Scientific Research Techniques Training with AI

General Information About the Course This course is intended for graduate students. The aim of this course is to equip students with the competence to conduct research on thesis and article literature, establish cause-effect relationships and identify variables, create models, …

Teacher

FutureX

Kategoriler

Artificial Intelligence, Law, Research

Review

Overview

General Information About the Course

This course is intended for graduate students. The aim of this course is to equip students with the competence to conduct research on thesis and article literature, establish cause-effect relationships and identify variables, create models, formulate hypotheses, determine research techniques, develop the main structure of the article, determine sample size, create a scale, collect data, analyze data, report and interpret data, and create a bibliography.

Who should take this course?

Master’s, doctoral, thesis students, academic/administrative staff, R&D and policy analysis teams.

Program Gain

Participants will gain the competence to individually complete the processes, especially the analyses, in their THESIS and article studies.

You can access the study titled “Artificial intelligence (AI) use in data analysis: A comparison of ChatGPT and SmartPLS outputs in PLS-SEM analysis”, which will also be used as a reference in the training, by clicking the button.

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Curriculum

8 Sections
9 Lessons
18 Hours

Expand all sectionsCollapse all sections

Introduction to Research
1
- 1.1
  1. How is scientific research done in social sciences?
Theory
The basic logic of Structural Equation Modeling (SEM) is,
0
Methodology
1
- 3.1
  Model building, variable types and determination of hypotheses
Survey Application
1
- 4.1
  Points to consider in scale adaptation and data collection processes
Preparation for Analysis
1
- 5.1
  Cleaning the collected data, determining the commands (prompts) and performing practical analysis with Artificial Intelligence
Reliability and Validity Analyses
2
- 6.1
  Factor loadings, Cronbach’s Alpha, Composite Reliability, AVE
- 6.2
  Correlation Analyses (Fornell-Larcker, HTMT criterion)
Hypothesis Testing
1
- 7.1
  Testing of hypotheses, path coefficients, t-statistics, p-values, R² values
Conclusion
2
- 8.1
  Formatting SEM results, tables and figures
- 8.2
  Interpretation of SEM results

yapayzekabilimseltürkçee

Free

Course Features

Lectures 9
Quiz 0
Duration 18 Saat
Skill level All levels
Language Türkçe
Students 12
Assessments Yes