```Date: Wed, 16 Feb 2005 23:57:50 -0600 Reply-To: Jeffrey Berman Sender: "SPSSX(r) Discussion" From: Jeffrey Berman Subject: Re: nested anovas and reading expected mean squares Comments: cc: LUCINDA M TEAR In-Reply-To: <200502170500.j1H50rPG003270@nenya.memphis.edu> Content-type: text/plain; charset="US-ASCII" On 2/16/05 12:20 PM, LUCINDA M TEAR wrote: > Hello - I submitted this last week but no one responded. Thought I'd try > again. Thank you for any help! > > >> Hello: >> >> I am running a nested ANOVA with the following design: >> Factor A - fixed - 5 levels >> Factor B - Fixed - 3 levels >> Factor C(B) - Random, nested within Factor B - 3 levels per level of >> factor B >> AB interaction >> AC(B) interaction >> Error (unequal sample sizes in each cell). >> >> The way I calculate the EMS, >> A would be a pseudo F test and I should test >> B against C(B) >> AC(B) against Error >> AB against AC(B) and >> C(B) against Error >> >> If I run the model within Univariate GLM as >> /DESIGN = A B C(B) A*B A*C(B) >> Or >> /DESIGN = A B C*B A*B A*C*B >> >> From the error terms specified at the end of the output, I think that SPSS >> tested C(B) against AC(B) and A against AC(B): >> >> Error for Intercept: .984 MS(C(B)) + .016 MS(Error) >> Error for A: .973 MS(A * C(B)) + .027 MS(Error) >> Error for B: .990 MS(C(B)) + .010 MS(Error) >> Error for C(B): .977 MS(A * C(B)) + .023 MS(Error) >> Error for AB: .981 MS(A * C(B)) + .019 MS(Error) >> Error for AC(B): MS(Error) >> I'm not sure how to read the EMS table or some of my problem with my EMS >> is that I didn't completely account for the different sample sizes within >> each cell (I just called them all "n" while calculating my EMS). >> >> Can anyone give me any leads here? Are my EMS calculations wrong or am I >> not understanding the output? I'm happy to provide my EMS calculations or >> the entire data set. Lucinda: I suspect you're running into the same issue I encountered when developing examples with SPSS for my graduate statistics course. SPSS (and SAS) use a different model for deriving expected mean squares than that found in many analysis of variance textbooks. SPSS uses an "unconstrained parameters" model, whereas the derivation in the textbooks is a "constrained parameters" model. Differences between the models arise in situations in which some factors are random and others are fixed. There is a spirited debate among statisticians about which model is more appropriate or general. For competing perspectives, see: McLean, R. A., Sanders, W. L., & Stroup, W. W. (1991). A unified approach to mixed linear models. American Statistician, 45, 54-64. Voss, D. T. (1999). Resolving the mixed models controversy. American Statistician, 53, 352 - 356. Wolfinger, R., & Stroup, W. (2000). [Letter to the editor]. American Statistician, 54, 228-229. David Nichols of SPSS posted about this issue some time ago and I have attached his message below. Personally, I tend to be persuaded by Voss's arguments in favor of the constrained parameters model, at least in cases in which there are no empty cells in the design. However, this is not the model used when relying on the RANDOM subcommand of GLM (or UNIANOVA). -Jeffrey Berman University of Memphis ----- Forwarded message follows ----- From: nichols@spss.com (David Nichols) Subject: Expected mean squares and error terms in GLM Date: 1996/11/05 Message-ID: <55oa9t\$1tj@netsrv2.spss.com>#1/1 organization: SPSS, Inc. newsgroups: comp.soft-sys.stat.spss I've had a few questions from users about expected mean squares and error terms in GLM. In particular, with a two way design with A fixed and B random, many people are expecting to see the A term tested against A*B and B tested against the within cells term. In the model used by GLM, the interaction term is automatically assumed to be random, expected mean squares are calculated using Hartley's method of synthesis, and the results are not as many people are used to seeing. In this case, both A and B are tested against A*B. Here's some information that people may find useful. It would appear that there's something of a split among statisticians in how to handle models with random effects. Quoting from page 12 of the SYSTAT DESIGN module documentation (1987): There are two sets of distributional assumptions used to analyze a two factor mixed model, differing in the way interactions are handled. The first, used by SAS (1985, p. 469-470), can be traced to Mood (1950). Interaction terms are assumed to be a set of i.i.d. normal random variables. The second, used by DESIGN, is due to Anderson and Bancroft (1952). They impose the constraint that the interactions sum to zero over the levels of fixed factor within each level of the random factor. According to Miller (1986, p. 144): "The matter was more or less resolved by Cornfield and Tukey (1956)." Cornfield and Tukey derive expected mean squares under a finite population model and obtain results in agreement with Anderson and Bancroft. On the other side, Searle (1971) states: "The model that leads to [Mood's results] is the one customarily used for unbalanced data." Statisticians have divided themselves along the following lines: Mood (1950, p. 344) Anderson and Bancroft (1952) Hartley and Searle (1969) Cornfield and Tukey (1956) Hocking (1985, p. 330) Graybill (1961, p. 398) Milliken and Johnson (1984) Miller (1986, p. 144) Searle (1971, sec. 9.7) Scheffe (1959, p. 269) SAS Snedecor and Cochran (1967, p. 367) DESIGN The references are: Cornfield, J., & Tukey, J. W. (1956). Average values of mean squares in factorials. Annals of Mathematical Statistics, 27, 907-949. Graybill, F. A. (1961). An introduction to linear statistical models (Vol. 1). New York: McGraw-Hill. Hartley, H. O., & Searle, S. R. (1969). On interaction variance components in mixed models. Biometrics, 25, 573-576. Hocking, R. R. (1985). The analysis of linear models. Monterey, CA: Brooks/Cole. Miller, R. G., Jr. (1986). Beyond ANOVA, basics of applied statistics. New York: Wiley. Milliken, G. A., & Johnson, D. E. (1984). Analysis of Messy Data, Volume 1: Designed Experiments. New York: Van Nostrand Reinhold. Mood, A. M. (1950). Introduction to the theory of statistics. New York: McGraw-Hill. Scheffe, H. (1959). The analysis of variance. New York: Wiley. Searle, S. R. (1971). Linear models. New York: Wiley. Snedecor, G. W., & Cochran, W. G. (1967). Statistical methods (6th ed.). Ames, IA: Iowa State University Press. SPSS can be added to the left hand column. We're assuming i.i.d. normally normally distributed random variables for any interaction terms containing random factors. -- ---------------------------------------------------------------------------- - David Nichols Senior Support Statistician SPSS, Inc. Phone: (312) 329-3684 Internet: nichols@spss.com Fax: (312) 329-3668 ---------------------------------------------------------------------------- - ----- End of forwarded message ----- ```

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