| If you can understand where the means for main effects and interactions
are for a 2 (participant sex) x 2 (dress condition) x 2 (attitudes toward
marriage). Analysis of variance (ANOVA), then you should be able to apply
this knowledge to other types of factorial designs. In this example, male
or female participants read about a marital rape victim who is dressed
somberly or suggestively and then made ratings of how responsible the victim
was. An additional independent variable was created from participants responses
to an attitudes toward marriage scale and resulted in two conditions: those
participants having more traditional attitudes toward marriage and those
having more modern attitudes toward marriage. The results of the analysis
appear below:
...This is the ANOVA Summary Table...
Recall that when you are writing up a results section you want to cover
three things:
You should be able to see that there are significant main effects for sex of participant (SEX), dress condition (COND), and attitudes toward marriage (ATMM). How do you know? If you examine the p-values for these main effects, then you can see that the values are less than .05. The F-statement and effect size calculation ( r ) for sex of participant would be: F(1, 152) = 20.70, p < .05 (r = .35). How would you write up the significant main effect for participant sex? Like this... A 2 (sex of participant) x 2 (dress condition) x 2 (attitudes toward marriage) analysis of variance (ANOVA) was calculated on participants' ratings of victim responsibility. There was a significant main effect for participant sex, F(1, 152) = 20.70, p < .05 (r = .35). In general, male participants assigned a greater amount of responsibility to the victim (M = 5.83, SD = 2.33) than did female participants (M = 4.26, SD = 2.62). Note:
The " 2 (sex of participant) x 2 (dress condition) x 2 (attitudes toward
marriage) analysis of variance (ANOVA)"
You would then continue on doing the same thing for any other significant main effects and interactions. If the analysis was not significant, then you would still need to provide the F-statement, but you would not have to describe it. For example: The Sex of Participant x Dress Condition interaction was not significant, F(1, 152) = 0.40, p > .05 (r = .05). What about significant interactions? You would deal with significant interactions in the same way. However, when you describe the interaction, you do not include means and standard deviations, because those would be put in a table. For example: There was a marginally significant Dress Condition x Attitudes toward Marriage interaction, F(1, 152) = 2.94, p < .09 (r = .14). As can be seen in Table 1, in the somberly dressed condition, participants holding traditional attitudes toward marriage assigned more responsibility to the victim than did participants holding more modern attitudes toward marriage. In the suggestively dressed condition, participants holding traditional attitudes toward marriage assigned more responsibility to the victim than did participants holding more modern attitudes toward marriage. Of course, you will have to create a table with the appropriate information. Where would you find the appropriate information? Well, using the descriptive statistics table, you would look for that information under the three main collumns: SEX-COND-ATMM. For example, in the above interaction description, you would find the descriptive statistics across from "total-somberly-traditional" (M = 5.25), "total-somberly-modern" (M = 3.85), "total-suggestive-traditional" (M = 5.69), and "total-suggestive-modern" (M = 5.42). Test Yourself: Write-up the
significant main effects for "COND" and "ATMM." In addition, attempt the
write-up for the significant
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