semPower: General power analysis for structural equation models.

Area of Science:

• Social and Behavioral Sciences
• Psychometrics
• Statistical Modeling

Background:

• Structural Equation Modeling (SEM) is widely used in social and behavioral sciences for hypothesis testing.
• Adequate statistical power, dependent on sample size, is crucial for detecting hypothesized effects.
• Current SEM applications often neglect statistical power in sample size determination due to difficulties in effect size estimation and software limitations.

Purpose of the Study:

• To present semPower 2, an enhanced R package for comprehensive statistical power analyses in SEM.
• To address the challenges of integrating power analysis into SEM research practices.
• To provide user-friendly tools for researchers to conduct various types of power analyses.

Main Methods:

• The study introduces the semPower 2 R package, offering advanced power analysis functionalities.
• It supports both analytical and simulation-based approaches for a priori, post hoc, and compromise power analyses.
• The package accommodates SEM with and without latent variables, multigroup settings, and common model types like CFA and cross-lagged panel models.

Main Results:

• semPower 2 provides comprehensive tools for diverse power analyses in SEM.
• The package simplifies the process of defining effect sizes within SEM frameworks.
• It offers convenience functions for common SEM model types, enhancing usability.

Conclusions:

• semPower 2 facilitates robust statistical power analysis in SEM research.
• The package aims to improve the rigor of hypothesis testing by integrating power considerations.
• It empowers researchers to make informed decisions regarding sample size and statistical power in their studies.