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Latent Profile Analysis using MCLUST (in R)

Hi there! This is Jihong. This is a webpage folked from JOSHUA M. ROSENBERG. It aims to provid a very clear example about how to conduct Latent Profile Analysis using MCLUST in r. Import data and load packages library(tidyverse) library(mclust) library(hrbrthemes) # typographic-centric ggplot2 themes data("iris") df <- select(iris, -Species) # 4 variables explore_model_fit <- function(df, n_profiles_range = 1:9, model_names = c("EII", "VVI", "EEE", "VVV")) { x <- mclustBIC(df, G = n_profiles_range, modelNames = model_names) y <- x %>% as.

Simulation Study of Linking using mirt

Introduction Calibration of Form A Look at the data Plot the density of true \(\theta\) of Group A CTT Table Clean data Classical Test Theory Final Calibration of Form A Model Specification Calibration of Form B Final Calibration of Form A Model Specification of B b-plot a-plot Linking This simulation study is to show how to do IRT Linking Process using mirt R Package.

One Example of Measurement Invariance

What is Measurement Invariance (MI)? Why we should use Measurement Invariance? How to use Measurement Invariance Multiple Group CFA Invariance Example (data from Brown Charpter 7): Major Deression Criteria across Men and Women (n =345) Data Import Model Specification Model Options Runing Model Model Comparision STRUCTUAL INVARIANCE TESTS Factor Variance Invariance Model Factor Mean Invariance Model Model Comparision Recently, I was asked by my friend why should we use Measurement Invariance in real research.