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Hi Matthew,

Thanks very much for sending me the messages on the CRTNET listserv related to Amos. Some of the correspondents mentioned differences between fit values obtained from Amos and results from other SEM programs. I might be able to contribute some relevant technical information about this. Please feel free to forward this to the listserv.

Up until version 4.02, when a model included means and intercepts as explicit model parameters, Amos used a different baseline model than most other SEM programs used in computing fit measures like NFI, NNFI, CFI, etc. Amos's baseline model required each observed variable to have a mean of zero. By contrast, most other SEM programs allowed the means to be unconstrained in the baseline model. Because Amos's baseline model typically fit extremely badly, fit measures like NFI, NNFI, CFI etc. took on larger values in Amos than in most SEM programs. In other words, Amos's baseline model was so bad it made all your models look good by comparison. Amos's old baseline model (used prior to version 4.02) was not wrong, because the choice of baseline model is up to the individual. In fact, Amos 5 still allows that old baseline model, among others, as an option when you perform a specification search. However, the difference between Amos's baseline model and the one used by most other S!

 EM programs was causing a lot of confusion, and so the decision was finally made in 4.02 to allow means to be unconstrained in the Amos's standard baseline model. So in version 4.02, Amos fell into line with the other SEM programs. I might mention that in Amos 5, when doing a specification search, you can use any of four baseline models -- means can be either fixed at zero or unconstrained, and correlations can be either fixed at zero or constrained to be equal.

As for BIC, the Bayes Information Criterion, I think the reason that one of the correspondents questioned Amos's BIC calculation is that Adrian Raftery provided two different formulas for computing BIC. The Raftery references are given at the end of this email. Up through version 4, Amos used the 1993 formula. Amos 5 uses the 1995 formula.

There were also some comments about Amos that are not related to correctness of results, but have more to do with the user interface and ease of use. I read these comments with great interest, but I mainly wanted to address the issues of correctness of results.

If any CRTNET members want to follow up, please ask them to get in touch with me at ggwebm@amosdevelopment.com.

Best regards, Jim

BIC REFERENCES


Raftery, A. (1993). Bayesian model selection in structural equation models. In K. Bollen & J. Long (Eds.), Testing structural equation models (pp. 163-180): Newbury Park, California.

Raftery, A. (1995). Bayesian model selection in social research. In P. Marsden (Ed.), Sociological Methodology 1995 (pp. 111-163): San Francisco.


Communication Research Measures -- Volume II

A new volume of scales and indexes in communication is now underway and we encourage you to nominate measures that you find particularly useful for your research. In this new volume, we'll be covering group, intercultural, organizational, health, interpersonal, mass, electronic media, instructional, political, and other related areas. We'll also include some measures from outside the discipline that communication researchers have found useful (e.g., sensation seeking, locus of control, personality). Mainly we'll focus on measures created in the last 10 years, but might consider some that weren't included in the first volume.

Send the measure's name (and citation, if you have it) to me and I'll forward them to my editorial colleagues on this project (Dave Seibold, Betsy Perse, Beth Graham, and Alan Rubin).

Thanks for your input!

Rebecca Rubin rrubin@kent.edu

--- Here is a review of statistical resources on the web

and a list of free statistical software

and then, after you do the statistics, here are links to sites on how to present data

hope this is useful!

gene shackman The Global Social Change Research Project



Correction: Bartlett test in this particular application does not test if the correlation matrix is an identity matrix (or it would almost always reject the null) but that the *residual* correlation matrix is identity.


-- Last edited May 15, 2012

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Unless otherwise noted, the contents of this page were written by participants on the Media Space Wiki, operated by Davis Foulger, and should be cited accordingly. For example (APA):
Foulger, D. and other participants. (May 15, 2012). Statistical Methods. MediaSpaceWiki. Retrieved on from http://evolutionarymedia.com/wiki.htm?StatisticalMethods
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