Chapter 14, Table 5: Using SPSS Statistics

Chapter 14, Table 5: D Variables for Higher Order Designs for Repeated Measures (Multivariate Approach) via SPSS Point and Click

For the hypothetical data contained in Table 14.4, Table 14.5 gives an appropriate and substantively interesting set of D variables. The D variables (rather than the raw data itself) is used because of the benefits and flexibility gained from analyzing the D variables directly (rather than indirectly as we did with the Table 14.4 data).

1. Because the data of Table 14.5 is already in the form of D variables, rather than using the Repeated Measures procedures, the Multivariate procedures themselves can be used. Recall the Multivariate procedure can be used by clickingAnalyze, and then General Linear Model, and then by specifying Multivariate. Once the Multivariate procedure window opens, each of the D variables should be moved into the Dependent Variables box as follows:

image002 (10).jpg
Move D Variables to the Dependent Variables Box

2. By clicking on the Options button, additional nonstandard options can be specified. Because D variables can be formed with substantively interesting questions in mind, it is very likely that we are interested in the parameter estimates and confidence intervals for the D variables. When this is the case clicking on the Options button and then specifying Parameter Estimates by clicking in the Parameter Estimates box (so as to put a check mark there), will tell SPSS that we want to estimate the parameters and form confidence intervals for them.

image004 (6).jpg
Specify Parameter Estimates

3. After the Parameter Estimates box has been check, clicking Continue and then OK on the Multivariate menu will yield the results of interest (specifically the omnibus main effects and interaction as well as the specific contrasts specified by the D variables). Although we do not illustrate it here, forming other sets of D variables and following the procedures outlined will lead to answers of other (potentially) interesting questions. For example, the 11th D variable of the chapter tests the difference between the eighth degree and the zeroth degree conditions when noise is present. Although we do not form and test such a D variable here, with the methods we have illustrated, such a D variable could easily be formed and analyzed.