General instructions. Please write your full name at the top of every page you hand in. Please collate and staple your pages together. Please write clearly and thoroughly explain all your computations; an unannotated series of formulas that mysteriously ends up with the correct number will not be given full credit. When doing the homework, you are encouraged to use all resources at your disposal to the extent that they help you learn the material; nevertheless, you must write your own answers in your own words.

1. (8 pts.)
   Load into SPSS the data from Table 11.5 of the textbook.
   • Do an omnibus test on the age factor using repeated measures ANOVA and the Greenhouse-Geisser correction to interpret significance. Include the ANOVA table and explain what part of the table you used to make your conclusion. (You did this in lab; just do it again here.)
     
   • To every score from subject 2, add 1000. From every score from subject 12, subtract 100. Include a print out of the resulting data table. Do an omnibus test on the age factor using repeated measures ANOVA and the Greenhouse-Geisser correction to interpret significance. How does the result compare to the previous part?
     
   • Explain why adding a constant to every score from a subject makes no difference to a within-subject ANOVA.

2. (12 pts.)
   Load into SPSS the data from #19, p. 571. Read the paragraph that explains the experiment from which these data were generated.
   • Make a boxplot of the data from the four conditions. Are there outliers?
     
   • Make a graph of the means from the four conditions.
     
   • Run an omnibus repeated-measures ANOVA, using the Greenhouse-Geisser correction for non-sphericity. Include the table and your conclusion.
     
   • The experiment was run using infants, who often are impatient during experiments and cannot be run through several conditions because they fuss and cry. So it might be necessary to run the experiment using a between-subject design. Analyze the data as if they were from a between subject design. Compare the results with the repeated-measures analysis.
     
   • It is often difficult to get large numbers of infants to participate in an experiment; there are only so many babies in a community with parents who are willing to go to the effort to participate in an experiment. So there is a big incentive to use within-subject designs if possible, to reduce the number of subjects needed. Briefly discuss how the treatment effects themselves in this experiment might differ when the conditions are run between-subject or within-subject. As a separate issue, also discuss possible differential carryover effects for the specific conditions in a within-subject design.