostly as a lazy man's tool or a last resort, but occasionally sensibly). Data prep. (including transformations) seem to be a necessity. A good starter book is Data Preparation for Data Mining by Dorian Pyle. It is equivalent to the first part of a low level intro stats book and is mainly concerned with assessing the distributions, variance structure, etc. before deciding to press ahead. I have not so far seen a sensible book on data mining itself. Definitely none equivalent to the many fine texts out there on ultivariate statistics. Many of the DM books are sales blurbs for one or another black-box package. things should change for the better in a couple of years. SR Millis wrote: I'm looking for recommendations for recent books and papers on basic techniques for exploratory data analysis. Thanks, SR Millis = 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?
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: 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/ =
Re: errors in journal articles
The earlier responders make some good points but..I have seen plotted regression lines when the rsquare was 0.005, scatterplots where two populations were separated by a line that makes a southern gerrrymander envious, where clusters had fewer than 3 members, etc. etc. The whole thing would be funny but these journal articles are used to make policy, affect legislation, etc. there is hell to pay if a chemist misreads a spectrum or a geologist confuses east from west. My feelingis that most egregious stuff should be recognized by a comment in the journal. Sending in a comment to a journal is also a good learning experience for the student in that she have to be really sure it is a blooper and that the blooper makes a difference in the conclusions. Lise DeShea wrote: List Members: I teach statistics and experimental design at the University of Kentucky, and I give journal articles to my students occasionally with instructions to identify what kind of research was conducted, what the independent and dependent variables were, etc. For my advanced class, I ask them to identify anything that the researcher did incorrectly. As an example, there was an article in a recent issue of an APA journal where the researchers randomly assigned participants to one of six conditions in a 2x3 factorial design. The N wouldn't allow equal cell sizes, and the reported df exceeded N. Yet the article said the researchers ran a two-way fixed-effects ANOVA. One of my students wrote on her homework, It is especially hard to know when you are doing something wrong when journals allow bad examples of research to be published on a regular basis. I'd like to hear what other list members think about this problem and whether there are solutions that would not alienate journal editors. (As a relative new assistant professor, I can't do that or I'll never get published, I'll be denied tenure, and I'll have to go out on the street corners with a sign that says, Will Analyze Data For Food.) Cheers. Lise ~~~ Lise DeShea, Ph.D. Assistant Professor Educational and Counseling Psychology Department University of Kentucky 245 Dickey Hall Lexington KY 40506 Email: [EMAIL PROTECTED] Phone: (859) 257-9884 = 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: compartments
Dennis: without going into chapter and verse,I think you are touching on sumpin real. The excitement these days tends to be at interfaces between disciplines not at the centers of old disciplines. Our academic departments were largely defined in the 19th century--some have made the jump--astrophysics, biochemistry, nonlinear economics, etc. But by and large the students still labor in the departmental centers not on the interfaces. There has to be some reason why wall streetfirms are hiring topologists, that pure math has been mad by theoretical phsicists, that bioengineering is hot stuff. Give em hell man. dennis roberts wrote: the difficulty in discussing new courses and other issues is that ... academe is a compartment system. most institutions have what is labelled as general education ... so that, it is assumed that it is GOOD for an undergraduate to have some from the science compartment, some from the quantitative compartment, some from the humanities compartment, so on and so forth. in many cases, this work is done before one declares the major. BUT, when we get to the major, we find more compartments ... in fact, more specific compartments ... in psychology for example, there is the personality compartment, motivation compartment, learning compartment, and so on then folks who are courageous might actually move to the graduate level and, guess what? MORE COMPARTMENTS AND MORE SPECIFICITY within each ... we have educational psychology and, there is the statistics compartment, the measurement compartment, cognitive learning compartment, and so on. this is how we have structured ourselves ... and this is how we act. and we cannot break out of that mold. in the area of research, the ideal approach would be to start off a cohort group ... and, begin real simple. say ... we design a very VERY simple survey ... a few demographics ... do some piloting to see that it makes sense to takers ... then begin to talk about how we might work with the data once we get some ... we write up what we did, what we found, and limitations to what has transpired then, we move up a notch ... perhaps work on a scale of some sort ... like an attitude scale ... work on the notion of developing items to measure some underlying construct ... actually construct some items ... do some pilot work ... see what happens ... and introduce some notions of reliability ... what it is ... how it is assessed ... how we can improve it ... and perhaps bring in some notions of validation too ... how scores on this measure might relate to other variables of interest ... we offer up some hypotheses about what should be related to what ... and when see gather some data ... we again come back to how we might handle the data ... perhaps bringing in the notion of correlation ... simple regression and the like and we write up the results ... say what we did ... how we handled the data ... what the problems were ... and try to summarize what we found then, we might turn to a simple experimental situation ... where we think of some useful independent variable to explore and manipulate ... talk about how do design and implement such a study ... how we recruit and assign Ss to conditions ... collect data .. and then approach how we might handle data of this sort ... maybe anova gets some air time ... then we write up the results ... say what we did ... tell what problems we ran into ... and summarize what we found in the long run, over several semesters ... we build up a good basket of skills THROUGH EXPERIENCING the acts ... we learn by doing ... discussing ... summarizing ... and then moving up the ladder of complexity but, this approach ... is almost impossible to implement within standard university settings ... whether it be for general education ... for work in the major ... or for graduate study BECAUSE ... our instruction and methods have been SO COMPARTMENTALIZED ... and usually, faculty are only really competent to teach in one maybe two of these subdivisions ... the only practical way to do this would be for ONE entire department ... that has complete control over THEIR say 200 students ... could revamp what they do and what their students take ... but, this is a pipe dream ... and it is a super pipe dream if you happen to be a department that is expected to provide overall SERVICE COURSES ... for those outside of your OWN group of students so, back to the main issue ... trying to have a survey course ... in whatever such approaches cover the water ... FAST with no depth ... and that seems to be the way programs want it nowadays ... especially when a student ventures outside of his or her COMPARTMENT ... so, do i think that a book or course can be designed in a way that will focus on READING AND INTERPRETING articles and research reports? well, sure ... but, if the students don't have the PREREQUISITE SKILLS in analysis, measurement, design,
Re: John Tukey
a great spirit. An ornament to the Profession. A person who made all of our lives easier. A person who wrote with the gusto and spirit of an enthusiast. A Hero. Robin Becker wrote: In article [EMAIL PROTECTED], Petr Kuzmic [EMAIL PROTECTED] writes Donald Macnaughton wrote: John Wilder Tukey died last night ... very sad news -- Robin Becker = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
