modelbpp (version 0.1.6)

measurement_invariance_models: Measurement Invariance Models

Description

Generate metric and scalar invariance models and their partial invariance versions.

Usage

measurement_invariance_models(
  cfa_out,
  max_free = 1,
  metric = TRUE,
  scalar = TRUE,
  progress = TRUE
)

Value

A list of lavaan::cfa()

output. The names are automatically generated to indicate whether a model is configural, metric, or scalar invariance, or the item(s) without between-group constraints on the loadings (for partial metric invariance) or intercepts (for partial scalar invariance).

Arguments

cfa_out

The output of lavaan::cfa().

max_free

The maximum number of constraints to be released when generating the partial invariance models. For example, if set to 1, then only the partial metric invariance model only has at most one item allowed to have different loadings across group. Default is 1. If set to zero, then no partial invariance models will be generated.

metric

Logical. If TRUE, the default, then metric invariance model and its partial invariance versions are generated.

scalar

Logical. If TRUE, the default, then scalar invariance model and its partial invariance versions are generated.

progress

Logical. If TRUE, the default, progress bars will be displayed when fitting partial invariance models.

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Details

This a helper function to generate, based on a multigroup confirmatory factor analysis (CFA) model with no between-group equality constraints, the following models:

• A metric invariance model (loadings constrained to be equal across groups).

• A scalar invariance model (intercepts and loadings constrained to be equal across groups).

• Partial invariance versions of the previous two models, such as a model with the loadings of all items, except for one, constrained to be equal across groups.

The models generated can then be used in model_set() to compute BPPs.

Requirements

The model used as the input needs to be fitted with no between group constrains, that is, it is a configural invariance model. Although not a must, it is advised to use the default way to identify each factor (that is, fixing a loading to one).

Implementation

This function simply use the group.partial and group.equal argument of lavaan::cfa() to generate the models.

See Also

model_set()

Examples

library(lavaan)
# For illustration, only one factor is used,
# with one item from another factor added
# just to make the model not saturated.
HSmod <-
"
spatial =~ x1 + x2 + x3 + x4
"
fit_config <- cfa(model = HSmod,
                  data = HolzingerSwineford1939,
                  group = "school")
fit_mi <- measurement_invariance_models(fit_config)
names(fit_mi)
# Need to add 'skip_check_sem_out = TRUE' to use multigroup models.
out <- model_set(sem_out = fit_mi,
                 skip_check_sem_out = TRUE,
                 progress = FALSE,
                 parallel = FALSE)
print(out)