multilevel.cfa: Multilevel Confirmatory Factor Analysis

In misty: Miscellaneous Functions 'T. Yanagida'

Multilevel Confirmatory Factor Analysis

Description

This function is a wrapper function for conducting multilevel confirmatory factor analysis to investigate four types of constructs, i.e., within-cluster constructs, shared cluster-level constructs, configural cluster constructs, and simultaneous shared and configural cluster constructs by calling the cfa function in the R package lavaan.

Usage

multilevel.cfa(..., data = NULL, cluster, model = NULL, rescov = NULL,
               model.w = NULL, model.b = NULL, rescov.w = NULL, rescov.b = NULL,
               const = c("within", "shared", "config", "shareconf"),
               fix.resid = NULL, ident = c("marker", "var", "effect"),
               ls.fit = FALSE, estimator = c("ML", "MLR"),
               optim.method = c("nlminb", "em"), missing = c("listwise", "fiml"),
               print = c("all", "summary", "coverage", "descript", "fit", "est",
                         "modind", "resid"),
               mod.minval = 6.63, resid.minval = 0.1, digits = 3, p.digits = 3,
               as.na = NULL, write = NULL, append = TRUE, check = TRUE, output = TRUE)

Arguments

...	a matrix or data frame. If model, model.w, and model.b are NULL, multilevel confirmatory factor analysis based on a measurement model with one factor labeled wf at the Within level and one factor labeled bf 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 ... when specifying the argument cluster using the variable name of the cluster variable. If model or mode.w and model.b is specified, the matrix or data frame needs to contain all variables used in the model argument(s). Alternatively, an expression indicating the variable names in data. Note that the operators ., +, -, ~, :, ::, and ! can also be used to select variables, see 'Details' in the df.subset function.
data	a data frame when specifying one or more variables in the argument .... Note that the argument is NULL when specifying a matrix or data frame for the argument ....
cluster	either a character string indicating the variable name of the cluster variable in ... or data, or a vector representing the nested grouping structure (i.e., group or cluster variable).
model	a character vector for specifying the same factor structure with one factor at the Within and Between Level, or a list of character vectors for specifying the same measurement model with more than one factor at the Within and Between Level, e.g.,model = c("x1", "x2", "x3", "x4") for specifying a measurement model with one factor labeled wf at the Within level and a measurement model with one factor labeled bf at the Between level each comprising four indicators, or model = list(factor1 = c("x1", "x2", "x3", "x4"), factor2 = c("x5", "x6", "x7", "x8")) for specifying a measurement model with two latent factors labeled wfactor1 and wfactor2 at the Within level and a measurement model with two latent factors labeled bfactor1 and bfactor2 at the Between level each comprising four indicators. Note that the name of each list element is used to label factors, where prefixes w and b are added the labels to distinguish factor labels at the Within and Between level, i.e., all list elements need to be named, otherwise factors are labeled with "wf1", "wf2", "wf3" for labels at the Within level and "bf1", "bf2", "bf3" for labels at the Between level and so on.
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 can only be specified by using the arguments model.w, model.b, and model.b.
model.w	a character vector specifying a measurement model with one factor at the Within level, or a list of character vectors for specifying a measurement model with more than one factor at the Within level.
model.b	a character vector specifying a measurement model with one factor at the Between level, or a list of character vectors for specifying a measurement model with more than one factor at the Between level.
rescov.w	a character vector or a list of character vectors for specifying residual covariances at the Within level.
rescov.b	a character vector or a list of character vectors for specifying residual covariances at the Between level.
const	a character string indicating the type of construct(s), i.e., "within" for within-cluster constructs, "shared" for shared cluster-level constructs, "config" (default) for configural cluster constructs, and "shareconf" for simultaneous shared and 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.
ident	a character string indicating the method used for identifying and scaling latent variables, i.e., "marker" for the marker variable method fixing the first factor loading of each latent variable to 1, "var" for the fixed variance method fixing the variance of each latent variable to 1, or "effect" for the effects-coding method using equality constraints so that the average of the factor loading for each latent variable equals 1.
ls.fit	logical: if TRUE (default) level-specific fit indices are computed when specifying a model using the arguments model.w and model.b given the model does not contain any cross-level equality constraints.
estimator	a character string indicating the estimator to be used: "ML" for maximum likelihood with conventional standard errors and "MLR" (default) for maximum likelihood with Huber-White robust standard errors and a scaled test statistic that is asymptotically equal to the Yuan-Bentler test statistic. Note that by default, full information maximum likelihood (FIML) method is used to deal with missing data when using "ML" (missing = "fiml"), whereas incomplete cases are removed listwise (i.e., missing = "listwise") when using "MLR".
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" (default) for listwise deletion or "fiml" for full information maximum likelihood (FIML) method. Note that FIML method is only available when estimator = "ML", that it takes longer to estimate the model using FIML, and that FIML is prone to convergence issues which might be resolved by switching to listwise deletion.
print	a character string or character vector indicating which results to show on the console, i.e. "all" for all results, "summary" for a summary of the specification of the estimation method and missing data handling in lavaan, "coverage" for the variance-covariance coverage of the data, "descript" for descriptive statistics, "fit" for model fit, "est" for parameter estimates, and "modind" for modification indices. By default, a summary of the specification, descriptive statistics, model fit, and parameter estimates are printed.
mod.minval	numeric value to filter modification indices and only show modifications with a modification index value equal or higher than this minimum value. By default, modification indices equal or higher 6.63 are printed. Note that a modification index value of 6.63 is equivalent to a significance level of \alpha = .01.
resid.minval	numeric value indicating the minimum absolute residual correlation coefficients and standardized means to highlight in boldface. By default, absolute residual correlation coefficients and standardized means equal or higher 0.1 are highlighted. Note that highlighting can be disabled by setting the minimum value to 1.
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.
p.digits	an integer value indicating the number of decimal places to be used for displaying the p-value.
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 naming a file for writing the output into either a text file with file extension ".txt" (e.g., "Output.txt") or Excel file with file extension ".xlsx" (e.g., "Output.xlsx"). If the file name does not contain any file extension, an Excel file will be written.
append	logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.
check	logical: if TRUE (default), argument specification, convergence and model identification is checked.
output	logical: if TRUE (default), output is shown.

Value

Returns an object of class misty.object, which is a list with following entries:

call	function call
type	type of analysis
data	data frame used for the current analysis
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., summary for the summary of the specification of the estimation method and missing data handling in lavaan, coverage for the variance-covariance coverage of the data, descript for descriptive statistics, fit for model fit, est for parameter estimates, and modind for modification indices.

Note

The function uses the functions cfa, lavInspect, lavTech, modindices, parameterEstimates, and standardizedsolution provided in the R package lavaan by Yves Rosseel (2012).

Author(s)

Takuya Yanagida [e-mail]

References

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

See Also

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

Examples

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

#----------------------------------------------------------------------------
# Model specification using 'x' for a one-factor model
# with the same factor structure with one factor at the Within and Between Level

#..........
# Cluster variable specification

# Example 1a: Cluster variable 'cluster' in 'x'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4", "cluster")], cluster = "cluster")

# Example 1b: Cluster variable 'cluster' not in 'x'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster)

# Example 1c: Alternative specification using the 'data' argument
multilevel.cfa(y1:y4, data = Demo.twolevel, cluster = "cluster")

#..........
# Type of construct

# Example 2a: Within-cluster constructs
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               const = "within")

# Example 2b: Shared cluster-level construct
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               const = "shared")

# Example 2c: Configural cluster construct (default)
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               const = "config")

# Example 2d: Simultaneous shared and configural cluster construct
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               const = "shareconf")

#..........
# Residual covariances at the Within level

# Example 3a: Residual covariance between 'y1' and 'y3'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               rescov = c("y1", "y3"))

# Example 3b: Residual covariance between 'y1' and 'y3', and 'y2' and 'y4'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               rescov = list(c("y1", "y3"), c("y2", "y4")))

#..........
# Residual variances at the Between level fixed at 0

# Example 4a: All residual variances fixed at 0
# i.e., strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               fix.resid = "all")

# Example 4b: Fesidual variances of 'y1', 'y2', and 'y4' fixed at 0
# i.e., partial strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               fix.resid = c("y1", "y2", "y4"))

#..........
# Print all results

# Example 5: Set minimum value for modification indices to 1
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               print = "all", mod.minval = 1)

#..........
# Example 6: lavaan model and summary of the estimated model

mod <- multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
                      output = FALSE)

# lavaan model syntax
cat(mod$model)

# Fitted lavaan object
lavaan::summary(mod$model.fit, standardized = TRUE, fit.measures = TRUE)

#..........
# Write results

# Example 7a: Assign results into an object and write results into an Excel file
mod <- multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
                      print = "all", write = "Multilevel_CFA.txt", output = FALSE)

# Example 7b: Assign results into an object and write results into an Excel file
mod <- multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
                      print = "all", output = FALSE)

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

# Estimate model and write results into an Excel file
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
               print = "all", write = "Multilevel_CFA.xlsx")

#----------------------------------------------------------------------------
# Model specification using 'model' for one or multiple factor model
# with the same factor structure at the Within and Between Level

# Example 8a: One-factor model
multilevel.cfa(Demo.twolevel, cluster = "cluster", model = c("y1", "y2", "y3", "y4"))

# Example 8b: Two-factor model
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

# Example 8c: Two-factor model with user-specified labels for the factors
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model = list(factor1 = c("y1", "y2", "y3"), factor2 = c("y4", "y5", "y6")))

#..........
# Type of construct

# Example 9a: Within-cluster constructs
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "within",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

# Example 9b: Shared cluster-level construct
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "shared",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

# Example 9c: Configural cluster construct (default)
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "config",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

# Example 9d: Simultaneous shared and configural cluster construct
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "shareconf",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

#..........
# Residual covariances at the Within level

# Example 10a: Residual covariance between 'y1' and 'y4' at the Within level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
               rescov = c("y1", "y4"))

# Example 10b: Fix all residual variances at 0
# i.e., strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
               fix.resid = "all")

#----------------------------------------------------------------------------
# Model specification using 'model.w' and 'model.b' for one or multiple factor model
# with different factor structure at the Within and Between Level

# Example 11a: Two-factor model at the Within level and one-factor model at the Between level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model.w = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
               model.b = c("y1", "y2", "y3", "y4", "y5", "y6"))

# Example 11b: Residual covariance between 'y1' and 'y4' at the Within level
# Residual covariance between 'y5' and 'y6' at the Between level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
               model.w = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
               model.b = c("y1", "y2", "y3", "y4", "y5", "y6"),
               rescov.w = c("y1", "y4"),
               rescov.b = c("y5", "y6"))

## End(Not run)

misty documentation built on Oct. 24, 2024, 5:10 p.m.