Supplemental Material for Solving Variables With Monte Carlo Simulation Experiments: A Stochastic Root-Solving Approach

A Stochastic Root-Solving Approach".This supplement contains SimDesign code (Chalmers & Adkins, 2020) relevant for solving sample sizes, condence interval widths, and expected power using the SimSolve() function. Strategy for Deciding on Sample Size in Structural Equation ModelsCode associated with the subsection Strategy for Deciding on Sample Size in Structural Equation Models for a conrmatory factor analysis model with two latent variables, where interest is in the latent correlation parameter .

Publication Details

Journal
Psychological Methods
Published
2024-09-05
DOI
https://doi.org/10.1037/met0000689.supp
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
0.00
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article

Supplemental Material for Solving Variables With Monte Carlo Simulation Experiments: A Stochastic Root-Solving Approach

Psychological Methods
Probabilistic and Robust Engineering Design
article

Supplemental Material for Solving Variables With Monte Carlo Simulation Experiments: A Stochastic Root-Solving Approach

article en

Abstract

A Stochastic Root-Solving Approach".This supplement contains SimDesign code (Chalmers & Adkins, 2020) relevant for solving sample sizes, condence interval widths, and expected power using the SimSolve() function. Strategy for Deciding on Sample Size in Structural Equation ModelsCode associated with the subsection Strategy for Deciding on Sample Size in Structural Equation Models for a conrmatory factor analysis model with two latent variables, where interest is in the latent correlation parameter .

Psychological Methods
Openalex Percentile: Top 85%
Probabilistic and Robust Engineering Design
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Supplemental Material for Solving Variables With Monte Carlo Simulation Experiments: A Stochastic Root-Solving Approach · Psychological Methods (2024) | TGRS Research Map | TGRS