Dr. Shonn Cheng

☕️

Shonn Cheng

Assistant Professor

National Taipei University of Technology

About Me

Dr. Shonn Cheng received his Ph.D. in Educational Studies at The Ohio State University (USA) in summer 2019. Thereafter, he pursued one-year postdoctoral training at Virginia Commonwealth University (USA). During 2020-2022, he worked as an Assistant Professor in the Department of Instructional Systems Design and Technology at Sam Houston State University (USA). Currently, Dr. Cheng is an Assistant Professor in the Graduate Institute of Technological and Vocational Education at National Taipei University of Technology (Taiwan) and the Director of the META Lab.

Interests

Motivation
Expertise
Training with Technology
Analysis

Education

PhD Educational Studies
The Ohio State University
MA Educational Studies
The Ohio State University
MA Education
New Mexico State University
BA English
Wenzao Ursuline University of Languages
AMS Business Administration
National Taipei University of Business

Recent Publications

Shonn Cheng (2025). A scoping review of research on first-generation college students’ learning in online environments from 2000 to 2020. Journal of Computing in Higher Education.

Blog

Missing Value (notes for myself)

p.16-17 Introduction This part explains the detailed steps of calculating Little’s test statistic to check for the Missing Completely at Random (MCAR) assumption, as described in the provided example. The test is based on comparing the means of observed and missing data patterns, with the computation of the test statistic $T_L$ and its interpretation.

Jul 19, 2025

SEM (notes for myself)

p.21 Covariance Derivation The covariance between two random variables $X_1$ and $X_2$ is defined as: $$ \text{Cov}(X_1, X_2) = \mathbb{E}\left[(X_1 - \mathbb{E}(X_1))(X_2 - \mathbb{E}(X_2))\right] $$Step 1: Expanding the expression First, expand the product inside the expectation:

Jul 2, 2025

Why Do We Need to Care About Endogeneity?

Let’s consider a simple linear regression model: $$ y = \beta_0 + \beta_1 x + \varepsilon $$Where: $y$: outcome variable $x$: predictor $\varepsilon$: error term (disturbance or residual) $\beta_0, \beta_1$: parameters to estimate What OLS Assumes For the OLS (Ordinary Least Squares) estimator to be unbiased, one of the Gauss-Markov assumptions is:

Jun 28, 2025

When `reorder` Fails

I am using the mpg dataset included in the ggplot2 package. #load packages library(tidyverse) library(tidytext) #view data mpg ## Rows: 234 ## Columns: 11 ## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "… ## $ model <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "… ## $ displ <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.… ## $ year <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200… ## $ cyl <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, … ## $ trans <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto… ## $ drv <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4… ## $ cty <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1… ## $ hwy <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2… ## $ fl <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p… ## $ class <chr> "compact", "compact", "compact", "compact", "compact", "c… mpg %>% group_by(trans) %>% count() %>% ungroup() %>% ggplot(mapping = aes(trans, n)) + geom_bar(stat = "identity")

Jul 13, 2024

Coefficient H

Please check McNeish (2018) for details. Here, I am using the NSCH dataset as an example. #load packages library(haven) library(tidyverse) library(userfriendlyscience) #import data data<-read_sav("nsch.sav") #clear the current graphics frame and get ready for the next plot plot.new()

Apr 8, 2024