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354 PART V: Analyzing and Reporting ResearchA side-by-side stem-and-leaf display for the two conditions of the vocabularystudy looks like this:Older AdultsCollege Students2* 32 72 3* 013 88943 4* 122344 578843 5* 0134997 5 6789944220 6* 298865 6430 7*888 732 8*Look at the display on the left, the one for the older participants. How wouldyou characterize it? The data seem to be somewhat normally distributed,although an extreme score, an outlier, appears to be present. The “32” doesn’tseem to belong with the rest of the data. (There are ways to operationalizeoutliers in terms of their distance from the middle of the distribution, and somecomputer programs will do this automatically.) Without additional informationabout the nature of the respondent (e.g., amount of medication taken thatday, or possible reading problems), the experimenters could find no reasonto exclude this score from the study. The presence of this possible outliernecessarily increases the amount of variability present in this group relativeto what it would be without this score. Nevertheless, we must acknowledgethat some data sets are naturally going to be more variable than others. Forexample, the older adults in this study simply may represent a more heterogeneousgroup of individuals than those in the college student sample. (There isa moral here: Obtain as much relevant information about your participants asis conveniently possible at the time you collect data. An extreme score shouldbe treated as a true score unless you know the score is extreme due to error orto circumstances unrelated to the study.)Now look at what the side-by-side stem-and-leaf display reveals about bothdistributions. You should immediately see that scores in the groups overlap tosome degree, but there are many more scores above 60 in the older group thanin the college group. This “picture” of the data begins to confirm the idea thatthe older adults performed better overall than the college students on this testof vocabulary size.Conclusion In the first stage of data analysis—the process of getting to knowour data—we should identify(a) the nature and frequency of any errors in the data set and, if errors arepre sent, whether corrections could be made or data need to be dropped;