Ou Zhang

Research Scientist & Data Scientist

Pearson Psychometrics Service

Biography

I am a data scientist and psychometrician at Pearson. My research interests include psychometrics, applied statistics, data visualization, machine learning, and general data science. My current work mainly focuses on data science, psychometrics modeling, statistical analysis, testing development, and consulting.

I received my PhD in Educational Research, Evaluation, and Methodology from University of Florida in 2012.

Prior to joining Pearson Psychometrics Service, I was a Psychometrician II and team lead at clinical assessment branch in Pearson. While at clinical branch, I led multiple flagship clinical assessment products, including WISC-5, GFTA-3, KLPA-3, Quotient ADHD diagnosis test, TEACH-2 Behavior test.

Outside of psychometrics and data science, I am also a programming lover, a sports fan, and a father of two wonderful kids.

Interests

Psychometrics
Applied Statistics
Data Science
Machine Learning
Data Visualization

Education

PhD in Educational Research, Evaluation, and Methodology, 2012

University of Florida
MEd in Educational Research, Measurement, and Evaluation, 2007

Boston College
BSc in Computer Science, 2001

Chengdu University of Technology

Experience

Data Scientist & Research Scientist

Pearson Psychometrics Service

Mar 2017 – Present San Antonio, Texas

Member of the Pearson psychometric team working across multiple projects including the NY Regents, The Partnership for Assessment of Readiness for College and Careers (PARCC), The Arizona English Language Learner Assessment (AZELLA), and National Board for Professional Teaching Standards (NBPTS), etc.

Responsibilities include:

Perform research program on psychometric modeling
Create and maintain SAS modules and R packages
Oversee development and execution of programming needs for scoring and reporting
Develop and maintain statistical methodologies for the psychometrics framework
Program and implement reproducible report system for multiple projects
Prepare research projects for presentation to national conferences and technical advisory committee

Psychometrician II & Team Lead

Pearson Clinical Assessment

Aug 2012 – Mar 2017 San Antonio, Texas

Lead psychometrician and team lead of the clinical assessment team working across multiple projects including the Wechsler Intelligence Scale for Children (WISC5), Test of Everyday Attention for Children (TEA-Ch2), and Pharmacy College Admission Test (PCAT)

Responsibilities include:

Designed and developed automated scoring and text mining algorithm and implemented to scoring module of digital assessment platform
Developed statistical model and scopes to examine the validity and reliability of the psychological/behavior measurement products
Evaluated and interpreted the statistical and psychometric analysis results for the internal stakeholders and research development
Authored technical documentations and statistical reports for the Pearson measurement projects
Motored and supervised psychometric teams including guiding psychometrician and statistical analysts in completing assignments and building up necessary statistical analysis and modeling techniques

Research Fellow & Statistical Consultant

Assessment and Program Evaluation Services, University of Florida

Nov 2008 – Aug 2012 Gainesville, Florida

Research assistant for the psychometric team working on the Collaborative Assessment & Program Evaluation Services.

Responsibilities include:

Worked with project director in the construction of evaluation plans and monitored evaluation process
Assisted in establishing, monitoring, evaluating, developing and implementing strategies for project evaluation
Designed and developed online survey instruments through qualtrics for NSF program evaluation
Compiled, cleaned, and analyzed quantitative/qualitative survey data for further analysis
Performed Portfolio Management campaign tracking and analysis
Developed& wrote summary reports of data analysis for multiple stakeholder audiencesand program staff
Created R scripts to score technology enhanced items

Projects

Quotient ADHD Diagnosis System

General Intelligence G-loading

Recent Posts

9 Join Function Example with the R {dplyr} Package

1 Simple Example Data 2 Load {dplyr} package 3 Function 1: inner_join 4 Function 2: left_join 5 Function 3: right_join 6 Function 4: full_join 7 Function 5: semi_join 8 Function 6: anti_join 9 Complex Example 1: Join Multiple Data Frames 10 Complex example 2: Join by Multiple Columns 11 Complex example 3: Join Data & Delete ID I always wanted to write a blog post summarizing the join function.

Last updated on Nov 25, 2020 9 min read R, data wrangling, SQL

Outliers-Part 4:Finding Outliers in a multivariated way

1 Data Source 1.1 Variables in Data 2 Model-specific methods 2.1 Cook’s Distance 2.2 Pareto 3 Multivariate methods 3.1 Mahalanobis Distance 3.1.1 Details about Mahalanobis Distance 3.2 Robust Mahalanobis Distance 3.3 Minimum Covariance Determinant (MCD) 3.3.1 robust tolerance ellipsoid (RTE) 3.4 Invariant Coordinate Selection (ICS) 3.5 OPTICS 3.6 Isolation Forest 3.7 Local Outlier Factor 4 ‘check_outliers’ function in {performance} R package 4.0.1 Threshold specification 5 Reference Figure 0.

Last updated on Nov 18, 2020 15 min read R, Statistics

Outliers-Part 3:Outliers in Regression

1 Types of Unusual Observations 1.1 Regression Outliers 1.2 Leverage 1.3 Influential Observations 1.4 Good vs. Bad Leverage 2 Detecting Influential Observations 2.1 Graphic diagnostics 2.1.1 A scatter plot with Confidence Ellipse 2.1.2 Quantile Comparison Plots (QQ-Plot) 2.1.2.1 Rule of Thumb 2.1.3 Added-variable plots 2.2 Numerical diagnostics 2.2.1 Hat Matrix 2.2.1.1 Rule of Thumb 2.2.2 Standardized Residuals 2.2.2.1 Rule of Thumb 2.2.3 Studentized Residuals 2.

Last updated on Nov 12, 2020 21 min read R, Statistics

Outliers-Part 2:Finding Outliers in a univariated way

1 Method 1: Sorting Your Datasheet to Find Outliers 2 Method 2: Graphing Your Data to Identify Outliers 2.1 Histogram 2.2 Boxplot 2.2.1 Adjusted boxplot (Hubert and Vandervieren, 2008) 3 Method 3: Using Z-scores to Detect Outliers 3.1 Z-Score pros: 3.2 Z-Score cons: 4 Method 4: Using the Interquartile Range (IRQ) to Create Outlier Fences 5 Method 5: Percentiles 5.1 scores function from {outliers} packages 6 Method 6: Hampel filter 7 Method 7: Finding Outliers with Hypothesis Tests 7.

Last updated on Nov 10, 2020 21 min read R, Statistics

Outliers-Part 1:Causes, Philosophy and General Rules

1 What are Outliers? 2 Causes for Outliers 3 Types of Outliers 4 Philosophy about Finding Outliers 5 General Rules Figure 0.1: Outliers 4 years ago (Yes, back to 2016), I was asked by a director of data science department from a very famous IT company about outliers. Basically, she asked two questions: What are outliers? How to detect them? Also in my daily research life, I have encountered noisy data all the time.

Last updated on Nov 11, 2020 8 min read R, Statistics

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