Contents

372 PART V: Analyzing and Reporting Researchand psychological well-being. A researcher then seeks to show how variousresponses are related, that is, how they are correlated. Do people who claim tohave low self- esteem also report having difficulty dating? Is length of time childrenspend in day care related to measures of their attachment to their mothers?Do SAT scores predict success after college?In what follows we examine how researchers analyze and interpret acorrelational study.Stage 1: Getting to Know the DataBecause there are always two sets of scores in a correlational study and becausethe relationship between these scores is of primary interest, the stages of dataanalysis proceed somewhat differently than when a comparison between meansis the focus of the study. For purposes of illustration, assume that a researcheris interested in correlating two measures of psychological well-being obtainedfrom self-reports of college students (see Chapter 5 for a discussion of self- reportdata). Both measures are in the form of 10-point rating scales. One measure isbased on the question “How much do you worry about grades?” (1 not atall, 10 very much). The second measure is based on the question “How muchdifficulty do you experience concentrating during class exams?” (1 not at all,10 very much).Cleaning the Data Each respondent provides two scores, and both sets ofscores should be checked carefully for errors such as impossible values (e.g.,numbers outside the range of the scale), as well as outliers. A stem-and-leafdisplay may be used to examine the data in each set. When possible responsesare limited, as they typically are when scales are used, outliers are less likelyto be present than when there is no limit on a response (e.g., reporting annualincome).Conclusion Only when the investigator is assured that the data contain no errorsor values that are likely to distort the findings should the analysis proceed.Stage 2: Summarizing the Data• The major descriptive techniques for correlational data are the constructionof a scatterplot and the calculation of a correlation coefficient.• The magnitude or degree of correlation is seen in a scatterplot bydetermining how well the points correspond to a straight line; strongercorrelations more clearly resemble a straight line (linear trend) of points.• The magnitude of a correlation coefficient ranges from 1.0 (a perfectnega tive relationship) to 1.0 (a perfect positive relationship); a correlationcoef ficient of 0.0 indicates no relationship.Data summary begins by examining descriptive statistics for each set ofscores. Then the degree of relationship between these sets of scores is summarizedboth graphically and numerically.