Publications by Tarid Wongvorachan
Text Mining and Word Cloud fundamentals
Text Mining method allows us to highlight the most frequently used keywords in a paragraph of texts. After we finish mining the text, our output can be in forms of word cloud (aka, text cloud or tag cloud), Word Frequency Plot, or Term Association. Advantages of using word clouds are its simplicity in communicating qualitative findings in terms o...
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Support Vector Machine_Practice
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. The most important question, however, is how do we decide the most optimized hyperplane. setwd("D:...
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Logistic Regression_Practice
setwd("D:/Class Materials & Work/Summer 2020 practice/Logistic Regression") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/Logistic Regression" #Prepare the data----------------------------------------- #Import a dataframe df <- read.csv("binary.csv", header = T) str(df) #Check data structure ## 'data.frame': 400 obs. of ...
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Confirmatory Factor Analysis_Practice
We will be using a built-in dataset from lavaan package, the HolzingerSwineford1939 dataset. Confirmatory Factor Analysis (CFA) is usually conducted to test the hypothesis of relationships between observed (manifest) variables and latent variables based on the already established theory. Formulating model syntax is how we describe the model befor...
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Multiple Group CFA_Practice
The lavaan package also supports multiple groups SEM by adding group argument in the cfa() function. In this practice, we will fit a CFA model with the HolzingerSwineford1939 data set. setwd("D:/Class Materials & Work/Summer 2020 practice/SEM/Multiple Group CFA") library(lavaan) library(semTools) First, we will specify the model. HS.model <- '...
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Path Analysis_Practice
Path analysis is a form of multiple regression that examines statistical causal mechanism between IVs and a DV. Using this method one can estimate both the magnitude and significance of causal connections between variables. We have multiple regression equations that explain relationships between the DVs to the IV in a restricted order as explaine...
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Full Structural Equation Modeling_Practice
In this practice, we will use the built-in PoliticalDemocracy dataset, which is also used by Bollen (1989) in his book. The figure below contains a graphical representation of the model that we want to fit. The above model can be syntactically explained as follows: setwd("D:/Class Materials & Work/Summer 2020 practice/SEM/SEM") library(lavaan)...
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Multidimensional EFA_Practice
Multidimensional EFA is similar to its unidimensional variance, but with two or more latent variable (factor) in the model. Splitting the BFI dataset For this chapter, you’ll be using the bfi dataset, which consists of responses to 25 items measuring the Big Five personality traits. Since you’ll be doing both exploratory and confirmatory fac...
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Classical Test Theory_Practice
Classical Test Theory (CTT) CTT (aka weak theory or true-score theory) aims to predict outcomes of the test such as item difficulty or the ability of test-takers to improve reliability of the test itself. CTT assumes that each person has a true score that would be obtained if there were no errors in measurement. Further, this approach to psychome...
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Transitioning from EFA to CFA_Practice
In this practice, we will combine both EFA and CFA on the BigFive Personality dataset (bfi). setwd("D:/Class Materials & Work/Summer 2020 practice/SEM/EFA_to_CFA") #Loading required packages library(parameters) library(tidyverse) library(psych) library(see) library(lavaan) library(performance) library(semPlot) The first step is to test t...
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