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I post these materials in this website for any potential audiences. If any readers would like to pass any of these materials or use for any purposes beside educating themselves, please contact me by email directly.
The articles written in English have blue links or blue table cells. The articles written in Thai have red links or red table cells.
Here are my teaching materials while I was a lecturer in Chulalongkorn University and Texas Tech University.
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.
This course covers basic principles of psychological testing and measurement, including basic statsitics, scaling, test construction, reliability, validity, item analysis, and test utility. See more details here.
This course covers basic software knowledge for studying in psychology. I taught how to browse 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. See more details here.
This course covers basic knowledge for structural equation modeling. I have some notes that I used when I was a lab instructor. See more details here.
This course covers basic knowledge for multilevel modeling. See more details here.
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.
R is a unique statistical application such that it is an almost perfect combination between programming language and statistical packages, which are required skills for Monte Carlo simulation. The class covered all steps in a Monte Carlo simulation including (a) specifying design conditions, (b) generating data from a desired model, (c) analyzing data, (d) selecting target statistics from the results, and (e) summarizing the statistics across design conditions for publication. Furthermore, the seminar included debugging techniques, running R in high-performance computers, and communication with other statistical packages. See more details here.
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: (a) Importing and exporting data, (b) Getting around in R (the R console, objects, basic computation), (c) Analyzing data, (d) Visualizing data, (e) Programming in R, (f) Extending your R knowledge (packages, help), and (g) 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, time will be available during breaks and for a short period after the seminar for individual consultations.
R for General Linear Model
This seminar will extend the knowledge from the Introduction to R class. The topics for this seminar include: (1) simple and multiple regression, (2) interactions and categorical variables multiple regression, (3) diagnostic checks for violation of assumptions, (4) one-way and factorial analysis of variance, and (5) visualizing data for further interpretation. Additional topics (e.g., mediation) may be included as time allows. The class is designed such that any persons who have 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, time will be available during breaks and at the end of the seminar for individual consultations.
This seminar will demonstrate how to estimate structural equation modeling (SEM) in R environment. As most people know, R is an open-source (free!), programmable statistics platform with many options for different types of data analysis---comparing to at least several-hundred-dollar commercial SEM programs.This seminar will focus on the lavaan package that is a very user-friendly SEM package. The topics for this seminar include: (a) a short introduction to R, (b) a short introduction to SEM packages in R, (c) an introduction to the lavaan package, (d) creating syntax, (e) interpreting outputs, (f) some tips to improve efficiency, and (g) 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 is underused. Therefore, this seminar aim is to encourage CRMDA staff using the HPC by showing the way to use it easily.
Missing Data Analysis
Missing data is common in data collection, especially in survey. Some types of missing data could alter the anylysis results, both in parameter estimates and standard errors. I gave a short lecture on types of missing data and how to handle missing data in 2015 at the Faculty of Psychology, Chulalongkorn University in 2013. I hope this presentation will give you good background on missing data analysis.
Writing and Presentation Note
When I was a lecturer at CU, there was no academic writing guideline, especially in APA reference tailored for writing in Thai. Thus, I wrote a short reference manual to train students in academic writing. I also wrote the grading rubrics in cognitive psychology class. I think this rubric is also helpful as the writing guideline in addition to the reference manual. I also have a powerpoint from class lecture discussing about 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 technique. Check her powerpoint for tips, techniques, do and don't in presentation.