R: Cross-Level Measurement Invariance Evaluation

multilevel.invar {misty}

R Documentation

Cross-Level Measurement Invariance Evaluation

Description

This function is a wrapper function for evaluating configural, metric, and scalar cross-level measurement invariance using multilevel confirmatory factor analysis with continuous indicators by calling the cfa function in the R package lavaan.

Usage

multilevel.invar(..., data = NULL, cluster, model = NULL, rescov = NULL,
                 invar = c("config", "metric", "scalar"), fix.resid = NULL,
                 ident = c("marker", "var", "effect"),
                 estimator = c("ML", "MLR"), optim.method = c("nlminb", "em"),
                 missing = c("listwise", "fiml"),
                 print = c("all", "summary", "coverage", "descript", "fit",
                           "est", "modind", "resid"),
                 print.fit = c("all", "standard", "scaled", "robust"),
                 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` is `NULL`, multilevel confirmatory factor analysis based on a measurement model with one factor at the Within and Between level comprising all variables in the matrix or data frame is conducted to evaluate cross-level measurement invariance. 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. If `model` is specified, the matrix or data frame needs to contain all variables used in the `model` argument. Alternatively, an expression indicating the variable names in `data` e.g., `multilevel.invar(x1, x2, x3, data = dat)`. 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 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`.
`invar`	a character string indicating the level of measurement invariance to be evaluated, i.e., `config` to evaluate configural measurement invariance (i.e., same factor structure across levels), `metric` (default) to evaluate configural and metric measurement invariance (i.e., equal factor loadings across level), and `scalar` to evaluate configural, metric and scalar measurement invariance (i.e., all residual variances at the Between level equal zero).
`fix.resid`	a character vector for specifying residual variances to be fixed at 0 at the Between level for the configural and metric invariance model, 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.
`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 and model comparison, `"est"` for parameter estimates, and `"modind"` for modification indices. By default, a summary of the specification and model fit and model comparison are printed.
`print.fit`	a character string or character vector indicating which version of the CFI, TLI, and RMSEA to show on the console, i.e., `"all"` for all versions of the CFI, TLI, and RMSEA, `"standard"` (default when `estimator = "ML"`) for fit indices without any non-normality correction, `"scaled"` for population-corrected robust fit indices with ad hoc non-normality correction, and `robust` (default when `estimator = "MLR"`) for sample-corrected robust fit indices based on formula provided by Li and Bentler (2006) and Brosseau-Liard and Savalei (2014).
`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 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 extention `".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`	matrix or data frame specified in `x`
`args`	specification of function arguments
`model`	list with specified model for the configural, metric, and scalar invariance model
`model.fit`	list with fitted lavaan object of the configural, metric, and scalar invariance model
`check`	list with the results of the convergence and model identification check for the configural, metric, and scalar invariance model
`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 a list with model fit based on standard, scaled, and robust fit indices, `est` for a list with parameter estimates for the configural, metric, and scalar invariance model, and `modind` for the list with modification indices for the configural, metric, and scalar invariance model

Note

The function uses the functions lavTestLRT provided in the R package lavaan by Yves Rosseel (2012).

Author(s)

Takuya Yanagida takuya.yanagida@univie.ac.at

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

Examples

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

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

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

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

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

#----------------------------------------------------------------------------
# Model specification using 'x' for a one-factor model

#..........
# Level of measurement invariance

# Example 2a: Configural invariance
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, invar = "config")

# Example 2b: Metric invariance
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, invar = "metric")

# Example 2c: Scalar invariance
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, invar = "scalar")

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

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

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

#..........
# Example 4: Print all results
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, print = "all")

#..........
# Example 5: lavaan model and summary of the estimated model
mod <- multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                        cluster = Demo.twolevel$cluster, output = FALSE)

# lavaan syntax of the metric invariance model
mod$model$metric

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

#----------------------------------------------------------------------------
# Model specification using 'model' for one or multiple factor model

# Example 6a: One-factor model
multilevel.invar(Demo.twolevel, cluster = "cluster", model = c("y1", "y2", "y3", "y4"))

# Example 6b:  Two-factor model
multilevel.invar(Demo.twolevel, cluster = "cluster",
                 model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))

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

# Example 7a: Write results into an Excel file
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, print = "all",
                 write = "Multilevel_Invariance.txt")

# Example 7b:  Write results into an Excel file
multilevel.invar(Demo.twolevel[,c("y1", "y2", "y3", "y4")],
                 cluster = Demo.twolevel$cluster, print = "all",
                 write = "Multilevel_Invariance.xlsx")

# Assign results into an object and write results into an Excel file
mod <- multilevel.invar(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_Invariance.xlsx")

## End(Not run)

[Package misty version 0.6.5 Index]