invariance {EGAnet} | R Documentation |
Measurement Invariance of EGA
Structure
Description
Estimates configural invariance using bootEGA
on all data (across groups) first. After configural variance is established,
then metric invariance is tested using the community structure that established
configural invariance (see Details for more information on this process)
Usage
invariance(
data,
groups,
structure = NULL,
iter = 500,
configural.threshold = 0.7,
configural.type = c("parametric", "resampling"),
corr = c("auto", "cor_auto", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("BGGM", "glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
uni.method = c("expand", "LE", "louvain"),
ncores,
seed = NULL,
verbose = TRUE,
...
)
Arguments
data |
Matrix or data frame. Should consist only of variables to be used in the analysis |
groups |
Numeric or character vector (length = |
structure |
Numeric or character vector (length = |
iter |
Numeric (length = 1).
Number of iterations to perform for the permutation.
Defaults to |
configural.threshold |
Numeric (length = 1).
Value to use a threshold in |
configural.type |
Character (length = 1).
Type of bootstrap to use for configural invariance in |
corr |
Character (length = 1).
Method to compute correlations.
Defaults to
For other similarity measures, compute them first and input them
into |
na.data |
Character (length = 1).
How should missing data be handled?
Defaults to
|
model |
Character (length = 1).
Defaults to
|
algorithm |
Character or
|
uni.method |
Character (length = 1).
What unidimensionality method should be used?
Defaults to
|
ncores |
Numeric (length = 1).
Number of cores to use in computing results.
Defaults to If you're unsure how many cores your computer has,
then type: |
seed |
Numeric (length = 1).
Defaults to |
verbose |
Boolean (length = 1).
Should progress be displayed?
Defaults to |
... |
Additional arguments that can be passed on to
|
Details
In traditional psychometrics, measurement invariance is performed in sequential testing from more flexible (more free parameters) to more rigid (fewer free parameters) structures. Measurement invariance in network psychometrics is no different.
Configural Invariance
To establish configural invariance, the data are collapsed across groups
and a common sample structure is identified used bootEGA
and itemStability
. If some variables have a replication
less than 0.70 in their assigned dimension, then they are considered unstable
and therefore not invariant. These variables are removed and this process
is repeated until all items are considered stable (replication values greater
than 0.70) or there are no variables left. If configural invariance cannot be
established, then the last run of results are returned and metric invariance
is not tested (because configural invariance is not met). Importantly, if any
variables are removed, then configural invariance is not met for the
original structure. Any removal would suggest only partial configural invariance
is met.
Metric Invariance
The variables that remain after configural invariance are submitted to metric
invariance. First, each group estimates a network and then network loadings
(net.loads
) are computed using the assigned
community memberships (determined during configural invariance). Then,
the difference between the assigned loadings of the groups is computed. This
difference represents the empirical values. Second, the group memberships
are permutated and networks are estimated based on the these permutated
groups for iter
times. Then, network loadings are computed and
the difference between the assigned loadings of the group is computed, resulting
in a null distribution. The empirical difference is then compared against
the null distribution using a two-tailed p-value based on the number
of null distribution differences that are greater and less than the empirical
differences for each variable. Both uncorrected and false discovery rate
corrected p-values are returned in the results. Uncorrected p-values
are flagged for significance along with the direction of group differences.
Three or More Groups
At this time, only two groups are supported. There is a method proposed to test three or more groups in Jamison, Golino, and Christensen (2023) but this approach has not been thoroughly vetted and validated. Future versions of the package will provide support for three or more groups once there is an established consensus for best practice.
For more details, see Jamison, Golino, and Christensen (2023)
Value
Returns a list containing:
configural.results |
|
memberships |
Original memberships provided in |
EGA |
Original |
groups |
A list containing: |
permutation |
A list containing:
|
results |
Data frame of the results (which are printed) |
Author(s)
Laura Jamison <lj5yn@virginia.edu>, Hudson F. Golino <hfg9s at virginia.edu>, and Alexander P. Christensen <alexpaulchristensen@gmail.com>,
References
Original implementation
Jamison, L., Golino, H., & Christensen, A. P. (2023).
Metric invariance in exploratory graph analysis via permutation testing.
PsyArXiv.
See Also
plot.EGAnet
for plot usage in EGAnet
Examples
# Load data
wmt <- wmt2[-1,7:24]
# Groups
groups <- rep(1:2, each = nrow(wmt) / 2)
## Not run:
# Measurement invariance
results <- invariance(wmt, groups, ncores = 2)
# Plot with uncorrected alpha = 0.05
plot(results, p_type = "p", p_value = 0.05)
# Plot with BH-corrected alpha = 0.10
plot(results, p_type = "p_BH", p_value = 0.10)
## End(Not run)