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MIIVefa

A quick tutotial for using the MIIVefa package in R.

MIIVefa uses Model Implied Instrumental Variables (MIIVs) to perform Exploratory Factor Analysis (EFA).

The Basics

MIIVefa is data-driven algorithm for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.

Running MIIVefa

1, Prepare your data.

• The input data frame should be in a wide format: columns being different observations and rows being the specific data entries.

• Column names should be clearly labeled.

2, Installing MIIVefa.

• In the R console, enter and execute ‘install.packages(“MIIVefa”)’ or ‘devtools::install_github(“https://github.com/lluo0/MIIVefa”)’ after installing the “devtools” package.

• Load the MIIVefa by executing ‘library(MIIVefa)’ after installing.

3, Running miivefa.

• The only necessarily required input is the raw data matrix.

• All 4 arguments are shown below.

• ‘sigLevel’ is the significance level with a default of 0.05. ‘scalingCrit’ is the specified criterion for selecting the scaling indicator whenever a new latent factor is created and the default is ‘sargan+factorloading_R2.’ And ‘CorrelatedErrors’ is a vector containing correlated error relations between observed variables with a default of NULL.

                EFAmiiv <- function(data,
  
                sigLevel = .05,
  
                scalingCrit = "sargan+factorloading_R2",
  
                correlatedErrors = NULL)

Output of MIIVefa

• The output of a miivefa object contains 2 parts:

• 1, a suggested model, of which the syntax is identical to a ‘lavaan’ model. Accessible via output$model.

• 2, a miivsem model fit of the suggested model. The suggested model is run and evaluated using ‘MIIvsem’ and all miivsem attributes can be accessed. Accessible via output$fit. # Examples of MIIVefa

Please refer to the package vignette.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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