Since there will often be too many comparisons for Bonferroni to be effective, this is often the only viable option if any adjustment to the probabilities is desired. Bonferroni will be too conservative if more than a few comparisons are made LSD(none), which is Fisher's Least Significant Differences, or unadjusted probabilities īonferroni, which simply multiplies the significance by the number of comparisons (with a maximum significance of 1).
The three adjustments available for the significances which are available in EMMEANS are: Or the differences might be larger for one gender. Or the differences between Females and Males might be positive for one drug, but negative for another. For example, there might be no difference between the genders for one of the drugs, but a significant difference for the other two. Again, since we assume that a significant interaction motivated this test, we anticipate observing some difference in the profiles. This time there would be three pairs of tests, Female versus Male, then Male versus Female (redundant, with the opposite sign). EMMEANS = TABLES(drug*sex) COMPARE(sex) ADJ(LSD)Īs part of the same analysis. We can request as many /EMMEANS subcommands as we wish, so we could simultaneously include (Half the tests are redundant, because they are for the same pair but in the opposite order, so the difference is the same but with the opposite sign.) Since we are assuming that there is a significant interaction, we anticipate that there will be some difference in the profiles of the two genders. If the three drugs are A, B, and C, we will see a table which will test, for Females, the hypotheses that there is no difference between A and B, A and C, B and A, B and C, C and A, C and B, and then the same six hypotheses but for Males. Now you can use the menu Run->All to re-run your analysis, which will now include a Test of Simple Effects.įor the hypothetical syntax above, suppose that drug has three levels, while sex has two. The completed syntax should be as follows: EMMEANS = TABLES(drug*sex) COMPARE(drug) ADJ(LSD)ĭiscard the request for a table of the drug main effect alone if you wish: it was convenient to request it to simplify cut-and-paste operations. Now, after the word COMPARE, type drug enclosed in parentheses: The subcommand /EMMEANS = TABLES(drug*sex) is the one we need to modify we need to specify the factor for which we want pairwise comparisons.Ĭopy COMPARE ADJ(LSD) from the subcommand /EMMEANS = TABLES(drug), and paste it after the interaction, so: /EMMEANS = TABLES(drug*sex) COMPARE ADJ(LSD). This will open a Syntax window, where you should find something like this: Now click the Continue button to return to the Univariate dialog box. This leaves a choice between SIDAK or none.) Note that since Bonferroni simply multiplies by the number of comparisons, it will be excessively conservative for most situations which require simple effects. (Leave this as LSD(none) if you don't have a preference. Choose the interaction(s) for which you wish to request Simple Effects, and click the triangle button to add them to the list "Display Means for:".Įven if you are not interested in any of the main effects, for convenience add one of the main effects, place a check in the box labeled "Compare main effects", and choose your preferred option for "Confidence interval adjustment". In the box labeled "Estimated Marginal Means", you should see a list of "Factors and Factor Interactions". (If not, set up the model at this time.) Click on the Options. Assuming that you just ran your ANOVA model and observed the significant interaction in the output, the dialog will have the dependent variables and factors already set up. Return to the General Linear Model->Univariate dialog.
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Here, we will describe how to make the necessary modifications to syntax pasted from the General Linear Model->Univariate dialog box.
Unfortunately, at this time to obtain a Simple Effects Test does require the use of SPSS command syntax. This test can be performed with SPSS General Linear Model, using the Estimated Marginal Means option. (For multi-way analyses, all combinations of levels of the other factors.) Sometimes these are referred to as Simple Main Effects. Simple Effects tests reveal the degree to which one factor is differentially effective at each level of a second factor. This will produce a table comparing all pairs of levels of one factor, for each level of all the other factors.