Statistical power

Engineering

Philosophy

Aerospace engineering

Statistics

Chromatography

Range (aeronautics)

Computer science

A priori and a posteriori

Structural equation modeling

PsycINFO

Psychology

Mathematics

Statistical hypothesis testing

Political science

Epistemology

Econometrics

Sample size determination

MEDLINE

Chemistry

Sample (material)

Statistical model

Psychological Methods

Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable-and thus more replicable-results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A tutorial on assessing statistical power and determining sample size for structural equation models.