Chapter 14: Misconceptions
When people read, hear, or prepare research summaries,
they sometimes have misconceptions about what is or isn't "sound
practice" regarding the collection, analysis, and interpretation
of data. Here are some of these common (and dangerous) misconceptions
associated with the content of Chapter 14.
 In a repeated measures ANOVA, it's only fair to administer
the treatments in the same order to everyone.
 The Ftest for the source called "Subjects"
(or what might be called "Between Subjects") provides as much
useful information as any of the other Ftests.
 In a twoway, threeway, or higherorder repeated
measures ANOVA, just one of the MS values serves as the denominator
for all Ftests of main effects and interactions.
 Carryover effects are eliminated if the treatments
are administered in all possible orders to the study's subjects.
 Because each cell mean is based on the same amount
of data, a repeated measures ANOVA is robust to its underlying assumptions.
 The number of subjects involved in a repeated measures
ANOVA can be determined by adding 1 to the df value on the "Total"
row of the summary table.
 A repeated measures ANOVA has the same underlying
assumptions as do ANOVAs without repeated measures.
 A 2x4x6 treatmentsbytreatmentsbysubjects ANOVA
will generate 7 Fvalues, just as is the case in any "regular"
threeway ANOVA.
 Fvalues that turn out significant using the GeisserGreenhouse
conservative Ftest procedure should be viewed with skepticism.
 A splitplot factorial can only be used in the field
of agriculture.
 A Lindquist Type I ANOVA is to be avoided because
it's highly likely to generate Type I errors.
 If the groups that form the levels of the betweensubjects
factor have the same n, the researcher is not obligated to check on
the sphericity assumption.
 A threeway mixed ANOVA always has 2 betweensubjects
factors and 1 withinsubjects factor.
 The total number of subjects in a mixed ANOVA can
be determined by adding 1 to the df value on the "Total" row
of the summary table.
 All of the Fvalues of a mixed ANOVA have the same
amount of statistical power.
 A split plot factorial 2.22 has 2 factors, with 2
levels in the first factor and 22 levels in the second factor.
 A twoway mixed ANOVA is like a regular twoway ANOVA
in that (1) three Fvalues will appear in the ANOVA summary table and
(2) the same MS serves as the denominator when computing the three Fvalues.
 In a mixed ANOVA, tests of simple main effects can
be used to compare the different levels of the betweensubjects factor(s)
but cannot be used to compare the different levels of the withinsubjects
factor(s).
