R: Random Allocation of Items to Parcels in a Structural...

parcelAllocation {semTools}

R Documentation

Random Allocation of Items to Parcels in a Structural Equation Model

Description

This function generates a given number of randomly generated item-to-parcel allocations, fits a model to each allocation, and provides averaged results over all allocations.

Usage

parcelAllocation(model, data, parcel.names, item.syntax, nAlloc = 100,
  fun = "sem", alpha = 0.05, fit.measures = c("chisq", "df", "cfi", "tli",
  "rmsea", "srmr"), ..., show.progress = FALSE, do.fit = TRUE)

Arguments

`model`	`lavaan` model syntax specifying the model fit to (at least some) parceled data. Note that there can be a mixture of items and parcels (even within the same factor), in case certain items should never be parceled. Can be a character string or parameter table. Also see `lavaanify` for more details.
`data`	A `data.frame` containing all observed variables appearing in the `model`, as well as those in the `item.syntax` used to create parcels. If the data have missing values, multiple imputation before parceling is recommended: submit a stacked data set (with a variable for the imputation number, so they can be separateed later) and set `do.fit = FALSE` to return the list of `data.frame`s (one per allocation), each of which is a stacked, imputed data set with parcels.
`parcel.names`	`character` vector containing names of all parcels appearing as indicators in `model`.
`item.syntax`	lavaan model syntax specifying the model that would be fit to all of the unparceled items, including items that should be randomly allocated to parcels appearing in `model`.
`nAlloc`	The number of random items-to-parcels allocations to generate.
`fun`	`character` string indicating the name of the `lavaan` function used to fit `model` to `data`. Can only take the values `"lavaan"`, `"sem"`, `"cfa"`, or `"growth"`.
`alpha`	Alpha level used as criterion for significance.
`fit.measures`	`character` vector containing names of fit measures to request from each fitted `lavaan` model. See the output of `fitMeasures` for a list of available measures.
`...`	Additional arguments to be passed to `lavaanList`
`show.progress`	If `TRUE`, show a `txtProgressBar` indicating how fast the model-fitting iterates over allocations.
`do.fit`	If `TRUE` (default), the `model` is fitted to each parceled data set, and the summary of results is returned (see the Value section below). If `FALSE`, the items are randomly parceled, but the model is not fit; instead, the `list` of `data.frame`s is returned (so assign it to an object).

Details

This function implements the random item-to-parcel allocation procedure described in Sterba (2011) and Sterba and MacCallum (2010). The function takes a single data set with item-level data, randomly assigns items to parcels, fits a structural equation model to the parceled data (using lavaan), and repeats this process for a user-specified number of random allocations. Results from all fitted models are summarized in the output. For further details on the benefits of the random allocation of itesm to parcels, see Sterba (2011) and Sterba and MccCallum (2010).

Value

`Estimates`	A data frame containing results related to parameter estimates with columns corresponding to parameter names, average parameter estimates across allocations, the standard deviation of parameter estimates across allocations, the minimum parameter estimate across allocations, the maximum parameter estimate across allocations, the range of parameter estimates across allocations, and the proportions of allocations in which the parameter estimate is significant.
`SE`	A data frame containing results related to standard errors with columns corresponding to parameter names, average standard errors across allocations, the standard deviation of standard errors across allocations, the minimum standard error across allocations, the maximum standard error across allocations, and the range of standard errors across allocations.
`Fit`	A data frame containing results related to model fit with columns corresponding to fit index names, the average of each index across allocations, the standard deviation of each fit index across allocations, the minimum of each fit index across allocations, the maximum of each fit index across allocations, and the range of each fit index across allocations.

Author(s)

Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)

References

Sterba, S. K. (2011). Implications of parcel-allocation variability for comparing fit of item-solutions and parcel-solutions. Structural Equation Modeling, 18(4), 554–577. doi:10.1080/10705511.2011.607073

Sterba, S. K. & MacCallum, R. C. (2010). Variability in parameter estimates and model fit across random allocations of items to parcels. Multivariate Behavioral Research, 45(2), 322–358. doi:10.1080/00273171003680302

Examples


## Fit 2-factor CFA to simulated data. Each factor has 9 indicators.

## Specify the item-level model (if NO parcels were created)
item.syntax <- c(paste0("f1 =~ f1item", 1:9),
                 paste0("f2 =~ f2item", 1:9))
cat(item.syntax, sep = "\n")
## Below, we reduce the size of this same model by
## applying different parceling schemes


## 3-indicator parcels
mod.parcels <- '
f1 =~ par1 + par2 + par3
f2 =~ par4 + par5 + par6
'
## names of parcels
(parcel.names <- paste0("par", 1:6))

## Not run: 
parcelAllocation(mod.parcels, data = simParcel, parcel.names, item.syntax,
                 nAlloc = 20, std.lv = TRUE, parallel = "snow", iseed = 12345)

## End(Not run)


## multigroup example
simParcel$group <- 0:1 # arbitrary groups for example
mod.mg <- '
f1 =~ par1 + c(L2, L2)*par2 + par3
f2 =~ par4 + par5 + par6
'
## names of parcels
(parcel.names <- paste0("par", 1:6))

set.seed(12345)
parcelAllocation(mod.mg, data = simParcel, parcel.names, item.syntax,
                 std.lv = TRUE, group = "group", group.equal = "loadings",
                 nAlloc = 20, show.progress = TRUE)



## parcels for first factor, items for second factor
mod.items <- '
f1 =~ par1 + par2 + par3
f2 =~ f2item2 + f2item7 + f2item8
'
## names of parcels
(parcel.names <- paste0("par", 1:3))

set.seed(12345)
parcelAllocation(mod.items, data = simParcel, parcel.names, item.syntax,
                 nAlloc = 20, std.lv = TRUE)



## mixture of 1- and 3-indicator parcels for second factor
mod.mix <- '
f1 =~ par1 + par2 + par3
f2 =~ f2item2 + f2item7 + f2item8 + par4 + par5 + par6
'
## names of parcels
(parcel.names <- paste0("par", 1:6))

set.seed(12345)
parcelAllocation(mod.mix, data = simParcel, parcel.names, item.syntax,
                 nAlloc = 20, std.lv = TRUE)

[Package semTools version 0.5-0 Index]