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The articles written in English have blue links or blue table cells. The articles written in Thai have red links or red table cells.

University Courses

Here are my teaching materials from while I was a lecturer at Chulalongkorn University and Texas Tech University.

Introduction (and Intermediate) Statistics in Psychology

This course covers basic statistics useful in psychological research, including descriptive statistics, probability, parameter estimation, mean comparison, correlation, regression, ANOVA, and chi-square statistics. See more details here.

Psychological Testing and Measurement

This course covers basic psychological testing and measurement principles, statistics, scaling, test construction, reliability, validity, item analysis, and test utility. See more details here.

Multilevel Modeling

This course covers the statistical techniques used to analyze data with a hierarchical or nested structure. This can include data from surveys where students are nested within schools or experiments where different observations are nested within subjects. The class will cover the theoretical foundations of multilevel models. Students will learn the distinction between fixed and random effects. Students will learn how to fit multilevel models using statistical software and how to interpret the results. This course will also cover advanced topics such as model selection, missing data handling, and dyadic data analysis. See more details here.

Structural Equation Modeling

This course covers fundamental knowledge for structural equation modeling (SEM). SEM is the statistical technique used to analyze relationships between latent variables. I have some notes I used as a lab instructor at the University of Kansas. See more details here.

Seminar

Introduction to R

This seminar will introduce the R statistical computing environment. R is an open-source (free!) programmable statistics platform with many options for different types of data analysis. The topics for this seminar include:

• Importing and exporting data
• Getting around in R (the R console, objects, basic computation)
• Analyzing data
• Visualizing data
• Programming in R
• Extending your R knowledge (packages, help)
• A look ahead to more advanced topics (linear regression, 3-d graphics)

These topics will be addressed through hands-on activities with example data sets and R code templates that you can take and re-use for your future projects. Additionally, individual consultations will be available during breaks and after the seminar.

Materials: Introduction. Introduction in HTML. Exercise Answers. Dataset: RSem.txt; RSem.csv; RSem.sav (SPSS Dataset); RSem.R.

R for General Linear Model

This seminar will extend the knowledge from the Introduction to R class. The topics for this seminar include:

• Simple and multiple regression.
• Interactions and categorical variables multiple regression.
• Diagnostic checks for violation of assumptions.
• One-way and factorial analysis of variance.
• Visualizing data for further interpretation.

Additional topics (e.g., mediation) may be included as time allows. The class is designed so anyone who has experienced R for about two hours can understand the course materials. These topics will be addressed through hands-on activities with example data sets and R code templates that you can take and re-use for your future projects. Additionally, individual consultations will be available during breaks and after the seminar.

Materials: Paper. Exercise Answers.

lavaan package

This seminar will demonstrate how to estimate structural equation modeling (SEM) in an R environment. As most people know, R is an open-source (free!) programmable statistics platform with many options for different types of data analysis. This seminar will focus on the package, a very user-friendly SEM package. The topics for this seminar include:

• A short introduction to R.
• A brief introduction to SEM packages in R.
• An introduction to the package.
• Creating syntax.
• Interpreting outputs.
• Some tips to improve efficiency.
• A look ahead to more advanced topics (e.g., graphics, bootstrap, and SEM with categorical indicators).

These topics will be addressed through hands-on activities with example data sets and R code templates that you can take and re-use for your future projects.

Materials: Presentation. Example Codes. Example Dataset 1. Example Dataset 2. Example Dataset 3. Example Dataset 4. Example Dataset 5. Example Dataset 6.

KUANT guides by Patrick Miller: Guide in HTML. Guide in PDF. Example Dataset.

Missing Data Analysis

Missing data is expected in data collection, especially in a survey. Some types of missing data could alter the analysis results, both in parameter estimates and standard errors. I gave a short lecture on types of missing data and how to handle missing data. This presentation will provide you with a good background on missing data analysis.

Materials: Presentation.

Dyadic Data Analysis

Dyadic data analysis comprises statistical techniques for analyzing data from pairs of individuals. This section explores different types of dyads and methods for modeling the interdependence and interactions between dyad members.

Materials: Presentation (Part 1: Intro to R). Presentation (Part 2: Dyadic Data Analysis). R script (HTML). R script (Rmd). Dataset 1. Dataset 2. Dataset 3. Dataset 4. Dataset 5. Exercise. Exercise Dataset 1. Exercise Dataset 2. Exercise Dataset 3. Exercise Dataset 4. Exercise Dataset 5.

Looking for my older teaching materials, internal departmental talks, or work from my early career? Please visit the Archived Resources page. Please note that some of these materials may no longer reflect the current state of the art or my current perspectives, but I have kept them available in case anyone still relies on them.