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Strengthening Benefit-Cost Analysis for Early Childhood Interventions: Workshop Summary
drawing as much information as possible from multiple studies, particularly when the interventions are similar and the samples represented are diverse across the studies. The most important feature of a meta-analysis is representation of the effects on a certain outcome in terms of a standardized effect size that can be compared across studies. Analysis then focuses on the distribution of effect sizes—the central tendency of that distribution and also the variation around that mean. The key question is the extent to which that variation is associated with or can be explained by moderator variables, such as differences in setting, subject characteristics, and so on. So, in essence, meta-analysis is the empirical study of the generalizability of intervention effects.
A few issues make this analysis challenging. First, the question of what constitutes the “same” intervention is complicated. Few interventions are crisply and unambiguously defined, and the developers of an intervention may modify it as they learn from experience. At what point are the modifications sufficient to produce a different intervention? In general, meta-analyses are designed to focus on a type of intervention defined generically, rather than in terms of a specific intervention protocol. However, there are no formal typologies to which researchers could turn for grouping similar interventions in areas like early childhood programs. Because there is no “periodic table of the elements for social interventions,” Lipsey pointed out, classification is a judgment call, and not all analysts will make it in the same way.
A related problem is that, even with any reasonably concise definition of a particular intervention, variability abounds. A statistical test used in meta-analysis, the Q test, is a tool for answering the question of whether or not the between-study variation on the effect sizes for a given outcome is greater than one would expect from the within-study sampling error. Lipsey explained that “it’s not unusual to find three, four, five, six, eight, even ten times as much variability across studies [of social interventions] as one would expect just from the sampling error within.” This degree of variability—far greater than what is typical in medical studies, for example—is inconsistent with a conclusion that the effects can be generalized. Figure 6-1 illustrates the major sources of variance in studies of social interventions, using the results of an analysis of meta-analyses of psychological, education, and behavioral interventions (Wilson and Lipsey, 2001). The numbers reflect the rough proportions of different sources of variation. Lipsey highlighted how much of the variability is associated with aspects of the methodology—almost as much as is associated with the characteristics of the interventions themselves.
In other words, he noted, “effect size distributions are being driven almost as much by the input of the researchers as they are by the phenomenon that researchers are studying.” And this variability may obscure