Pearsonís r Correlation (modified from Instructorís Resource Guide for the Text)

The concept of correlation is first introduced in Chapter Three. When working with the regional data files (GLOBAL, AFRICA, ASIA, EUROPE, LATIN, and NAF-SAS), the following guidelines for interpreting positive or negative correlations (Pearsonís r) may be helpful. These are only crude estimates for interpreting strengths of correlations:

If r = +.70 or higher Very strong positive relationship
+.40 to +.69 Strong positive relationship
+.30 to +.39 Moderate positive relationship
+.20 to +.29 weak positive relationship
+.01 to +.19 No or negligible relationship
-.01 to -.19 No or negligible relationship
-.20 to -.29 weak negative relationship
-.30 to -.39 Moderate negative relationship
-.40 to -.69 Strong negative relationship
-.70 or higher Very strong negative relationship

Cramerís V Correlation (modified from Instructorís Resource Guide for the Text)

The concept of correlations using survey data is first introduced in Chapter Six. When working with survey data files (any file beginning with the prefix WVS), you can use the following guidelines for interpreting Cramerís V correlations. Again, these are only crude estimates for interpreting strengths of correlations:

If Cramerís V = .25 or higher Very strong relationship
.15 to .25 Strong relationship
.11 to .15 Moderate relationship
.06 to .10 weak relationship
.01 to .05 No or negligible relationship

Significance Ė What it means

A statistically significant finding is one that is determined (statistically) to be very unlikely to happen by chance. Statisticians are able to calculate the likelihood that any observed relationship between two variables (as indicated by any number of cases) could have happened by chance (or random variation). If it is calculated that there is less than a one in twenty chance (.05 or 5%) that the observed relationship could have happened by chance, the findings are designated as significant. If there is less than a one in one hundred chance (.01 or 1%), they are designated as highly significance. Significance is influenced by the number of cases in your sample, and the observed range (difference) of the sample. Simply put, youíre more likely to be sure the differences you observe from a sample are accurate for the whole population if there are many cases and large comparative differences in the observed relationship between a specific set of variables. This text indicates significance by placing one or two asterisks (*) after the Pearsonís r, or Cramerís V. In cross-tabulation, using the "Statistics" view, you can see Cramer's V on the second line, and the Prob.= on the first line. If Prob.= is less than 0.05, your data is significant. See pgs 91-93 in the text for further explanation.