Conduct a priori Monte Carlo simulation to empirically study the effects of (mis)specifications of input information on the calculated sample size. Random data are generated from the true covariance matrix but fit to the proposed model, whereas sample size is calculated based on the input covariance matrix and proposed model.
ss.aipe.sem.path.sensitiv(model, est.Sigma, true.Sigma = est.Sigma,
which.path, desired.width, N=NULL, conf.level = 0.95, assurance = NULL,
G = 100, ...)
the G random confidence interval widths
the sample size calculated
name of the model parameter of interest
desired confidence interval width
mean of the G random confidence interval widths
median of the G random confidence interval widths
99, 95, 90, 85, 80, 75, 70, and 60 percentiles of the G random confidence interval widths
the proportion of confidence interval widths narrower than desired
the upper empirical Type I error rate
the lower empirical Type I error rate
total empirical Type I error rate
confidence level
successful replications
the model the researcher proposes, may or may not be the true model. This argument should be 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 does the sem package; see sem and specify.model for detailed documentation about model specifications in the RAM notation.
the covariance matrix used to calculate sample size, may or may not be the true covariance matrix. The row names and column names of est.Sigma should be the same as the manifest variables in est.model.
the true population covariance matrix, which will be used to generate random data for the simulation study. The row names and column names of est.Sigma should be the same as the manifest variables in est.model.
the name of the model parameter of interest, and must be in a double quote
desired confidence interval width for the model parameter of interest
the sample size of random data. If it is NULL, it will be determined by the sample size planning method
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)
number of replications in the Monte Carlo simulation
allows one to potentially include parameter values for inner functions
Keke Lai (University of California -- Merced) and Ken Kelley kkelley@nd.edu
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 documentation
about model specifications, 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