If wanted, indicators for latent variables can be replaced with
reliablity corrected single items, using Chronbach’s \(\alpha\). This can either be done using the
relcorr_single_item
function, returning the altered model
syntax and data, or via the rcs
argument in
modsem
. Here we can see an example using the
relcorr_single_item
function:
tpb_uk <- "
# Outer Model (Based on Hagger et al., 2007)
ATT =~ att3 + att2 + att1 + att4
SN =~ sn4 + sn2 + sn3 + sn1
PBC =~ pbc2 + pbc1 + pbc3 + pbc4
INT =~ int2 + int1 + int3 + int4
BEH =~ beh3 + beh2 + beh1 + beh4
# Inner Model (Based on Steinmetz et al., 2011)
INT ~ ATT + SN + PBC
BEH ~ INT + PBC
BEH ~ INT:PBC
"
corrected <- relcorr_single_item(syntax = tpb_uk, data = TPB_UK)
corrected
Here we can see that relcorr_single_item
returns a new
model syntax, and a new data.frame
containing the generated
items. Additionally, it also returns the Chronbach’s \(\alpha\) and average variance
extraced (AVE) for the different constructs in the model. The
syntax and data can be extracted using the $
operator, and
used to estimate the model.
syntax <- corrected$syntax
data <- corrected$data
est_dca <- modsem(syntax, data = data, method = "dblcent")
est_lms <- modsem(syntax, data = data, method="lms", nodes=32)
summary(est_lms)
The easiest approach however, is to use the rcs
argument
in the modsem
function to call
relcorr_single_item
before estimating the model.
If you don’t want to use reliablity-corrected single items for all of
the latent variables in the model, you can use the choose
argument in relcorr_single_item
(orrcs.choose
in modsem
) to select which set of indicators to
replace.