Measurement error issues in substance abuse services research: Lessons from structural equation modeling and psychometric theory

Abstract

Evaluating the effectiveness of treatment for long-term chronic conditions requires more sensitive measures of quality of life and daily functioning than those treatments for acute care. Unfortunately, most of these measures require some degree of subjective reports and are affected by a number of systematic and unsystematic measurement errors that typically increase the probability of incorrectly declaring the treatment ineffective or indicative of a Type II error. These include method effects, such as social desirability or acquiescence, and structural errors that occur when the items reliably measure something other than what they were intended to measure. This paper explores some potential methods for removing or otherwise controlling random measurement error, assessment artifacts, irrelevant variation in outcome measures, and confounding sources of covariation in a structural equations paradigm. Using examples with some popular measures of quality of life and functioning, the paper also looks at the appropriateness of their use in observational, quasi-, and randomized field experiments.