| Measure | Use |
| Effect size | Indicates how much one variable affects another. Effect size ranges from 0 to 1: An effect size of 0.2 is called small, 0.5 moderate, and 0.8 large. |
| Significance | Indicates whether the results might have occurred by chance. A finding that chance would produce the results only 5 times in 100 is significant at the 0.05 level. A finding that chance would produce the results once in 100 times is significant at 0.01; once in 1,000 times is significant at 0.001. |
| Cost-benefit analysis | Calculates how much a particular independent variable costs versus how much it saves. This is particularly useful for analyzing public spending. For instance, one cost-benefit analysis showed that an intensive preschool program cost $15,166 per child (in 2000 dollars) but saved $215,000 (again, 2000 dollars) later on, in reduced costs of special education, unemployment, prison, and other public expenses (Belfield et al., 2006). |
| Odds ratio | Indicates how a particular variable compares to a standard, set at 1. For example, one study found that, although less than 1 percent of all child homicides occurred at school, the odds were similar for public and private schools. The odds of such deaths occurring in high schools, however, were 18.47 times that of elementary or middle schools (set at 1.0) (MMWR, January 18, 2008). |
| Factor analysis | Hundreds of variables could affect any given behavior. In addition, many variables (such as family income and parental education) may overlap. To take this into account, analysis reveals variables that can be clustered together to form a factor, which is a composite of many variables. For example, SES might become one factor, child personality another. |
| Meta-analysis | A “study of studies.” Researchers use statistical tools to synthesize the results of previous, separate studies. Then they analyze the accumulated results, using criteria that weight each study fairly. This approach improves data analysis by combining the results of studies that were too small, or too narrow, to lead to solid conclusions. |
| Who Participates? For all these measures, the characteristics of the people who participate in the study (formerly called the subjects, now called the participants) are important, as is the number of people who are studied. This also is presented with statistics. |