Plan sample size for structural equation models so that the confidence intervals for the model parameters of interest are sufficiently narrow
ss.aipe.sem.path(model, Sigma, desired.width, which.path,
conf.level = 0.95, assurance = NULL, ...)
the names of the model parameters
the index of the model parameter of interest
the necessary sample size calculated
the names of the observed variables
the population variance of the model parameter of interest at the calculated sample size
an RAM (reticular action model; e.g., McArdle & McDonald, 1984) specification of a structural equation model, and should be of class mod. The model is specified in the same manner as the sem package; see sem and specify.model for detailed documentation about model specifications in the RAM notation.
estimated population covariance matrix of the manifest variables
desired confidence interval width for the model parameter of interest
the name of the model parameter of interest, presented in double quotation marks
confidence level (i.e., 1- Type I error rate)
the assurance that the confidence interval obtained in a particular study will be no wider than desired (must be NULL or a value between 0.50 and 1)
allows one to potentially include parameter values for inner functions
Keke Lai (University of California--Merced)
This function implements the sample size planning methods proposed in Lai and Kelley (2010). It depends on the
function sem in the sem package to calculate the expected information matrix, and uses the same notation to specify SEM
models as does sem. Please refer to sem for more detailed documentations
about model specification, the RAM notation, and model fitting techniques. For technical discussion
on how to obtain the model implied covariance matrix in the RAM notation given model parameters, see McArdle and McDonald (1984).
Fox, J. (2006). Structural equation modeling with the sem package in R. Structural Equation Modeling, 13, 465--486.
Lai, K., & Kelley, K. (in press). Accuracy in parameter estimation for targeted effects in structural equation modeling: Sample size planning for narrow confidence intervals. Psychological Methods.
McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology, 37, 234--251.
sem; specify.model; theta.2.Sigma.theta; ss.aipe.sem.path.sensitiv