Efficient Designs for Judgment Analysis

In formulating a public policy, judgment analysis enablespolicy makers to assess and accommodate the relative importance ofdiffering viewpoints and concerns of competing actors as ananalytic decision aid. Among the important constraints that preventjudgment analysis from being widely applied to the policyformulation process is a methodological limitation inherent injudgment analysis: It requires too many scenarios to be judged in asingle judgment task. Addressing this issue, this dissertationimplemented two efficient design concepts - efficient plausibledesign and augmented representative design - suggested byMcClelland (1999) as alternative design frameworks for judgmentanalysis that balance two conflicting principles: the principles ofrepresentative design and statistical efficiency. It also sought toderive a generalizable rule about the minimum number of casesneeded to arrive at reliable conclusions about a judgment policygiven the judgment task. Additionally, it tested the applicabilityof the bootstrap analysis as an alternative method to estimate thestability of the coefficients modeled for a judgment policy giventhe limited number of observations.