摘要：The purpose of this article is to help researchers avoid common pitfalls associated with reliability including incorrectly assuming that (a) measurement error always attenuates observed score correlations, (b) different sources of measurement error are not cumulative, and (c) reliability is a function of instrumentation. To accomplish our purpose, we first describe what reliability is and why researchers should care about it with focus on its impact on effect sizes. Second, we review how reliability is assessed with comment on the deleterious consequences of cumulative measurement error. Third, we consider how researchers can use reliability generalization as a prescriptive method when designing their research studies to form hypotheses about whether or not reliability estimates will be acceptable given their sample and testing conditions. Finally, we discuss options that researchers may consider when faced with analyzing unreliable data.
