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.