Educational statistics with Base R script

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Title: 2-months online training on Educational Data Analysis for policy research based on base R Script. 

Educational policy: 
Educational policy research is a systematic investigation of laws, regulations, and guidelines that govern educational systems. It analyzes how policies affect learning outcomes, equity, resource allocation, teacher effectiveness, and institutional governance. The research may involve qualitative, quantitative, or mixed methods to evaluate policy effectiveness and recommend improvements.

Duration: Two months (two hours per week)
Mode: Online
Focus: Hands-on statistical analysis with real-world educational data

What You Will Learn:

Data handling, visualization, and descriptive statistics
Hypothesis testing including t-tests, Chi-square, and ANOVA
Regression analysis and correlation methods
Hands-on project and structured report writing
Presentation session with certification
Limited Seats Available – Register Now

Syllabus 

Title:  Online training on Educational Data Analysis for policy research based on base R Script. 

Description of the Given Educational Dataset
The dataset provided for this training includes various factors influencing educational performance and governance. It consists of teacher-related variables such as Teacher ID, Current Qualification Level, Training Hours Completed, and Training Needs Score. School-related characteristics include School ID, Region (Urban/Rural), Digital Classrooms Available, and Infrastructure Score. Financial aspects such as Annual Budget, Funds Allocated for Training, and Funds Allocated for Infrastructure are also included. Additionally, the dataset captures stakeholder engagement through Stakeholder ID, Stakeholder Type (Teacher/Admin), Resistance Score, and Concerns Expressed. Finally, governance and pedagogy aspects are represented through Governance Score, Pedagogy Score, and Performance Indicators.  

Objectives 
The objective of this training is to help students analyze this dataset to understand the relationships between teacher training, school infrastructure, financial allocation, and educational performance.  

Training Calendar: 

Training Calendar: Educational Data Analysis with R Script

The first month of the training focuses on learning R scripting with data visualization.
First week 
 In the first week, students will be introduced to the R environment and its basic functionalities, including data types, creating vectors, matrices, and data frames, as well as importing and exporting data. 
Second week 
The second week will cover descriptive statistics, where students will learn how to compute measures such as mean, median, standard deviation, and create frequency tables. They will also practice visualizing data using histograms, bar plots, and boxplots.
Third week 
 The third week will introduce hypothesis testing and correlation, guiding students through manual calculations of t-tests and z-tests, along with computing Pearson’s correlation.
Fourth week 
 In the fourth week, students will explore inferential statistics, including Chi-square tests, ANOVA, and simple regression analysis, all without using external packages.  
Second month

The second month will focus on report writing and data analysis using the psych package. 

Fifth week 
In the fifth week, students will be introduced to the psych package, learning how to compute summary statistics, explore data, and perform reliability analysis. 
Sixth week 
The sixth week will cover advanced data analysis using psych, including factor analysis, visualization of variable relationships, and interpretation of statistical outputs. 
Seventh week 
In the seventh week, students will focus on structuring research reports, interpreting statistical results, and writing research conclusions. 
Eighth week 
The eighth week will focus on student presentations and final evaluations. Students will present their findings based on their analysis of the educational dataset, explaining their methodology, statistical results, and key insights. This session will also include a discussion and feedback segment, where students can address questions and receive guidance on improving their analytical and presentation skills. The training will conclude with a certification ceremony, recognizing the students' efforts and successful completion of the course.
This structured approach ensures that students develop skills in R scripting, data visualization, statistical analysis, and professional report writing, preparing them for real-world data analysis in educational research. 

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