In this section we turn to Max & Onghena’s (“Some issues
in the statistical analysis of completely randomized and repeated measures
designs for speech, language and hearing research”, JSLHR 42, 261-270, 1999) second critique of common practice in doing ANOVAs:
the fact that we’ve been ignoring the problem of possible sphericity violations in repeated measures designs. StatView does not deal with this problem, and thus
it presents a limitation on using StatView to do your repeated measures analyses.

Sphericity

Some
options and partial solutions

__Option
0__: Of course if you run the test in StatView and there’s
no significant effect, then you’re ok – we’re talking here about over-estimating
significance and getting a false positive; if you get no effect even with
such a method, then you know there really is no effect, and there’s no need
to go to special lengths to continue to fail to get an effect.

__Option
1__: Test for sphericity
violations, i.e. test for correlations among variables.
StatView provides Bartlett’s test of sphericity.
A resulting high chi-square value with a low p value is BAD, but if
the data are uncorrelated you’re probably OK. (However,
Max & Onghena are dubious about the integrity of such tests in the first
place, so even this isn’t clear. We’ll see proof
that this is a waste of time with our later sample analysis.) But in any event note that StatView’s test doesn’t
do anything as a result of a significant correlation, or tell you what to
do to correct for the violation. All the test
does is tell you if there’s a problem (maybe).

__Option
2__: Use StatView’s MANOVA
instead of ANOVA; a significant result from this test is valid; but this procedure
is very conservative, i.e. unlikely to show significant differences

how to do MANOVA in StatView:

*Open
their Exercise file, and under Analyze – ANOVA, select MANOVA. Ignore “independent variable”, drag the variables
into “dependent variables” and “factors” as appropriate.
This gves all the separate ANOVA tables, plus the other stuff.
*

* Do some analysis so that you
have the Analysis Browser open (to the left) -- e.g. the file
3-level-compact -- do RM ANOVA (select View - Variable
Browser if it’s not already there); then under Analysis Browser
- ANOVA, double-click MANOVA Tables (you can’t use the regular menu for
this as it defaults to factorial design; but the browser gives you the choice);
be sure to select Repeated Measures design; AND use the Variable Browser
to declare your compact variable as dependent (note that you can do this
after the analysis fails to run – it will run after you do this)
*

__Option
3__: use SPSS
– it allows you to correct the df according to the degree of correlation
in the data – see next
section

* last updated
Dec. 2006 by P. Keating*

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