• Choose a category
• All
• Classic-Fiction

By Root 541 0

for example, Frank Sulloway showed—contrary to what historians have believed for over a century—that proponents and opponents in the Darwinian Revolution, as well as the Copernican Revolution, the Protestant Reformation, the French Revolution, the civil rights movement, and many others, were not divided by social class. Whether someone was upper class, middle class, or lower class had little to no influence on his or her thinking. But anecdotes and narrative writing—the tools of the historian—will not reveal this because there is no mechanism to tell you if you are not simply seeking and finding anecdotes and quotes to support your hypothesis. The only way to know what is really going on is to conduct a formal test to find out which variables are significant and which are not.

One of the most common statistics used by social scientists is the correlation coefficient, represented by r, which has a range from .00 to ±1.0—from no relationship to a perfect relationship. The relationship of height and weight, for example, shows a high correlation whereas, say, height and I.Q. shows a low correlation. A negative correlation, signified by a minus sign in front of the value of r, represents associated values in opposite directions, as in golf skill and golf scores—as the first goes up, the second goes down. In the social sciences most correlations fall in the .00 to .50 range. The correlation of religious interests between identical twins raised apart, for example, is r = .49, a very significant figure that indicates a strong genetic component to religiosity—fully half of the variation between people on their religious interests can be accounted for by their genes. [Note: Normally psychologists square the r to obtain the percentage of variance explained by genetic factors (e.g., an r of .71 generates an r2 of .50, or 50 percent of the variance accounted for), but in the case with twins this is not necessary because, as Arthur Jensen explained: “Most psychologists have learned to treat correlations as the square root of variance explained. But it is incorrect to take the square of twins or other kinship correlations to determine the proportion of variance attributable to genetic or environmental effects. The unsquared correlation itself is correctly interpreted as a proportion.”]

The study reported in Chapter 4 was conducted by MIT social scientist Frank Sulloway and me. We employed a multiple regression analysis of our religiosity data, which is a statistical tool employed when more than one variable is a significant predictor of another. For example, we found that education negatively correlates with religiosity—as education goes up, religiosity goes down. But we also discovered that age and parental conflict are associated with a decrease in religiosity, while gender, parents’ religiosity, and being raised to be religious, are all associated with an increase in religiosity. Since there are multiple causes of religiosity, we are obliged to conduct a multiple regression analysis to tease apart how these numerous variables operate separately and together.

The p value represents the probability that a given correlation could result by chance. A correlation is considered statistically significant if there is only a probability of 1 in 20 (or p < .05), of its being due to chance. A p value of .01 means that there is only 1 chance in 100 that the correlation happened by chance. A p of .001 is 1 in 1,000, and a p of .0001 means the likelihood of the correlation being due to chance is 1 in 10,000. Most of the correlations we found in our study were significant at the p < .0001 level or better. We report our sample size represented by N. Although our total sample size was 2,707, the N will vary from statistic to statistic, since not everyone answered every question on the survey. In general, our sample size was larger than most encountered in social science research (many of which rely on introductory psychology courses for their subject pools), especially in religious surveys. For example, Kenneth Pargament, in his comprehensive 1997