multilevel.omega: Multilevel Composite Reliability

In misty: Miscellaneous Functions 'T. Yanagida'

Multilevel Composite Reliability

Description

This function computes point estimate and Monte Carlo confidence interval for the multilevel composite reliability defined by Lai (2021) for a within-cluster construct, shared cluster-level construct, and configural cluster construct by calling the cfa function in the R package lavaan.

Usage

multilevel.omega(x, cluster, rescov = NULL,
                 const = c("within", "shared", "config"),
                 fix.resid = NULL, optim.method = c("nlminb", "em"),
                 missing = c("listwise", "fiml"), nrep = 100000, seed = NULL,
                 conf.level = 0.95, print = c("all", "omega", "item"),
                 digits = 2, as.na = NULL, write = NULL, check = TRUE,
                 output = TRUE)

Arguments

x	a matrix or data frame. Multilevel confirmatory factor analysis based on a measurement model with one factor at the Within level and one factor at the Between level comprising all variables in the matrix or data frame is conducted. Note that the cluster variable specified in cluster is excluded from x when specifying the argument cluster using the variable name of the cluster variable.
cluster	either a character string indicating the variable name of the cluster variable in 'x' or a vector representing the nested grouping structure (i.e., group or cluster variable).
rescov	a character vector or a list of character vectors for specifying residual covariances at the Within level, e.g. rescov = c("x1", "x2") for specifying a residual covariance between indicators x1 and x2 at the Within level or rescov = list(c("x1", "x2"), c("x3", "x4")) for specifying residual covariances between indicators x1 and x2, and indicators x3 and x4 at the Within level. Note that residual covariances at the Between level cannot be specified using this function.
const	a character string indicating the type of construct(s), i.e., "within" for within-cluster constructs, "shared" for shared cluster-level constructs, and "config" (default) for configural cluster constructs.
fix.resid	a character vector for specifying residual variances to be fixed at 0 at the Between level, e.g., fix.resid = c("x1", "x3") to fix residual variances of indicators x1 and x2 at the Between level at 0. Note that it is also possible to specify fix.resid = "all" which fixes all residual variances at the Between level at 0 in line with the strong factorial measurement invariance assumption across cluster.
optim.method	a character string indicating the optimizer, i.e., "nlminb" (default) for the unconstrained and bounds-constrained quasi-Newton method optimizer and "em" for the Expectation Maximization (EM) algorithm.
missing	a character string indicating how to deal with missing data, i.e., "listwise" for listwise deletion or "fiml" (default) for full information maximum likelihood (FIML) method.
nrep	an integer value indicating the number of Monte Carlo repetitions for computing confidence intervals.
seed	a numeric value specifying the seed of the random number generator for computing the Monte Carlo confidence interval.
conf.level	a numeric value between 0 and 1 indicating the confidence level of the interval.
print	a character vector indicating which results to show, i.e. "all" (default), for all results "omega" for omega, and "item" for item statistics.
digits	an integer value indicating the number of decimal places to be used for displaying results. Note that loglikelihood, information criteria and chi-square test statistic is printed with digits minus 1 decimal places.
as.na	a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis. Note that as.na() function is only applied to x but not to cluster.
write	a character string for writing the results into a Excel file naming a file with or without file extension '.xlsx', e.g., "Results.xlsx" or "Results".
check	logical: if TRUE, argument specification, convergence and model identification is checked.
output	logical: if TRUE, output is shown.

Value

call	function call
type	type of analysis
data	matrix or data frame specified in x
args	specification of function arguments
model	specified model
model.fit	fitted lavaan object (mod.fit)
check	results of the convergence and model identification check
result	list with result tables, i.e., omega for the coefficient omega including Monte Carlo confidence interval and itemstat for descriptive statistics

Note

The function uses the functions lavInspect, lavTech, and lavNames, provided in the R package lavaan by Yves Rosseel (2012). The internal function .internal.mvrnorm is a copy of the mvrnorm function in the package MASS by Venables and Ripley (2002).

Author(s)

Takuya Yanagida [e-mail]

References

Lai, M. H. C. (2021). Composite reliability of multilevel data: It’s about observed scores and construct meanings. Psychological Methods, 26(1), 90–102. https://doi.org/10.1037/met0000287

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48, 1-36. https://doi.org/10.18637/jss.v048.i02

Venables, W. N., Ripley, B. D. (2002).Modern Applied Statistics with S (4th ed.). Springer. https://www.stats.ox.ac.uk/pub/MASS4/.

See Also

item.omega, multilevel.cfa, multilevel.fit, multilevel.invar, multilevel.cor, multilevel.descript

Examples

## Not run: 
# Load data set "Demo.twolevel" in the lavaan package
data("Demo.twolevel", package = "lavaan")

#---------------------------
# Cluster variable specification

# Cluster variable 'cluster' in 'x'
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4", "cluster")],
                 cluster = "cluster")

# Cluster variable 'cluster' not in 'x'
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster)

#---------------------------
# Type of construct

# Within-Cluster Construct
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, const = "within")

# Shared Cluster-Level Construct
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, const = "shared")

# Configural Construct
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, const = "config")

#---------------------------
# Residual covariance at the Within level and residual variance at the Between level

# Residual covariance between "y4" and "y5" at the Within level
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, const = "config",
                 rescov = c("y3", "y4"))

# Residual variances of 'y1' at the Between level fixed at 0
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, const = "config",
                 fix.resid = c("y1", "y2"), digits = 3)

#---------------------------
# Write results

# Assign results into an object and write results into an Excel file
mod <- multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                        cluster = Demo.twolevel$cluster, output = FALSE)

# Write results into an Excel file
write.result(mod, "Multilevel_Omega.xlsx")

# Write results into an Excel file
multilevel.omega(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, write = "Multilevel_Omega.xlsx")

## End(Not run)

misty documentation built on Nov. 15, 2023, 1:06 a.m.