Compromise Power AnalysesBoth α and 1-β are computed as functions of
- the effect size,
- N, and
- an error probability ratio q = β/α.
Compromise power analyses can be useful both before and after data collection. For example, an a priori power analysis might result in a sample size that exceeds the available resources. In such a situation, a researcher could specify the maximum affordable sample size and, using a compromise power analysis, compute α and (1-β) associated with, say, q = β/α = 4.
Alternatively, if a study has already been conducted but has not yet been analyzed, a researcher could ask for a reasonable decision criterion that guarantees perfectly balanced error risks (i.e. α = β), given the size of this sample and a critical effect size she is interested in.
Of course, compromise power analyses can easily result in unconventional significance levels larger than α=.05 (in case of small samples or effect sizes) or less than α=.001 (in case of large samples or effect sizes). However, we believe that the benefit of balanced type-1 and type-2 error risks often offsets the costs of violating significance level conventions.
Dienstag, 21. 05. 2013
Questions about this website? Contact
Letzte Änderung: 12.05.2009, 15:52