SemPower
General power analysis for structural equation models
Bibliographic Data
| ID | 23315902 |
|---|---|
| Authors | Morten Moshagen (0000-0002-2929-7288, Universität Ulm, corresponding author), Martina Bader (0000-0002-5706-8933, Universität Ulm) |
| Year | 2023 |
| Volume | 56 |
| Issue | 4 |
| Pages | 2901-2922 |
| Publication date | 2023-11-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Behavior Research Methods (JOURNAL) |
| Journal identifiers | ISSN: 1554-351X • E-ISSN: 1554-3528 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.3758/s13428-023-02254-7 |
| PMID | 37950114 |
| OpenAlex | W4388558341 |
| Language | EN |
| Citations received | 92 |
| References cited | 41 |
Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired.
A priori and a posteriori · Algorithm · Autoregressive model · Confirmatory factor analysis · Data mining · Econometrics · Latent variable · Machine learning · Power (physics) · Power analysis · Process (computing) · Regression analysis · Sample (material) · Sample size determination · Statistical hypothesis testing · Statistical model · Statistical power · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Artificial Intelligence · Behavioral Health and Interventions · Computer Science · Mathematics · Psychometric Methodologies and Testing
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Correlations redux
| Unique citing works | 92 |
|---|---|
| Citations per year | 23 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 78 |