Publications by Tarid Wongvorachan
Item Response Theory_Practice
We will be using the package ltm with two test data sets, one dichotomous and one polytomous. An example with Dichotomous data setwd("D:/Class Materials & Work/Summer 2020 practice/IRT") #Loading required packages library(ltm) library(mirt) We will be using the data from Law School Admission Test (LSAT), N = 1000, 5 items. The data can be ca...
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Data Visualization with base packages
Task1: Specify working directory setwd("D:/Class Materials & Work/ED_Psych 521- Data Management and Visualiztion/R Stat Package/R_Lesson 5- Data Visualization_Base/Practice 7") library(tidyverse) #Read dataset file CleanedData<-read.csv("Tarid_MergedData.Reduced_cleaned.csv", header=T) Task2: Bar chart for SchoolType #Extract Schooltype to ...
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Data Visualization with GGplot2_Advance
Task1: Specify working directory and read the file setwd("D:/Class Materials & Work/ED_Psych 521- Data Management and Visualiztion/R Stat Package/R_Lesson 7- Advance Visualization_GGplot2/Practice 9") #1.1 Read the file PIRLS_Data_Merged<-read.csv("PIRLS2011G4_MergedSample_ggplot2.csv", header=T) Task2: Recode the variable EngagedCat to a ord...
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Data Visualization with GGplot2
Task1: Working directory and read the file setwd("D:/Class Materials & Work/ED_Psych 521- Data Management and Visualiztion/R Stat Package/R_Lesson 6- Data Visualization_GGplot2/Practice 8") #Read dataset file CleanedData<-read.csv("Tarid_MergedData.Reduced_cleaned.csv", header=T) #Activating the package library(ggrepel) library(ggpubr) li...
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Estimating Rasch model with dichotomous data
Examples with Dichotomous data First, we will load the required package and data set. The LSAT is obtained from package ltm setwd("D:/Class Materials & Work/Summer 2020 practice/IRT_Dicho") library(mirt) LSAT <- data.frame(ltm::LSAT) With mirt function, we will use model=1 to indicate that we expect only one factor to be extracted in this expl...
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Educational Data Mining Practice
We will be using Programme for International Student Assessment (PISA) 2015 data set for turkey student. The data comes from a large-scale assessment in various knowledge domains of international students. setwd("D:/Class Materials & Work/Summer 2020 practice/Educational Data Mining") # Packages to be used library("data.table") library("tidy...
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Principal Component Analysis practice
Principal component analysis (PCA) allows us to summarize and to visualize the information in a data set containing inter-correlated quantitative observations. Each variable could be considered as a different dimension, but the numbver of four or more dimensions could be difficult to visualize. PCA can be used to extract the important information...
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Canonical Correlation Analysis practice
Canonical Correlational Analysis (CCA) is a class of multivariate statistical analysis technique that is used to analyze multiple measurements of objects simultaneously. There is a small difference between CCA and PCA- Principal Component Analysis; CCA is used to correlate simultaneously several metric Dependent Variable (DV) and several metric ...
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ARM with data preprocessing
For this practice, we will use the AdultUCI built-in data set in arules package. The data set is not transactionalized yet, so we will practice data preprocessing as well. setwd("D:/Class Materials & Work/Summer 2020 practice/ARM_2") library(arules) #for ARM library(arulesViz) #for ARM visualization Converting data frame into transactional da...
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Association Rule Mining practice
Association Rule Mining (ARM) is a data minin technique that focuses on mining for associations between itemsets for further applications. We will use package arules for ARM and arulesViz for AR visualization. The data set will be the built-in Groceries data. setwd("D:/Class Materials & Work/Summer 2020 practice/ARM") library(arules) #for ARM ...
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