What we read in empirial studies are usually results about correlations which are statistically significant or not. What we really want to know is if there is a causal relation and how large is this effect (the effect size). These are very different questions.
A very common failure in sexual abuse research is not to consider some important variables which, on one side, are correlated with abuse, and, on the other side, can cause harm, such as physical and psychological abuse, neglect, broken homes.
Therefore, some explanation:
One of the most fundamental principles in scientific methodology is that correlation is not causation. That is, for example, just because race is correlated with IQ, that does not mean that race causes differences in IQ. It could be that some third variable, such as home environment or socioeconomic status, is responsible for the race-IQ association.
To illustrate this concept, let’s take this simplistic example [] As you go from small towns to small cities, to large cities, the number of churches will increase. Further, as you go from small towns to big cities, the amount of crime also increases. Does this mean that building new churches will increase crime, or tearing some down will decrease crime? No, because there’s a third variable, population, that is responsible for both. As population grows, more churches are built and more crimes occur. If we factored out population size in this example, the correlation between number of churches and amount of crime would probably disappear.
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In talking about causality, we should first review some basic methodology. In the U.S., Whites score on average 15 IQ points higher than Blacks. Can you then conclude that race causes IQ differences? If you did, you would be called a racist, and justifiably so. Blacks and Whites differ not only in their race, but in their socioeconomic status, as well as other important factors. It could well be that coming from a poorer environment produces this IQ difference, rather than race. Home environment does have a big impact on intellectual development, so it may play the role of a third variable that completely accounts for the association of the two main variables - race and IQ.
Incidentally, a 15 point IQ difference between the races can be expressed in this way: race accounts for 34% of the variability in IQ scores among Whites and Blacks. In our national samples, CSA accounted for only 1% of the adjustment variation for females and only one half of one percent for males. By comparison, race was 34 to 68 times stronger in accounting for IQ variation. Thus, if we can argue that the race difference in IQ is caused, not by race, but by a poorer home environment, then surely we could consider making this argument for CSA: that the small differences in adjustment that were found may have been attributable to differences in home environment. This is a reasonable possibility. Children in broken homes are less supervised and are more prone, and willing, to engage in counternormative behavior, such as using drugs, skipping school, or engaging in taboo sex (such as sex with adults). In this scheme, the poor home environment not only predisposes them to CSA, but also predisposes them toward becoming less well adjusted. This scenario suggests that the relation that we found between CSA and adjustment could be spurious (that is, false), or, if causal, even weaker than it was.