Simulation-based design optimization for statistical power:
                        Utilizing machine learning.

Zimmer, Felix; Debelak, Rudolf

doi:10.1037/met0000611

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling: A Comparison of Various Estimation Approaches

Bogaert,

Loh,

Schuberth

et al. 2024

Structural Equation Modeling: A Multidisciplinary Journal

View full text Add to dashboard Cite

The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling: A Comparison of Various Estimation Approaches

Bogaert,

Loh,

Schuberth

et al. 2024

Structural Equation Modeling: A Multidisciplinary Journal

View full text Add to dashboard Cite

mlpwr: A Power Analysis Toolbox to Find Cost-Efficient Study Designs

Zimmer,

Debelak

2022

CRAN: Contributed Packages

View full text Add to dashboard Cite

Sample size planning for complex study designs: A tutorial for the mlpwr package

Zimmer,

Henninger,

Debelak

2023

Behav Res

View full text Add to dashboard Cite

A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling.

show abstract

Simulation-based design optimization for statistical power: Utilizing machine learning.

Cited by 3 publications

References 48 publications

The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling: A Comparison of Various Estimation Approaches

The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling: A Comparison of Various Estimation Approaches

mlpwr: A Power Analysis Toolbox to Find Cost-Efficient Study Designs

Sample size planning for complex study designs: A tutorial for the mlpwr package

Contact Info

Product

Resources

About