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Note

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

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.

Introduction to R

This course 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. I covered the basic knowledge in R and provided fundamentals for learning advanced R techniques. See more details here.

Simulation Studies in R

R is a unique statistical application that combines programming language and statistical packages almost perfectly. This course will show how to use R for a Monte Carlo simulation. The class covered all steps in a Monte Carlo simulation, including:

• Specifying design conditions.
• Generating data from a desired model.
• Analyzing data.
• Selecting target statistics from the results.
• Summarizing the statistics across design conditions for publication.

Furthermore, the class included debugging techniques, running R in high-performance computers, and communication with other statistical packages. See more details here.

Computer Application in Psychology

This course covers basic software knowledge for studying psychology, including how to browse the internet efficiently, how to search for psychology articles databased (e.g., PsycINFO), how to use MS Word, Excel, and PowerPoint, how to use Acrobat Professional, how to use SPSS for data management and basic statistics, and how to write an academic report in psychology. Please note that the class was in 2010. The materials in this class need to be updated. 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. 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: All materials.

KU High Performance Computing

The power of high-performance computing (HPC) is tremendous. A simulation study that requires one year using one computer can be run in only a day using HPC with 400 nodes. However, while I am providing this seminar, the HPC of KU CRMDA needs to be used more. Therefore, this seminar aims to encourage CRMDA staff to use the HPC by showing how to use it easily.

Materials: Paper. Examples.

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.

Writing and Presentation Note

As a lecturer at CU, there was no academic writing guideline, especially in APA references tailored for writing in Thai. Thus, I wrote a short reference manual to train students in academic writing. I also wrote the grading rubric in cognitive psychology class. This rubric is also helpful as a writing guideline in addition to the reference manual. I also have a PowerPoint from the class lecture discussing academic writing and APA reference guideline, as well as a PowerPoint about presenting statistical results.

While I taught the computer application class, Thipnapa Huansuriya was my guest lecturer, talking about presentation techniques. Check her PowerPoint for tips, techniques, and do and don't in presentations.