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SPSS Data Analysis Examples
One-way Manova

Examples of One-way Multivariate Analysis of Variance

Example 1. A researcher randomly assigns 33 subjects to one of three groups. The first group receives technical dietary information interactively from an on-line website. Group 2 receives the same information in from a nurse practitioner, while group 3 receives the information from a video tape made by the same nurse practitioner. The researcher looks at three different ratings of the presentation, difficulty, useful and importance, to determine if there is a difference in the modes of presentation. In particular, the researcher is interested in whether the interactive website is superior because that is the most cost-effective way of delivering the information.

Description of the Data

Let's pursue Example 1 from above.

We have a data file, manova.sav, with 33 observations on three response variables. The response variables are ratings of useful, difficulty and importance. Level 1 of the group variable is the treatment group, level 2 is control group 1 and level 3 is control group 2.

Let's look at the data.

Some Strategies You Might Be Tempted To Try

Before we show how you can analyze this with a canonical correlation analysis, let's consider some other methods that you might use.

SPSS One-way Manova

Although this is a multivariate analysis, we will begin by looking at the separate univariate anovas to get a feel for what is happening with the data. Please note: Not all of the SPSS output will be shown on this page and it will not necessarily be displayed in the order that it appears in the printout.

While none of the three anovas were statistically significant at the alpha = .05 level, in particular, the anova for difficulty was less than 1.

Next, we will look at the manova itself.

Now that we have have determined that the overall multivariate test is significant, we will follow up with several post-hoc tests. In particular, we wiil look at group 1 versus the average of groups 2 and 3 and also group2 versus group 3. These comparisons are performed using the contrast statement. The matrix needs as many rows as there are groups. The first row of the matrix needs to be all one's. The design statement is also needed to display the contrasts.

We know from the univariate tests above that difficulty by itself was clearly not significant so we will look at a test using the combination of useful and importance. This analysis is performed using the transform statement. Once again the matrix needs three rows, although we are only interested in the second row which will be labed as T2 in the output. The first row of the matrix needs to be all one's.

Sample Write-Up of the Analysis

There is a lot of variation in the write-ups of multivariate analysis of variance. The write-up below is fairly minimal, more detail may be required for most instances.

The multivariate test of differences between groups using the Wilks Lambda criteria was statistically significant (F(6, 56) = 3.54; p=0.0049). Follow-up multivariate comparisons showed that the treatment group was significantly different from the average of control 1 and control 2 (F(3,28) = 8.31; p=0.0004). Further, it was determined that control 1 and control 2 were not significant different (F(3,28) = 0.06; p=0.9785). Each of the F-ratio transformations of the Wilks criteria were exact.

None of the separate univariate anovas were statistically significant. In particular, the univariate test for difficulty has an F less than 1, so the analysis was rerun using the combination of useful and importance, which was statistically significant (F(2,30) = 12.99; p<0.0001).

Cautions, Flies in the Ointment

  • There is an assumption that the response variables are multivariate normal distributed.
  • Small samples can have low power but if the multivariate normality assumption is met the manova is generally more powerful than separate univariate tests.
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