Читать книгу Economic Evaluation in Education - Henry M. Levin - Страница 17

CU analysis is a close cousin of CE analysis. It refers to the evaluation of alternatives according to a comparison of their costs and their utility (a term that is often interpreted as value or satisfaction to an individual or group). Unlike CE analysis, which relies upon a single measure of effectiveness (e.g., a test score, the number of dropouts averted), CU analysis uses information on a range of outcomes to assess overall satisfaction. These outcomes are then weighted based on the decisionmaker’s preferences—that is, how much each outcome contributes to total utility. Data on preferences can be derived in many ways, either through highly subjective estimates by the researcher or through more rigorous methods designed to carefully elicit opinions as to the value of each outcome. Once overall measures of utility have been obtained, however, we proceed in the same way as CE analysis. We choose the interventions that provide a given level of utility at the lowest cost or those that provide the greatest amount of utility for a given cost. This CU analysis is like CE analysis except the outcome is weighted based on stakeholders’ perceptions or preferences.

We can apply CU analysis to a simple example of alternative reading programs that have outcomes that are not valued equally by the decisionmaker. One reading intervention raises test scores by 0.6 standard deviations, and another reading intervention raises test scores by 0.4 standard deviations. With a statewide achievement policy that a test score gain of 0.2 meets accountability standards for the school, then, although incremental test score gains are desirable, they are not valued in the same way as gains up to an effect size of 0.2. So, if we assume gains beyond 0.2 are worth half as much as gains below 0.2, then the utility of intervention one is 0.4 (= 0.2 + (0.6 – 0.2)/2) and of intervention two is 0.3 (= 0.2 + (0.4 – 0.2)/2). Now, with respective costs of $400 and $200, the second intervention is preferred from a CU basis: The first intervention is twice as costly but only one-third more valuable. Of course, we could imagine utility weights that would overturn this conclusion.

CU analysis is in one sense an extension of CE analysis. That is, CU analysis requires that the preferences of the decisionmakers be explicitly incorporated into the research. The classic example of a utility-based measure is the quality-adjusted life year (QALY) used by health sciences researchers (Drummond et al., 2009; Neumann, Thorat, Shi, Saret, & Cohen, 2015). Unfortunately, the challenge of CU analysis is that of finding valid ways to determine the values of outcomes in order to weight these preferences relative to costs. This quest requires separate modeling exercises often of substantial complexity. The simple reading example in the previous paragraph was made easier because there was only one outcome—test scores; when there are multiple outcomes that need to be combined, the utility calculations become more difficult and subjective.