Jihong Zhang

Ph D. Graduate Research Assistant

University of Iowa

My name is Jihong Zhang, a doctorial student at University of Iowa, as a part of the Department of Psychological and Quantitative Foundations in the College of Education.

My research interests mainly focus on the Bayesian Diagnostic Classification Models (DCMs) - a special kind of Item Response Model and the model checking method, as applied in the psychological, educational, and social sciences. I seek to improve the utility of advanced psychometric modeling and provide easy-to-use tools or softwares for researchers.

Interests

Bayesian Psychometric Modeling
Machine Learning and data mining in Education
Diagnostic Classification Models
Structure Equation Modelling
Educational Measurement

Education

Ph.D in Educational Measurement & Statistics , 2022

University of Iowa
MS in Educational Psychology, 2019

University of Kansas
BS in Applied Psychology, 2012

Soochow University

Recent Posts

[Pre-print] A Model Comparison Approach to Posterior Predictive Model Checks in Bayesian Confirmatory Factor Analysis

Abstract Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by the location of a ML-based estimate to the predictive distribution of a statistic for a model. When the ML-based point estimate is far away from the center of the density of the posterior predictive distribution, model fit is poor. One main critique of such standard PPMC procedures is the strong link to the ML-based point estimates of the observed data.

Last updated on May 25, 2020 12 min read R

Study Notes: gt package and format table

A introduction about gt package is here knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.align = "default", eval = TRUE) library(gt) suppressMessages(library(tidyverse)) Basics of gt A basic gt table can be created as so data("iris") glimpse(iris) ## Rows: 150 ## Columns: 5 ## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4… ## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.

Nov 21, 2019 3 min read blog

Some Thoughts After Reading <Statistical Rethinking>

Last night, I read the 1st chapter of Statistical Rethinking: A Bayesian Course with Examples in R and Stan from Richard McElreath. I found this is nice book to share with my friends. The core idea of that chapter is the relationship between NULL hypothesis and statistical model. That is, should we trust the statistical models to reject the NULL hypothesis? It is a long history to use statistical models to figure out what is true?

Last updated on Nov 14, 2019 2 min read blog

Use R as a bash language

R language could be easily used as a bash script using Rscript *.R. system() is a R base function which could run command line within R. Below is a simple example which allows to automate create a new blog post: (1) Ask users to type in filename, title and language (2) Create a new markdown file in specific directory (i.e. your local posts saved path) (3) Add some metadata in .

Jihong Zhang

Last updated on Nov 11, 2019 2 min read blog