A Priori Monte Carlo Simulation for Sample Size Planning for RMSEA in SEM

Conduct a priori Monte Carlo simulation to empirically study the effects of (mis)specifications of input information on the calculated sample size. The sample size is planned so that the expected width of a confidence interval for the population RMSEA is no larger than desired. Random data are generated from the true covariance matrix but fit to the proposed model, whereas the sample size is calculated based on the input covariance matrix and proposed model.

Usage

ss_aipe_rmsea_sensitivity(
  width,
  model,
  Sigma,
  N = NULL,
  conf_level = 0.95,
  G = 200,
  save = FALSE,
  filename = "ss_aipe_rmsea_sensitivity_result.csv",
  ...
)

Arguments

width: desired confidence interval width for the population RMSEA.
model: the model the researcher proposes, which may or may not be the true model, written in lavaan model syntax (see model.syntax). The observed variable names in the model must match the row and column names of Sigma.
Sigma: the true population covariance matrix, which is used to generate random data for the simulation study. The row and column names of Sigma must match the observed variables in model.
N: if N is specified, random samples of the specified size are generated. Otherwise the sample size is calculated with the sample size planning method so that the expected width of a confidence interval for the population RMSEA is no larger than width.
conf_level: confidence level (i.e., 1 - the Type I error rate).
G: number of replications in the Monte Carlo simulation.
save: option to save simulation results. With save = TRUE the per-replication results are written to filename.
filename: the name of the file that simulation results are saved to.
...: additional arguments passed to sem when fitting the model (for example estimator or missing).

Value

A data.frame with columns term and value summarizing the a priori Monte Carlo study. The term entries are: "mean_rmsea", "median_rmsea", "sd_rmsea" (summaries of the realized RMSEA estimates across the converged replications); "mean_ci_width", "median_ci_width", "sd_ci_width" (summaries of the realized interval widths); "pct_ci_less_w" (proportion of intervals narrower than the target width); "pct_ci_miss_low" and "pct_ci_miss_high" (tail-specific empirical non-coverage of the population RMSEA); "total_type_I_error" (overall empirical non-coverage, the sum of the two tails); and the echoes "suc_rep" (number of converged replications), "total_N" (the N evaluated), "df" (model degrees of freedom), "true_rmsea" (the population RMSEA recovered from fitting model to Sigma), "width", and "conf_level". The proportion rows are on the 0 to 1 scale, not percentages.

Details

This function implements the sample size planning method proposed in Kelley and Lai (2011). It uses sem to fit the proposed model to the population covariance matrix, which recovers the population RMSEA (the model misspecification) and the model degrees of freedom, and to fit the model to each simulated sample, and it uses ci_rmsea to construct the confidence interval for the population RMSEA in each replication. The model is specified in lavaan syntax, so lavaan must be installed.

Earlier versions of this function used the sem package to fit the model. The fit is now carried out with lavaan, the structural equation modeling backend used throughout DMAR. The population RMSEA is read from lavaan::fitMeasures(), which is computed reliably for the large-sample population fit.

Note

Replications in which lavaan fails to converge, or for which the RMSEA is undefined, are skipped; the number of converged replications is reported as suc_rep. Increase G if many replications fail to converge.

References

Cudeck, R., & Browne, M. W. (1992). Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value. Psychometrika, 57, 357–369. doi:10.1007/BF02295424

Kelley, K., & Lai, K. (2011). Accuracy in parameter estimation for the root mean square error of approximation: Sample size planning for narrow confidence intervals. Multivariate Behavioral Research, 46, 1–32. doi:10.1080/00273171.2011.543027

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. doi:10.18637/jss.v048.i02

Author

Ken Kelley kkelley@nd.edu

Examples

set.seed(113)

# True data generating model: two correlated factors (r = 0.5), three
# standardized indicators each (loadings 0.7). Build the implied population
# covariance matrix Sigma = Lambda Phi Lambda' + Psi.
Lambda <- matrix(0, 6, 2)
Lambda[1:3, 1] <- 0.7
Lambda[4:6, 2] <- 0.7
Phi   <- matrix(c(1, 0.5, 0.5, 1), 2, 2)
Sigma <- Lambda %*% Phi %*% t(Lambda) + diag(1 - 0.7^2, 6)
dimnames(Sigma) <- list(paste0("x", 1:6), paste0("x", 1:6))

# Proposed (misspecified) model: a single common factor.
proposed <- "g =~ x1 + x2 + x3 + x4 + x5 + x6"

# The simulation itself is not run at example time: it fits the proposed
# model once at a very large N to recover the population RMSEA, then
# generates and fits a fresh sample on every replication. The G below is
# already far smaller than a study one would report; the default of 200,
# or more, is the realistic setting. The call is:
# ss_aipe_rmsea_sensitivity(width = 0.05, model = proposed, Sigma = Sigma,
#                           G = 25)