Re: (none)
Thanks, Rich. My semi-automatic crap detector hits DELETE when it sees things like this anyway; but... did you notice that although SamFaz (or whoever, really) claims to cite a bill passed by the U.S. Congress he she or it is actually writing from Canada? I'm not quite sure what to make of that... On Wed, 2 May 2001, Rich Ulrich wrote: On 1 May 2001 16:14:28 -0700, [EMAIL PROTECTED] (SamFaz Consulting) wrote: Under the Bill s. 1618 title III passed by the 105th US congress this letter cannot be considered SPAM as long as the sender includes contact information and a method of removal. To be removed, hit reply and type ?remove? in the subject line. Here was a message posted, that my reader saw as an attachment. The lines above were at the start of the SPAM. Ahem. I am about 100% sure that the above is a lie. In multiple ways. For instance, Is there a legal definition of SPAM? snip, useful advice, because you've all already read it -- Don. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-472-3742 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Estimating methods in SEM
[EMAIL PROTECTED] (Rodney Carr) writes:
The problem I am having is that I'm not sure what estimating method
to use. EQS implements a number of different methods (Maximum
Likelihood, Least Squares, GLS, etc). Unfortunately they give quite
different results. Actually, LS gives fit indices that are fairly
high, but none of the others do (so I'd like to use the LS method!).
But I can't find any references that explain which method should be
used. Please, do you have any ideas for where I might look for
advice?
Hi Rodney, try the following sources:
SEMNET forum at http://www.gsu.edu/~mkteer/semnet.html (they feature an
archive of previous discussions which is quite helpful)
@Book{chou-bentler95,
booktitle ={Structural Equation Modeling. Concepts, Issues,
and Applications},
publisher ={Sage},
year = 1995,
editor = {Hoyle, Rick H.},
address = {Thousand Oaks, London, New Delhi}
}
@Book{kline98,
author = {Kline, Rex B.},
title ={Principles and Practice of Structural Equation Modeling},
publisher ={Guildford Press},
year = 1998,
address = {New York, London}
}
@Article{hoogland-boomsma98,
author = {Hoogland, Jeffrey J. and Anne Boomsma},
title ={Robustness Studies in Covariance Structure Modeling},
journal = {Sociological Methods \ Research},
year = 1998,
volume = 26,
pages ={329-367}
}
@Book{garson98,
author = {Garson, David},
title ={Structural Equation Modeling},
publisher ={College of Humanities and Social Sciences, North
Carolina State University},
year = 1998,
address = {\url{www2.chass.ncsu.edu/garson/pa765/structur.htm (13.11.00)}}
}
=
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Re: The apporach is important to me
Glen Barnett wrote: Glen Barnett [EMAIL PROTECTED] wrote in message news:9cp1q4$2ko$[EMAIL PROTECTED]... As a piece of general advice, take a look at George Polya's book How To Solve It. It's a very old book, but it contains some very useful advice. a summary of Polya's approach (but without the flavour of the book) can be found at: http://www.cis.usouthal.edu/misc/polya.html It's definitely worth reading the book, though. Glen Thank you so much Glen. If you can't solve a problem, then there is an easier problem you can solve: find it. - George Polya I am taking his advice seriously. Abdul Rahman = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
(no subject)
subscribe ,edstat-livan balducci, unesp = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
old fangled technology
A friend of mine sent me the following and, I decided to scan and post. These relate to old interpretations of NEW technology terms like ... modem, mega hertz, and the like. Some of these are a HOOT! It's best to follow the links in order ... some frames follow after others. I HAVE THIS FEELING THAT I HAVE SEEN THIS BEFORE ... BUT, SOME QUICK SEARCHING FAILED TO FIND ANY SOURCE. IF ANYONE KNOWS THE SOURCE OF THESE FUNNIES, PLEASE LET ME KNOW SO I CAN GIVE RIGHTFUL CREDIT. http://roberts.ed.psu.edu/users/droberts/mtbcommands/OldTech.htm _ dennis roberts, educational psychology, penn state university 208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED] http://roberts.ed.psu.edu/users/droberts/drober~1.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: Combinometrics
David Heiser wrote: We seem to have a lot of recent questions involving combinations, and probabilities of combinations. I've never seen multiset enumeration in elementary stats texts, perhaps because it is not very useful as a sampling model. While a multiset can certainly be the outcome of a sampling experiment, it is usually not natural to take a sample in which every multiset appears with the same probability, and so it is more useful to treat the (ordered) list of outcomes with repetition as the primitive model. It does turn up in thermodynamics and the discrete math courses taken by CS students. That said, there IS at least one natural application of such a sampling technique, used in a major industry, where it saves millions of dollars a year. Anybody know what I mean? I'll give the answer later! -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Omissions in Journal Articles
There was a recent discussion here of errors in journal articles. A related topic is incomplete information, or at least what I consider incomplete information. A recent article in the American Journal of Epidemiology (2001, Vol 153, No 6, 596-603) contains some nicely laid out (and badly titled, but save that for another thread) tables that show the mean value of a dependent variable in male and female subsamples, broken down by a dichotomous independent variable (exercise? Yes/No), and adjusted first by age and then by age and several other numerical variables. In a sociological journal I would most often see this analysis reported in multiple regression form, but again, that's another thread. In health related journals the convention is to speak of adjusted means, i.e. the predicted dependent variable value for members of each category of the independent variable, with the other predictors (covariates) set to specific values. The article does not specify what values of age (and the other predictors) were used to create the adjusted means. Instead there is a footnote to the table that says: Adjusted means calculated by using analysis of covariance. My question, directed to those of you who are more familiar with journals in this area than I am, is whether this is a standard footnote / explanation, which is supposed to make clear to regular readers what has been done? From my perspective it is inadequate, since the ANCOVA (or regression analysis) has merely produced a predictive formula, and any values whatsoever of the covariates could be plugged in to the equation. Now I happen to know what SPSS v10 does when asked to produce estimated means in its univariate GLM procedure: it plugs in the mean values. The output actually contains the values of those means for the record. (Is this true with other statistical software?) A user who knows the syntax can actually specify the values, but the Windows point+click screen doesn't allow that. Here's an example of the default subcommand statement: /EMMEANS = TABLES(exercise) WITH(age=MEAN xother=MEAN) Using the mean can produce misleading adjusted values, especially when the table contains subsample comparisons as in this article, where all analyses were sex-specific. If the default SPSS ANCOVA were followed, the adjusted values would be created at different values of age, since mean age differs by sex, and other covariate means are also different in this study. (In addition, different tables contained analyses of 2 other subgroups of the sample, with different mean Xs.) This may or may not be a problem for the authors' interpretation of the results, but it seems reasonable to expect editors to be more sensitive to their readers' need to know exactly what is going on. -- ** `o^o' * Neil W. Henry ([EMAIL PROTECTED]) * -:- * Virginia Commonwealth University * _/ \_ * Richmond VA 23284-2014 ** * http://www.people.vcu.edu/~nhenry * *** = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Analysis of a time series of categorical data
If there is a better venue for this question, please advise me. I am looking for methods to analyze categorical data similar to that shown below. If the results were quantitative, I believe that an analysis of covariance would be appropriate. However, with categorical data and relatively small samples, I am at a loss. Any help would be appreciated. The purpose of the experiment was to discover whether or not two groups of infectious organisms differ in there ability to infect a host over time. The two genetically different groups of infectious organisms (G1 and G2) are each subdivided into three subgroups based on smaller genetic differences. They are G1-S1, G1-S2, G1-S3, G2-S4, G2-S5, and G2-S6. The hosts must be sacrificed to discover which ones are infected. This results in counts of infected and non-infected hosts. (A critical biological point is that an infected host can become uninfected with time.) For each subgroup an unequal number of hosts are sampled at each of 4 time points such that the results look something like this for one type of host organism. Time point 1Time point 2Time point 3Time point 4 Hosts Inf Not-InfInf Not-InfInf Not-InfInf Not-Inf Tested G1-S11 14 11 4 11 1 13 2 57 G1-S27 8 12 3 14 2 15 8 69 G1-S31 246 18815915 95 G2-S43 12 12 4 10 4 14 2 61 G2-S55 105 68 7 1114 57 G2-S62 26 12 12 1116 1412 105 The questions are how can group 1 (G1) be compare to group 2 (G2) and how can subgroups be compared. I maintain that the heterogeneity within each group does not prevent pooling of the subgroup data within each group, because the groupings were made a priori based on genetic similarity. -- R. Mark Sharp, Ph.D. [EMAIL PROTECTED] Southwest Regional Primate Center Tel: 210-258-9476 Director of Biostatistics and Scientific ComputingFax: 210-258-9883 Southwest Foundation for Biomedical Research P.O. Box 760549 7620 West Loop 410 at Military Drive San Antonio, TX 78245-0549 = 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
Joel Best is a professor of sociology and criminal justice at the University of Delaware. This essay is excerpted from _Damned Lies and Statistics: Untangling Numbers From the Media, Politicians, and Activists_, just published by the University of California Press Telling the Truth About Damned Lies and Statistics By JOEL BEST The dissertation prospectus began by quoting a statistic -- a grabber meant to capture the reader's attention. The graduate student who wrote this prospectus undoubtedly wanted to seem scholarly to the professors who would read it; they would be supervising the proposed research. And what could be more scholarly than a nice, authoritative statistic, quoted from a professional journal in the student's field? So the prospectus began with this (carefully footnoted) quotation: Every year since 1950, the number of American children gunned down has doubled. I had been invited to serve on the student's dissertation committee. When I read the quotation, I assumed the student had made an error in copying it. I went to the library and looked up the article the student had cited. There, in the journal's 1995 volume, was exactly the same sentence: Every year since 1950, the number of American children gunned down has doubled. This quotation is my nomination for a dubious distinction: I think it may be the worst -- that is, the most inaccurate -- social statistic ever. Full text: http://chronicle.com/free/v47/i34/34b00701.htm -- Warren S. Sarle SAS Institute Inc. The opinions expressed here [EMAIL PROTECTED]SAS Campus Drive are mine and not necessarily (919) 677-8000Cary, NC 27513, USA those of SAS Institute. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
