To conduct a Factorial ANOVA one only need extend the logic of the oneway design. Table 13.2 presents the data for a 2 by 5 factorial ANOVA. The first factor, AGE, has two levels, and the second factor, CONDITION, has five levels. So, once again each observation can be uniquely coded.
|Old = 1||Counting = 1|
|Young = 2||Rhyming = 2|
|Adjective = 3|
|Imagery = 4|
|Intentional = 5|
For each pairing of AGE and CONDITION, there are 10 observations. That is, 2*5 conditions by 10 observations per condition results in 100 observations, that can be coded as follows. [Note, that the names for the factors are meaniful.]
Examine the table carefully, until you understand how the coding has been implemented. Note: one can enhance the readability of the output by using Value Labels for the two factors.
To compute the relevant statistics - a simple approach,
By clicking on the [Options...] button
one has the opportunity to select the Method used. According
to the online help,
For the our purposes, selecting the Hierarchical, or the
Experimental method will make available the option to
output Means and counts. --- Note: I don't know the details
of these methods, however, they are probably documented.
"Method: Allows you to choose an alternate method for
decomposing sums of squares. Method selection controls how the effects
For the our purposes, selecting the Hierarchical, or the Experimental method will make available the option to output Means and counts. --- Note: I don't know the details of these methods, however, they are probably documented.
As you can see the use of the Means and count option produces a nice summary table, with all the Variable Labels and Value Labels that were incorporated into the datasheet. Again, the use of those options makes the output a great deal more readable.
The output is a complete source table with the factors identified with Variable Labels
Higher order factorial designs are carried in the same manner as the two factor analysis presented above. One need only code the factors appropriately, and enter the corresponding observations.
Repeated measures designs will be discussed in the next section.