Answer

d—Correlation measures the degree to which two variables are linearly related.

A scatter plot would display graphically the association between two continuous variables. The x axis displays values for one variable, and the y axis values for the other. Each point represents one paired measurement of each variable. This is a useful way to display the data and would show U shaped and other non-linear associations as well as linear associations.

However, a scatter plot does not measure an association. To put a number to it requires an assessment of correlation. Pearson’s correlation coefficient or Spearman’s rank correlation coefficient may be used to assess linear correlation. The correlation coefficient may take any value between 1 and –1: 0 represents no linear association, 1 represents a perfect straight line with y values increasing with increasing x values, –1 represents a perfect straight line with y values decreasing with increasing x values. Like other parametric techniques, Pearson’s correlation coefficient is sensitive to outlying values and may generate misleading P values when the y axis values are not normally distributed. Spearman’s rank correlation coefficient, like other non-parametric techniques, is less sensitive and more robust.

Odds ratios can be used to compare only dichotomous values. Odds express the probability of something happening divided by the probability of it not happening.

A bar chart is used to plot the frequency of a nominal variable (such as eye colour) or an ordinal variable (such as agreement with a survey question: "disagree strongly, disagree, agree, agree strongly").

Cite this as: BMJ 2008;337:a197