A comparison of several approaches for controlling measurement error in small samples.

Abstract

It is well known that methods that fail to account for measurement error in observed variables, such as regression and path analysis (PA), can result in poor estimates and incorrect inference. On the other hand, methods that fully account for measurement error, such as structural equation modeling with latent variables and multiple indicators, can produce highly variable estimates in small samples. This article advocates a family of intermediate models for small samples (N < 200), referred to as single indicator (SI) models. In these models, each latent variable has a single composite indicator, with its reliability fixed to a plausible value. A simulation study compared three versions of the SI method with PA and with a multiple-indicator structural equation model (SEM) in small samples (N = 30 to 200). Two of the SI models fixed the reliability of each construct to a value chosen a priori (either .7 or .8). The third SI model (referred to as "SIα") estimated the reliability of each construct from the data via coefficient alpha. The results showed that PA and fixed-reliability SI methods that overestimated reliability slightly resulted in the most accurate estimates as well as in the highest power. Fixed-reliability SI methods also maintained good coverage and Type I error rates. The SIα and SEM methods had intermediate performance. In small samples, use of a fixed-reliability SI method is recommended. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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