A researcher believes that recall of verbal material differs with the level of processing. He divided his subjects into three groups. In the low processing group, participants read each word and were instructed to count the number of letters in the word. In the medium processing group, participants were asked to read each word and think of a word that rhymed. In the high processing group, participants were asked to read each word and try to memorize it for later recall. Each group was allowed to read the list of 30 words three times, then they were asked to recall as many of the words on the list as possible. If the researcher wants to know whether the three groups have different amounts of recall, what type of statistical test should be used?
Possibly correct but not the best answer.
Regression analysis can be used to derive an equation from which we can predict scores on one continuous variable based on scores on one or more another variables. Regression analysis is not usually used to test hypotheses about the differences between groups (though advanced uses of regression analysis do allow for this option).
There is a statistical procedure that is designed to test the equality of means for multiple groups.
t-test for Independent Samples
The t test for independent samples is used to compare means derived from only two independent samples.
Here there are three independent groups.
One-way ANOVA can be used to test hypotheses regarding the equality of the means of three or more groups. If after testing your data you found significant differences amongst your groups, you could use a post-hoc technique such as Tukey’s test to determine which specific groups performed better than others.
Two-way or Factorial ANOVA is used to test the effects of two independent variables.
Here there is only one independent variable (level of processing with three levels), so the two-way design is not appropriate.
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The Expert says…
The null hypothesis is that the three population means are the same, indicating that the three interventions have the same effect.
One-way ANOVA is appropriate for this situation.
An assumption for the F-test in one-way ANOVA is that the distributions of scores within each population are close to normal and that the variances of scores within each population are approximately the same. You should begin your analysis by checking to see if these assumptions are reasonably well met.
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